channel assignment in mobile communication

Channel Assignment Strategies in Mobile Communication Explained

Mohammad Jamiu

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What are channel assignment strategies.

Channel assignment strategies in mobile communication are used to allocate available radio channels to mobile users in a way that maximizes spectrum utilization and minimizes interference.

This is important because the radio spectrum is a limited resource, and there is a growing demand for mobile communication services.

Types of Channel Assignment Strategies

There are three main types of channel assignment strategies:

Fixed channel assignment (FCA)

Dynamic channel assignment (dca), hybrid channel assignment (hca).

In fixed channel assignment, each cell is allocated a fixed or predetermined set of channels (voice channels).

If all channels in a cell are occupied, the call from a mobile user is blocked and the user won’t receive service.

This strategy is simple to implement, but it can lead to inefficient spectrum utilization and increased interference if the traffic load is not evenly distributed across the cells.

Advantages of FCA:

• Simple to implement and manage
• Reduces co-channel interference

Disadvantages of FCA:

• Can lead to inefficient spectrum utilization
• Can lead to increased call blocking if traffic load is not evenly distributed across the cells

In dynamic channel assignment, channels are assigned to cells on demand, based on the current traffic load. i.e., there is no allocation of predetermined set of channels (voice channels).

This strategy is more efficient than FCA, but it is also more complex to implement.

Advantages of DCA:

• Improves spectrum utilization
• Reduces likelihood of blocking since all available channels are accessible to all cells

Disadvantages of DCA:

• More complex to implement and manage than FCA
• Can lead to increased call blocking if traffic load is high

HCA is a combination of FCA and DCA. In HCA, each cell is allocated a fixed set of channels, but additional channels can be dynamically assigned to cells if needed.

This strategy offers a good balance between simplicity and efficiency.

Advantages of HCA:

• Improves spectrum utilization compared to FCA
• Simpler to implement and manage than DCA

Disadvantages of HCA:

• Can be more complex to implement than FCA
• Can lead to increased co-channel interference compared to DCA

Channel Borrowing

Channel borrowing is a technique that can be used with any channel assignment strategy.

In channel borrowing, a cell can borrow a channel from a neighboring cell if all of its own channels are occupied.

This process is carried out by the Mobile Switching Center (MSC) which supervises the borrowing procedures and ensures that the borrowing of a channel does not interrupt or interfere with any of the calls in progress in the donor cell.

This technique can help to reduce call blocking and improve spectrum utilization.

Advantages of channel borrowing:

• Reduces call blocking

Disadvantages of channel borrowing:

• Can increase co-channel interference

Comparison of Channel Assignment Strategies (FCA, DCA and HCA)

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Principles of Mobile Communication pp 643–670 Cite as

Channel Assignment Techniques

• Gordon L. Stüber 2  
• First Online: 01 June 2017

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Channel assignment techniques are used extensively in frequency reuse systems to assign time-frequency resources to each user. There are many methods of allocating a channel upon a new call arrival or handoff attempt. A good channel allocation algorithm is the one that yields high spectral efficiency for a specified quality of service (including link quality, probability of new call blocking, and the probability of forced termination) and given degree of computational complexity and decentralization of control. It keeps the planned cell boundaries intact, allocates a channel to a MS quickly, maintains the best service quality for the MS at any instant, and relieves undesired network congestion. This chapter first discusses basic channel assignment techniques, then presents the details of some techniques. These include centralized dynamic channel assignment techniques such as the optimal maximum packing scheme. Afterwards, decentralized and fully decentralized dynamic channel assignment techniques are discussed. Borrowing schemes are discussed as well, where radio resources from neighboring cells can be borrowed to improve spectral efficiency and performance. The chapter goes on to discuss directed retry and moving direction based handoff schemes. The chapter concludes with some examples of dynamic channel assignment schemes for TDMA based cellular systems.

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The reuse factor N is related to the number of buffer rings R as follows. For linear cells N  =  R + 1. For hexagonal planar cells, N  =  i 2 + ij + j 2 , where for R odd i  =  j  = ( R + 1)∕2, and for R even i  =  R ∕2 and j  =  R ∕2 + 1.

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Stüber, G.L. (2017). Channel Assignment Techniques. In: Principles of Mobile Communication. Springer, Cham. https://doi.org/10.1007/978-3-319-55615-4_14

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Channel Assignment Schemes

Contributed by John S. Davis, II , U.C. Berkeley

One main issue in cellular system design reduces to one of economics. Essentially we have a limited resource transmission spectrum, that must be shared by several users. Unlike wired communications which benefits from isolation provided by cables, wireless users within close proximity of one another can cause significant interference to one another. To address this issue, the concept of cellular communications was introduced around in 1968 by researchers at AT&T Bell Labs. The basic concept being that a given geography is divided into polygons called cells.

Each cell is allocated a portion of the total frequency spectrum. As users move into a given cell, they are then permitted to utilize the channel allocated to that cell. The virtue of the cellular system is that different cells can use the same channel given that the cells are separated by a minimum distance according to the system propagation characteristics; otherwise, intercellular or cochannel interference occurs. The minimum distance necessary to reduce cochannel interference is called the reuse distance. The reuse distance is defined as the ratio of the distance, D , between cells that can use the same channel without causing interference and the cell radius, R . Note that R is the distance from the center of a cell to the outermost point of the cell in cases when the cells are not circular.

Channel Allocation

• Fixed Channel Allocation,
• Dynamic Channel Allocation and
• Hybrid Channel Allocation which is a combination of the first two methods.

Fixed Channel Allocation

Dynamic channel allocation.

• First, DCA methods typically have a degree of randomness associated with them and this leads to the fact that frequency reuse is often not maximized unlike the case for FCA systems in which cells using the same channel are separated by the minimum reuse distance.
• Secondly, DCA methods often involve complex algorithms for deciding which available channel is most efficient. These algorithms can be very computationally intensive and may require large computing resources in order to be real-time.

Hybrid Channel Allocation Schemes

The third category of channel allocation methods includes all systems that are hybrids of fixed and dynamic channel allocation systems. Several methods have been presented that fall within this category and in addition, a great deal of comparison has been made with corresponding simulations and analyses [Cox, Elnoubi, Jiang, Katzela, Yue, Zhang]. We will present several of the more developed hybrid methods below.

Channel Borrowing is one of the most straightforward hybrid allocation schemes. Here, channels are assigned to cells just as in fixed allocation schemes. If a cell needs a channel in excess of the channels previously assigned to it, that cell may borrow a channel from one of its neighboring cells given that a channel is available and use of this channel won't violate frequency reuse requirements. Note that since every channel has a predetermined relationship with a specific cell, channel borrowing (without the extensions mentioned below) is often categorized as a subclass of fixed allocation schemes. The major problem with channel borrowing is that when a cell borrows a channel from a neighboring cell, other nearby cells are prohibited from using the borrowed channel because of co-channel interference. This can lead to increased call blocking over time. To reduce this call blocking penalty, algorithms are necessary to ensure that the channels are borrowed from the most available neighboring cells; i.e., the neighboring cells with the most unassigned channels.

• The ratio of fixed to dynamic channels varies with traffic load.
• Nominal channels are ordered such that the first nominal channel of a cell has the highest priority of being applied to a call within the cell.

The last nominal channel is most likely to be borrowed by neighboring channels. Once a channel is borrowed, that channel is locked in the co-channel cells within the reuse distance of the cell in question. To be "locked" means that a channel can not be used or borrowed. Zhang and Yum [Zhang] presented the BDCL scheme as an improvement over the BCO method. From a frequency reuse standpoint, in a BCO system, a channel may be borrowed only if it is free in the neighboring cochannel cells. This criteria is often too strict.

• In Borrowing with Directional Channel Locking, borrowed channels are only locked in nearby cells that are affected by the borrowing. This differs from the BCO scheme in which a borrowed channel is locked in every cell within the reuse distance. The benefit of BDCL is that more channels are available in the presence of borrowing and subsequent call blocking is reduced. A disadvantage of BDCL is that the statement "borrowed channels are only locked in nearby cells that are affected by the borrowing" requires a clear understanding of the term "affected." This may require microscopic analysis of the area in which the cellular system will be located. Ideally, a system can be general enough that detailed analysis of specific propagation measurements is not necessary for implementation.

A natural extension of channel borrowing is to set aside a portion of the channels in a system as dynamic channels with the remaining (nominal) channels being fixed to specified cells. If a cell requires an extra channel, instead of borrowing the channel from a neighboring cell, the channel is borrowed from the common "bank" of dynamic channels. An important consideration in hybrid systems of this type is the ratio of dynamic channels to fixed channels. Analysis by Cox and Reudlink [Cox - 1973] showed that given ten channels per cell, an optimum ratio was 8 fixed channels and 2 dynamic channels. In general, the optimum ratio depends upon the traffic load [Zhang]. In addition to BDCL, a second channel allocation method was presented by Yum and Zhang [Zhang]. Referred to as Locally Optimized Dynamic Assignment Strategy (LODA), this method is best described as a purely dynamic channel allocation procedure as opposed to a hybrid method. In this strategy there are no nominal channels; all channels are dynamic. When a given cell needs to accommodate a call, it chooses from among the bank of available channels according to some cost criteria. The channel with minimum cost is assigned. In a general sense, the cost is a measure of the future blocking probability in the vicinity of the cell given that the candidate channel is assigned. A more detailed description of the cost function will be addressed below.

Dynamic Channel Reassignment

Similar to the goals of dynamic channel assignment is the process of Dynamic Channel Reassignment (DCR). Whereas a DCA scheme allocates a channel to an initial call or handover , a DCR system switches a cell's channel (that is currently being used) to another channel which is closer to the optimum according to frequency reuse or other cost criteria. Thus, for example, a user communicating with channel n may be switched to channel m during the middle of her/his call if channel m is a more efficient use of the available bandwidth from a frequency reuse point of view. Philosophically, DCR is equivalent to DCA.

Simulation and Comparison of Channel Allocation Schemes

A great deal of work is available comparing various realizations of channel allocation schemes [Cox, Elnoubi, Jiang, Katzela, Yue, Zhang]. In comparing performance, typical system metrics include blocking probability of new calls and blocking probability of handover calls. These metrics are written as functions of offered traffic (where the traffic may be written in a variety of forms). It is generally assumed that a blocked new call is preferred over a blocked hand-off call. The idea being that with a blocked hand-off, users are forced to terminate communication in the middle of their session. If this blocking happens at a particularly inopportune time, the results could be disastrous (e.g., business partners cut off in the middle of a vital negotiation). In the case of a blocked new call, at least the business negotiation hasn't started and the involved parties aren't interrupted. Blocking probability is an important metric throughout the field of queueing theory and in the case of M/M/ 1 queues, the Erlang-B formula is often used for analysis of blocking probability. Because blocked calls can be very disconcerting, systems are typically designed to have blocking probabilities of no more than 1% or 2%. This is consistent with the assumption of small offered traffic loads.

Cox and Reudink were the first researchers to present published comparisons of different channel allocation schemes. Their comparison was based on simulation of an outdoor vehicular wireless communication system [Cox - 1971, Cox - 1972, Jakes]. The simulation divided a region into a grid of square cells. The movement of vehicles had a two dimensional normal distribution with 0 mean and 30 mph standard deviation in each of the two orthogonal directions. Poisson arrivals were assumed for the rate of calls per vehicle and call durations were assume to have a truncated normal distribution (truncated on the left at zero) with a "mean" 90 seconds (true mean of 103.5 seconds).

Cox and Reudink's study considered uniform and non-uniform distributions of spatial traffic. In the uniform case, all cells had approximately the same call arrival rate while in the non-uniform case, some cells had a significantly higher call arrival rate. With both the uniform and non-uniform spatial distributions, fixed channel allocation schemes were optimally matched so that the cells with the greatest numbers of calls had the greatest number of channels to deal with those calls. In both cases of uniform and non-uniform traffic, results showed that for low blocking probabilities, dynamic channel allocation schemes could handle more calls than fixed channel allocation schemes. More specifically, in the case of uniform traffic, the DCA approach outperformed the FCA approach when the blocking probability was lower than 10%. At a blocking probability of 1%, the DCA approach could handle over 10% more calls than the FCA approach. In the case of non-uniform traffic, the DCA approach outperformed FCA for blocking rates up to 60%. At a blocking rate of 1%, DCA could handle almost 70% more calls per cell than FCA. Cox and Reudink performed another comparison involving dynamic channel reassignment in [Cox - 1973]. In this hybrid procedure, the total number of available channels is broken into two groups: fixed and dynamic channels. When a cell requires a channel, it first searches for an available fixed channel that is preassigned to the cell. If none of the fixed channels are available, a dynamic channel is searched for from the common bank of dynamic channels. If this search is in vain, the call is blocked. When users who were assigned fixed channels end their calls, these freed fixed channels are then assigned to users in the same cell who are currently using dynamic channels. This frees the dynamic channel for future use and ensures that a large number of channels being used are the optimally-spaced, fixed channels. Results from Cox and Reudink's study of dynamic channel reassignment showed that channel use was increased by over 60% compared to fixed channel allocation for a blocking rate of 1%. This result corresponds to uniform offered traffic.

• Fixed Channel Assignment (FCA),
• Borrowing with Channel Ordering (BCO),
• Borrowing with Directional Channel Locking (BDCL) and
• Locally Optimized Dynamic Assignment (LODA).

With respect to uniform offered traffic, their results showed that BDCL had the lowest blocking probability followed by BCO, LODA and FCA. With non-uniform offered traffic, the relative performance of the four methods was the same with the exception that in this case, LODA performed better than BCO. It makes sense that the ordering for BDCL, BCO and FCA was as found. Indeed, BDCL was specifically designed as an improvement over BCO and BCO was designed as an improvement over FCA [Zhang, Elnoubi]. The fact that the performance of LODA varies under uniform versus non-uniform traffic is rather interesting however. The reason behind this phenomenon is that LODA provides optimal channel allocation only in local regions. Given non-uniform traffic which consists of dense regions in certain local areas, LODA will accommodate these regions of high traffic offering. However, in a global sense, the LODA algorithm will not necessarily provide the optimal allocation. With uniform offered traffic, LODA does not have any regions with peak traffic to optimize; i.e., no local regions within which the benefits of LODA can be realized. Furthermore, with respect to the entire region, the optimization is generally not optimal in a global sense. The result is that with uniform traffic, LODA does not have any advantage to offer over BCO. From the previous discussion we see that one general result of all of the comparisons is that dynamic channel allocation outperforms fixed channel allocation for low blocking rates (below 10% in most cases). Blocking rates above 1% or 2% are generally not tolerated. This is generally an accepted guideline throughout the telecommunications industry and we will adhere to this design constraint as well.

Common Principles of Channel Allocation Schemes

• Channel allocation schemes must not violate minimum frequency reuse conditions.
• Channel allocation schemes should adapt to changing traffic conditions.
• Channel allocation schemes should approach (from above) the minimum frequency reuse constraints so as to efficiently utilize available transmission resources.

As the first requirement suggests, all channel allocation schemes adhere to condition 1. From a frequency reuse standpoint, a fixed channel allocation system distributes frequency (or other transmission) resources to the cells in an optimum manner; i.e., common channels are separated by the minimum frequency reuse distance. Thus, a fixed channel allocation scheme perfectly satisfies condition 3 as well. However, a fixed allocation scheme does not satisfy condition 2.

Philosophically, any dynamic channel allocation scheme will meet the requirements of all of the above three conditions to some degree. At the system architecture level dynamic channel allocation schemes may differ widely, but fundamentally, their only difference is in the degree to which they satisfy condition 3. Different DCA schemes attempt to satisfy condition 3 (in addition to conditions 1 and 2) by approaching the minimum frequency reuse constraint arbitrarily closely, and by doing so in as short a time period as possible. The above three conditions point to the fact that design of dynamic channel allocation schemes falls within the general class of optimization problems. Furthermore, since we can always assume that the available number of base stations is finite and the transmission resources will always be countable (due to FCC requirements if nothing else) then our problem can be reduced to the subclass of combinatorial optimization problems. As with all combinatorial optimization problems, there will exist a solution space and a cost function [Aarts & Korst]. A typical element of the solution space could be a particular layout of frequency channels among the base-stations. The cost function can be loosely characterized as the difference between the frequency reuse of an arbitrary solution and the frequency reuse of the optimized solution. The error associated with a non-optimized cost is realized as a future increased blocking probability or an otherwise unwarranted lack of channel availability. It is typically assumed that the solution to the wireless dynamic channel allocation problem is NP-complete [Yue, Cox - 1971]. The definition of np-completeness follows from the conjecture made in the late 1960's that there exists a class of combinatorial optimization problems of such inherent complexity that any algorithm, solving each instance of such a problem to optimality, requires a computational effort that grows superpolynomially with the size of the problem. In the case of dynamic channel allocation, the complexity is generally attributed to the required inclusion of cochannel interference in any analysis of dynamic channel allocation schemes [Yue]. The author is aware of one published article to date offering an analytical method (approximate) for calculating the performance of dynamic channel allocation [see Yue]. Recently, several approximation techniques have been proposed as methods for solving condition 3 of the dynamic channel allocation problem. In particular there has been interest in applying simulated annealing techniques [Duque-Anton] and neural network methods [Chan, Kunz, Funabiki] to dynamic channel allocation.

Channel and Base-Station Allocation Schemes

• [1] Chan, P. T. H., Palaniswami, M. & Everitt, D., "Neural Network-Based Dynamic Channel Assignment for Cellular Mobile Communication Systems," IEEE Transactions on Vehicular Technology, vol. 43, no. 2, May 1994.
• [2] Cox, D.C. and Reudink, D.O., "Dynamic Channel Assignment in High-Capacity Mobile Communications Systems," Bell System Technical Journal, vol. 50, no. 6, July-August 1971.
• [3] Cox, D.C. and Reudink, D.O., "Dynamic Channel Assignment in Two-Dimensional Large -Scale Mobile Radio Systems," Bell System Technical Journal, vol. 51, no. 7, September 1972.
• [4] Cox, D.C. and Reudink, D.O., "Increasing Channel Occupancy in Large-Scale Mobile Radio Systems: Dynamic Channel Reassignment," IEEE Transactions on Communications, vol. 21, no. 11, November 1973.
• [5] Duque-Anton, M., Kunz, D., & Ruber, B., "Channel Assignment for Cellular Radio Using Simulated Annealing," IEEE Transactions on Vehicular Technology, vol. 42, no. 1, February 1993.
• [6] Elnoubi, S. M., Singh, R., and Gupta, S.C., "A New Frequency Channel Assignment Algorithm in High Capacity Mobile Communication Systems," IEEE Transactions on Vehicular Technology, vol. 31, no. 3, August 1982.
• [7] Funabiki N. & Takefuji, Y., "A Neural Network Parallel Algorithm for Channel Assignment Problems in Cellular Radio Networks," IEEE Transactions on Vehicular Technology, vol. 41, no. 4, November 1992.
• [8] Gamst, A., "Homogeneous Distribution of Frequencies in a Regular Hexagonal Cell System," IEEE Transactions on Vehicular Technology, vol. 31, no. 3, August 1982.
• [9] Jakes, W. C., Micr wave Mobile Communication ns, IEEE Press: New Jersey, c. 1993.
• [10] Jiang, H. and Rappaport, S. S., "CBWL: A New Channel Assignment and Sharihng Method for Cellular Communication Systems," IEEE Transactions on Vehicular Technology, vol. 43., no. 2, May 1994.
• [11] Katzela, I., "Channel Assignment Schemes for Cellular Mobile Telecommunication Systems," Unpublished work, Columbia University, December 15, 1992.
• [12] Kunz, D., "Channel Assignment for Cellular Radio Using Neural Networks," IEEE Transactions on Vehicular Technology, vol. 40, no. 1, February 1991.
• [13] Yue, W., "Analytical Methods to Calculate the Performance of a Cellular Mobile Radio Communication with Hybrid Channel Assignment," IEEE Transactions on Vehicular Technology, vol. 40, now. 2, May 1991.
• [14] Zhang, M. and Yum, TS. P., "Comparisons of Channel-Assignment Strategies in Cellular Mobile Telephone Systems", IEEE Transactions on Vehicular Technology, vol. 38, no. 4, November 1989.

Combinatorial Optimization

• [15] Aarts, E. and Korst, J., Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing, John Wiley & Sons: Chichester, c. 1989.
• [16] Garey, M. R. & Johnson, D. S., Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Company: New York, c. 1979.

Try you luck as a frequency assigner in the Dynamic Channel Allocation game .

JPL's Wireless Communication Reference Website � John S. Davis, II and 1993, 1995.

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Evaluation of a channel assignment scheme in mobile network systems

• Nahla Nurelmadina 1 ,
• Ibtehal Nafea 1 &
• Muhammad Younas 2  

Human-centric Computing and Information Sciences volume  6 , Article number:  21 ( 2016 ) Cite this article

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The channel assignment problem is a complex problem which requires that under certain constraints a minimum number of channels have to be assigned to mobile calls in the wireless mobile system. In this paper, we propose a new scheme, which is based on double band frequency and channel borrowing strategy. The proposed scheme takes into account factors such as limited bandwidth of wireless networks and the capacity of underlying servers involved in processing mobile calls. It aims to ensure end-to-end performance by considering the characteristics of mobile devices. This is achieved by determining the position of users (or mobile stations) in wireless mobile systems. The proposed scheme is simulated in order to investigate its efficiency within a specific area of a large city in Saudi Arabia. Experimental results demonstrate that the proposed scheme significantly improves the performance of mobile calls as well as reduces the blocking when the number of mobile call increases.

Mobile devices and particularly mobile phones have been used for a variety of purposes ranging from voice calls through to sending SMS/emails to online banking and shopping. Mobile phones generally use cellular network system as one of the main communication network. The rate of increase in the popularity of mobile phone usage has far outpaced the availability of usable frequencies which are necessary for the communication between mobile users and the base stations of cellular networks. This constitutes an important bottleneck in the provided capacity of mobile cellular systems. Careful design of a network is necessary to ensure efficient use of limited frequency resources. One of the most important issues in the design of a cellular radio network is to determine a spectrum-efficient and conflict-free allocation of channels among the cells while satisfying both the traffic demand and the electromagnetic compatibility (EMC) constraints [ 1 ]. This is usually referred to as channel assignment or frequency assignment. The problem of channel assignment becomes increasingly important, i.e., how do we assign calls to available channels so as to improve performance and to minimize interference. This paper proposes a new scheme for channel assignment, which is called Double Band Frequency Channel Borrowing (DBFCB) scheme. The objective of the proposed scheme is to optimize channel utilization, improve performance and to reduce the blocking probability of calls in a wireless mobile network system.

The proposed scheme is systematically developed and validated through various simulation experiments. It has been applied to the central area of a large city, Madina Monwara, in the Kingdom of Saudi Arabia, using two bands (900, 1800 MHz). Channel borrowing techniques were simulated to investigate the efficiency of this scheme and to make sure that the scheme is viable. The theoretical analysis of the tele traffic was validated through MATLAB simulation analysis [ 2 ]. The simulation model is based on the number of users within a specific area which has BTSs. This is based on data collected from the famous local mobile operator, Zain Telecom Company‎.

The main contributions of the proposed scheme are to reduce the call blocking and call dropping probabilities. Such probabilities generally increase with the increase in the number of mobile users. Thus reduction in call blocking/dropping will enable improved service provisioning in mobile wireless network. In addition, DBFCB algorithm improves response time by using both benchmark and heavy traffic demands with the same known constraints.

The remainder of the paper is structured as follows. ‘‘ Mobile network architecture and communication ’’ section describes an architecture of a mobile network and a mobile communications process. ‘‘ Related work ’’ section reviews and analyses related work. ‘‘ The proposed scheme ’’ section presents the proposed scheme. ‘‘ Modelling of the proposed scheme ’’ section describes modelling of the proposed scheme. ‘‘ Experimental results ’’ section describes the experimental results and analysis. ‘‘ Conclusion ’’ section presents the conclusion.

Mobile network architecture and communication

This section describes the fundamental principles and concepts of wireless mobile network systems. It first presents a generalized architecture of mobile networks and describes its main components. It then describes mobile communications process.

Mobile network architecture

Figure  1 shows an mobile network architecture. The process of call handling in mobile network is carried out in different steps. First, a mobile device (making call) establishes a connection with the access point which is the base station. If the connection is successful the base station responds to the call of mobile device. Radio frequency connection establishment is triggered by sending a channel request message. This message requests the Base station system (BSS) for allocation of radio resources for radio connection setup. The mobile device then waits for an assignment of the access channel. At this point the mobile device is listening to the access channel for a reply. The BSS allocates a channel to the mobile device. This channel allocation assigns a frequency and a timeslot on that frequency. After the mobile device receives this message, it will only use the specified resources for communication within the mobile network.

The main components, of Fig.  1 , involved in mobile call handling, are explained as follows.

Mobile switching center (MSC) It provides call control and telephony switching services between telephone and data systems, and it also provides access to the fixed Public Switched Telephone Network. The MSC manages handoff and switching processes between cells. It communicates with each relevant BS (Base Station) in order to drop the call from the old BS and to set up a new one in the new BS (as a part of the handoff process). MSCs also orchestrate the process of creating new voice calls. An MS initiates a call by using a reverse control channel to make a request. The MSC has then to grant the request, after which a pair of voice channels is assigned to the call. The MSC includes one database for storing location information and call details of a mobile terminal. The MSC is also connected to a second database in which information about a subscriber registered in its mobile communication service is stored. The base stations route the communications to the MSC via a serving BSC. The MSC routes the communications to another subscribing wireless unit via a BSC/base station path or via the PSTN/Internet/other network to terminating destination. Between MSCs, circuit connections provide the handover mechanism that services calls as users roam from one service zone to another.

Home location register (HLR) It is a central master database within the GSM network, which maintains a permanent store of subscribers’ information, and location information for the mobile network. The HLR provides information on the services (subscribed) to the network users. It is also an important source of data to support the roaming process which enables incoming calls that are to be routed to the location of the subscriber.

AC or AUC This is the Authentication Center which contains a secured database handling authentication and encryption keys. It is also a key component of the HLR. It validates the mobile SIM (Security Information Management) card which attempts to connect to a mobile network. It verifies a mobile device by sending a randomly generated number to the mobile device. The mobile device then performs a calculation against it with a number it has stored and sends the result back. If the switch gets the number it expects then the call proceeds. The AC stores all data needed to authenticate a call and to encrypt both voice traffic and signaling messages [ 3 ].

Base station system (BSS) All radio-related functions are performed in the BSS, which consists of base station controllers (BSCs) and the base transceiver stations (BTSs) [ 3 ].

BSC It provides all the control functions and physical links between the MSC and BTS. It is a high-capacity switch that provides functions such as handover, cell configuration data, and control of radio frequency (RF) power levels in base transceiver stations. A number of BSCs are served by an MSC.

BTS It handles the radio interface to the mobile station. The BTS is the radio equipment (transceivers and antennas) needed to service each cell in the network. A group of BTSs are controlled by a BSC.

Mobile communication

Each mobile device uses a separate, temporary radio channel in order to communicate with the cell site. The cell site talks to many mobile devices at once, using one channel per mobile device. Channels use a pair of frequencies for communication (see Fig.  2 )—one frequency (the forward link) for transmission from the cell site and one frequency (the reverse link) for the cell site to receive calls from the mobile device. Mobile devices must stay near the base station to maintain communications. The basic structure of mobile networks includes telephone systems and radio services. Mobile radio service operates in a closed network and has no access to the telephone system. But mobile telephone service allows interconnection with the telephone network.

Mobile communication system

Related work

Various techniques and models have been developed in order to improve the performance of mobile calls and related services in mobile wireless networks. Different factors contribute to the performance aspects such as network traffic, bandwidth, computing devices, and the wireless signals between the mobile devices and nearby base stations of cellular radio networks.

Various channel assignment schemes have been widely investigated with a goal to maximize the frequency reuse. The channel assignment schemes in general can be classified into three strategies: fixed channel assignment (FCA), dynamic channel assignment (DCA) and the hybrid channel assignment (HCA) [ 4 , 5 ]. In FCA, a set of channels are permanently allocated to each cell based on pre-estimated traffic intensity. In this case, the co-channel interference (Transmission on same frequency), adjacent channel interference (Transmission on close frequencies), and the co-site channel interference lead to the main problem, i.e., it does not adapt to changing traffic conditions and user distribution. Moreover, the frequency planning becomes more difficult in a microcellular environment as it is based on the accurate knowledge of traffic and interference conditions. The main problem of FCA is the poor channel utilization wherein some users are unable to find any channel to use.

In DCA, there is no permanent allocation of channels to cells. Rather, the entire set of available channels is accessible to all the cells, and the channels are assigned on a call-by-call basis in a dynamic manner. This means that base station chooses frequencies depending on the frequencies already used in neighboring cells. But the issue with the DCA is to handle more traffic in a particular cell [ 6 , 7 ].

Kyasanur et al. [ 8 ] propose to improve the capacity of multi-channel wireless networks. This work exploits multiple interfaces but with the constraint that the number of available channels is greater than the number of available interfaces. It also proposes a strategy that maintains the autonomy of IEEE 802.11 such that it is not required to be modified.

Rajagopalan et al. [ 9 ] take into account quality of service parameters such as residual bandwidth, number of subscribers, duration of calls, frequency of calls and their priority. This work is based on the optimization of dynamic channel allocation using genetic algorithm (GA). It attempts to allocate channels to users such that overall congestion in the network is minimized by reusing already allocated frequencies. This work utilizes GA in order to ensure optimization. The optimized channels are then compared with non-optimized channels in order to check the efficiency of the proposed algorithm.

Shindeet al. [ 10 ] propose a multi-channel allocation model. It uses an evolutionary strategy with an allocation distance in order to enable efficient use of frequency spectrum. The problem of determining an optimal allocation of channels to mobile users that minimizes call blocking and call dropping probabilities is also emphasized in this work.

In order to ensure efficient and smooth service provisioning in the presence of network congestion, link failures, and mobile service station failures, Boukerche et al. [ 11 ] propose that the cellular network be divided into hexagonal cells as shown in Fig.  3 . This approach divides the cells into five groups of varying sizes. The request for a channel can be granted if the requesting cell receives the reply from all members of a group. However, this algorithm may not work properly if the replies received by the requesting cell do not satisfy the above mentioned criteria. The algorithm is successful in the scenarios when the area of coverage is divided into hexagonal cells and the reuse distance is fixed for all cells.

Frequency reuse (channel allocation)

The proposed scheme

The assignment of channels to cells or mobile devices is one of the fundamental resource management issues in a mobile communication system as it involves different cellular components, handover scenarios, and the complex roles of the base station (BS) and the mobile switching center (MSC). In order to appropriately plan a mobile cellular radio network it is necessary to allocate channels to base stations (BS) so as to ensure that the network can carry sufficient traffic while avoiding interference problem [ 12 ].

In a mobile communication system the total number of channels made available (free) to a system depends on the allocated spectrum and the bandwidth of each channel. However, in the current mobile communication system, the available frequency spectrum is limited and the number of mobile users is increasing. Hence the channels must be reused as much as possible in order to increase the system capacity. Thus it is important to allocate channels to cells or mobile devices in such a way so as to minimize the dropping probability of incoming and outgoing calls and the probability that the carrier-to interference ratio of any call falls below a pre-specified value; i.e. the blocking probability which is one of the most important quality of service (QoS) parameters in the channel assignment schemes.

The overall objective is to serve the maximal number of network users over limited transmission resources. The transmission resource is an available radio spectrum which consists of a limited number of frequencies or (channels). Channel assignment problem involves assigning frequencies to each radio cell in such a way that a set of constraints is satisfied [ 13 ]. These include the limited number of available frequencies in the radio spectrum as well as the traffic constraints corresponding to the minimum number of frequencies indispensable for covering communication between mobile devices moving in a particular cell. In addition, the electromagnetic compatibility constraints (EMC) may happen between channels in the same cell (co-site channel constraint), interference between neighboring cells (adjacent channel constraint) and interference between other cells utilizing the same channel (co-channel constraint) [ 14 ].

This paper proposes a new scheme (or algorithm) in order to optimize the frequency assignment and to enable the reuse of same frequency by sufficiently distant cell. This is to maximize the number of communication (calls) but with a limited number of frequencies. The proposed scheme is called dual band frequency channel borrowing (DBFCB). In Simple Borrowing, channel assignments are borrowed from the adjacent cells and are returned to that cell after it has become free. When a new call initiates and reaches to a cell, and if currently, all the permanent channels allocated to the cell are busy, then channels are borrowed from adjacent cell provided the channels are available (in adjacent cell) and minimum reusable distance constraint is met. In Channel Borrowing algorithms, a database is maintained for the record of channels as per their status either currently in use, borrowed or free. Mobile switch center (MSC), taking care of the channel borrowing activities, runs the channel borrowing procedure, so that channels available are borrowed from the cell having relatively more free channels. Channel borrowing is done under minimum reusable distance constraint. The performance may be reduced for ongoing connections, due to increase of overheads in the base stations of the cellular Mobile system [ 15 ].

The main steps of the working mechanism of the DBFCB scheme are illustrated as follows. These steps are diagrammatically shown in Fig.  4 .

Flow chart of the dual band frequency channel borrowing (DBFCB) scheme

When a mobile user wants to communicate with another user or a base station, it must first obtain a channel from one of the base stations that hears it. That is, when a user (mobile device) wants to starts a call, the base station (BS) is identified [ 16 ]. BS is then made aware about user’s location.

Based on the location, users close to the BS get higher priority compare to users who are away from the BS.

When a call request occurs within a cell, the channel allocation (with frequency 900 MHz) of this cell are tested.

The channels are tested in an order starting from the first channel of the list. This is to look for the availability of a free channel.

If a free channel is found, it is assigned to the call associated with the user (mobile device).

If no free channel can be found and all the channels are busy then a channel allocation (with frequency 1800 MHz) is borrowed from the adjacent cell. The adjacent cell is required to have the largest number of channels available for borrowing.

If all channels in the adjacent cell are busy then it borrows channels from the next cell (with frequency 1800 MHz), if available.

Modelling of the proposed scheme

This section explains the main elements which are involved in order to model the proposed scheme. Based on these the proposed scheme is then tested and evaluated through simulation experiments [ 17 ].

Modelling of the geographical area

In order to test the proposed scheme we model the (simulated) geographical area with respect to a real geographical area of one of the major cities in Saudi Arabia, called Madina Monwara. This city attracts a large number visitors and thus providing a good venue for testing the proposed scheme. It represents the user mobility and traffic behavior within a certain area such as the Haram Area in the city, as shown in Fig.  5 . For the proposed scheme, this area represents one cluster (as in related studies of modelling city areas [ 18 ]). In line with the related studies, the area under consideration (as in Fig.  5 ) exhibits specific characteristics such as population distribution, and distribution of MAPs (Movement Attraction Point).

Geographical area in the city of Madina Monwara, Saudi Arabia

Population distribution Population of people in a geographical area can be grouped into different classes including: visitors, cars, and local working people. The classification of groups is based on the mobility behavior of a population. However, in the proposed scheme, we consider a representative sample of people which are mobile users (making mobile calls). This is because mobile communication systems focus merely on the mobility behavior of mobile users.

Movement attraction points (MAP) MAPs represent locations that attract the population movements and at which people spend considerable time. Examples are work places, residences, shopping centers, etc. Each MAP characterizes the people group type it attracts. The proposed scheme considers the MAP (shown in Fig.  5 ) which is the main attraction for visitors in the city of Madina Monwara. Other types of MAPs include residences, work places, shopping centers, etc.

Traffic modelling

We consider the arrival of both incoming and outgoing calls. The call arrival rate refers to the total number of incoming and outgoing calls during busy hour conditions. The call arrival process follows Poisson distribution. For high mobility users, the rate of incoming calls is assumed to be higher than the corresponding outgoing calls.

Consider the scenario in wireless mobile network consisting of two cells in a series. New calls arrive in the first cell with Poisson rate and are served for a time interval that is negative exponentially distributed with mean calls carried in the first cell (block call). After completion of service, calls are offered to the second cell with a fixed handoff probability. These calls are serviced in the second cell for time intervals that are negative exponentially distributed with mean. For simplicity, we assume that cell receives no new calls and also generates no block calls to be given to the first cell. The blocking experienced by the new calls of mobile network in the first cell is given by the Erlang. The traffic load, in Erlang, is the product of the call arrival time and the call duration [ 19 ]. The call arrival time represents the cumulative sum of calls inter-arrival time, which follows a Poisson distribution with an average time (λ). Note that we characterize the joint probability distribution of the number of calls in the cells in such a way that we take into account that the users perform random motions. The inter-arrival time define the time period between two consecutive calls.

During the first part of simulation, λ was kept constant in order to investigate the performance at a certain time period with a fixed traffic load. In the second part, the traffic load varied with the simulation time, thus the performance was according to the traffic load. The call duration is chosen as a negative exponentially distributed because for all calls the arrival time and call duration are treated as independent random variables.

Experimental results

The proposed scheme is simulated using the MATLAB software [ 20 ]. The simulation model is divided into three parts. The first part deals with simulation parameters, such as the size of simulation area. The second part deals with the traffic generation parameters, such as inter-arrival time, call arrival time, call duration time and random variable generation (e.g., mobile location in the simulation window). The third part deals with the channel assignment mechanism.

Simulation model

The simulation model consists of a fixed window with four-overlapped cells. Each cell consists of two bands frequency, 900 MHz and 1800 MHz. The simulation area is equal to 4 Km 2 . Every cell covers 1 Km 2 ; assume that the cell type used can cover up to 1 Km 2 , macrocell. As shown in Fig.  6 , the simulation area is divided into four cells, each associated with one BTS (Base Transceiver Station). The coordinates for each BTS are as follows.

Distribution of mobile users in the simulation area

BTS(1) in X-pos starts from 0 to 1000 m and in y-pos from 0 to 1000 m.

BTS(2) in X-pos starts from 0 to 1000 m and in y-pos starts from 1000 m to 2000 m.

BTS(3) in X-pos starts from 1000 m to 2000 m and in y-pos starts from 1000 m to 2000 m.

BTS(4) in X-pos starts from 1000 to 2000 m and in y-pos starts from 0 to 1000 m.

The main parameters considered in the simulation are number of cells, number of channels, population size and the maximum number of iterations.

Simulation results

The proposed algorithm was investigated using four different cases. Each simulation was run ten times in order to obtain an average value in each case.

Case 1 The number of mobile users in the simulation area is 5000 and the channels are 72 in each cell in the entire BTS. The algorithm was investigated under extremely high traffic intensity. The average holding time call was adjusted to 60 s and the average arrival time was adjusted to 1 s. The simulation results are shown in Fig.  7 . The blocking call values show that all channels were consumed; i.e., value of channel availability is zero because all channels are busy and there is no free channel at BST. The negative value of channel availability means that new calls have no free channels. The positive value of channel availability means that free channels are available.

Channel consumption results at the end of simulation time in case 1

The results show, that in the case of frequency 900 band (1 to 4 BTS) all channels were consumed in 4 Base station that means no free channel (all channels locations were busy in cell) use the other frequency 1800 band.

In the case of frequency 1800 band (5 to 8 BTS), the following observations were made:

BTS 1 consumed all channels and 25 new calls were blocked no channel free available;

BTS 2 consumed all channels and 23 new calls were blocked no channel free available;

BTS 4 consumed all channels and 7 new calls were blocked no channel free available;

BTS 3 consumed 66 channels and 6 new channels were available channel free available.

Case 2 In this case, the algorithm was investigated under high traffic intensity. The average holding time was adjusted to 180 s and the average arrival time was adjusted to 1 s. Figure  8 shows the simulation results:

Channel consumption result at the end of simulation time in case 2

The results show that in frequency 900 band, all channels were consumed and no channels were free in the band 900 of all 4BTS. In using the other frequency 1800 band, the following observations were made:

BTS 1 consumed all channels and 4 calls were blocked. All channel in this base station are busy and thus 4 new calls were blocked;

BTS 2 consumed all channels and no free channel was available. All channels were busy in the cell and thus 6 new calls were blocked;

BTS 4 consumed all channels. 23 calls were blocked as there was no free channel available;

BTS 3 consumed 59 channels. 7 channels were available channel so calls are not blocked.

Case 3 In this case, the average holding time was adjusted to 180 s and the average arrival time was adjusted to 30 s. The simulation results are shown in Fig.  9 .

Channel consumption result at the end of simulation time in case 3

The results indicate that in frequency 900 band:

BTS 1 consumed 4 channels and 68 channels are available. Thus no call was blocked;

BTS 2 consumed 3 channels and 69 channels are available. Thus no call was blocked;

BTS 3 consumed 4 channels and 68 channels are available. Thus no call was blocked;

BTS 4 consumed 6 channels and 65 channels are available. Thus no call was blocked.

On the other hand, in frequency 1800 band, there were no channels consumed. All channels were available.

The algorithm was investigated under medium traffic intensity. Total channels in each cell were 72.

Case 4 The average holding time was adjusted to 180 s and the average arrival time was adjusted to 120 s. Figure  10 shows the simulation results.

Channel consumption result at the end of simulation time in case 4

According to the results gathered with frequency 900 band, the following observations were made:

BTS-1 consumed 1 busy channel and 71 channels were available, and no call attempted was blocked;

BTS-2 consumed 1 busy channel and 71 channels were available, and no call attempted was blocked;

BTS-3 consumed 2 busy channels and 70 channels were available, and no call attempted was blocked;

BTS-4 consumed no channel and all channel were available, and no call attempted was blocked;

But in the case of 1800 band there were no channels consumed. All channels were available and no call attempted was blocked. The algorithm in case 4 had lower call blocking as compared to the other cases. This shows the improvement of the proposed scheme in reducing the call blocking.

In mobile network systems, assigning a channel to a call in a cell in order to achieve high spectral efficiency is crucial to maintaining call quality and reducing call blocking. This paper proposed a new scheme in order to improve channel assignment problem in the mobile network systems. The proposed scheme takes into account double band frequency channel borrowing. It shows greater response to both benchmark and heavy traffic demands and it enhances network performance with optimum load on the network. The algorithm was evaluated using MATLAB that simulated the network and user distribution behavior in a specific (and busy) area of the city of Madina Monwara in Saudi Arabia. Various experiments were conducted. The results showed that the proposed scheme has the capability of reducing the probability of call blocking and call dropping. It also optimizes channel utilization in mobile network systems. The results also show the effectiveness of the algorithm in borrowing and assigning channels in a high traffic intensity and crowded area. Overall the proposed algorithm reduces the blocking rates of calls and improves the response time even under heavy traffic conditions.

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Authors’ contributions

NN and IN carried out related studies and analysis of the literature. NN, IN and MY participated in the design and development of the proposed scheme which is based on double band frequency and channel borrowing strategy. NN and IN collected simulation data and carried out experiments. MY participated in its design and coordination and helped to draft the manuscript. All authors have read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

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College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

Nahla Nurelmadina & Ibtehal Nafea

Department of Computing and Communication Technologies, Oxford Brookes University, Oxford, OX33 1HX, UK

Muhammad Younas

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Correspondence to Muhammad Younas .

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Nurelmadina, N., Nafea, I. & Younas, M. Evaluation of a channel assignment scheme in mobile network systems. Hum. Cent. Comput. Inf. Sci. 6 , 21 (2016). https://doi.org/10.1186/s13673-016-0075-0

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Received : 02 July 2016

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Published : 10 November 2016

DOI : https://doi.org/10.1186/s13673-016-0075-0

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Channel Assignment Strategies

What are channel allocation strategies.

Channel Allocation is a method of assigning the existing channels to the various cells within the cellular system. Channel assignment is mainly done to manage the traffic demands of the cell.

Concept of Channel Assignment

We are aware of the basic idea involved in mobile communication that a radio signal path is maintained between the base station and the mobile station so as to maintain communication. We know that there are various multiple access technique like FDMA, TDMA, and CDMA.

In Frequency Division Multiple Access, the various users of the cell are allotted different frequency bands to establish communication. While in Time Division Multiple Access, the communication is maintained over the same frequency band at different time slots. As against, in Code Division Multiple Access a well-defined pattern code is imposed on the actual signal and then transmission over the channel takes place.

Basically, mobile communication is known to be a wireless network that allows users to connect while roaming.

Need for Channel Assignment

We are aware of the fact that there are number of radio channels in a cell and when a call is made then a specific radio channel is assigned to ensure communication. However, when call attempts in a cell increases more than the assigned radio channels, then the extra call requests will remain unassigned, and hence the call will get dropped. When a higher number of calls remain unconnected within the cell then this shows poor quality service of the provider.

There is a term called ‘blocking probability’ which is defined as the overall dropped calls as a percentage of overall call requests within the cell.

Generally, service providers ensure that the blocking probability must be less than 3% of each cell and this depends on the number of channels assigned to each cell.

The figure shown above clearly represents that the number of channels assigned is 3 but the number of requested calls is 5. So, here number of calls is more than the overall available channels and so there are 2 dropped calls.

Now, let us have a look at the figure shown below:

Here in this scenario, the number of assigned channels to the cell are more in comparison to overall call requests generated and thus here some of the channels remain unused. This turns out to be a waste of the available channel resources.

Hence channel assignment is necessarily done to ensure good usage efficiency within the channel.

Considerations

There are few considerations regarding the channel assignment which are as follows:

• The antenna at the base station must be accessible but all devices of the channel hence spacing of the channels must be proper.
• The traffic demand of the cell must be fulfilled by all channels of that cell.
• Cochannel interference must be least as much as possible.

It is to be noted here that in earlier days practically economical approach was that a single antenna must be used for both transmitter and receiver. However, with the advent of technology now it is not necessary to use a common antenna as we can use power-efficient common amplifiers.

Hence, to achieve stable channel assignment point second and third must be more severely considered than the first one.

Methods of Channel Assignment

Channel Assignment is basically done to avoid co-channel and adjacent channel interference in the cellular system as much as possible.

There are mainly two methods of assigning channels to each cell which are fixed channel assignment and dynamic channel assignment. Let us now proceed to understand each method separately.

Fixed Channel Assignment

It is abbreviated as FCA. In this channel assignment technique, each mobile subscriber within the cell is assigned its own frequency channel to ensure communication. Here basically what follows is assigning the voice channels for a long period of time. The channel numbering is done in increasing order manner according to frequency. No matter what is the overall number of channels in the channel set, the highest and the lowest channel sets are frequency adjacent to each other.

Basically, in the fixed channel assignment technique, the channels to the cell are assigned statically. This means that whatever the traffic demand is the assigned channels will not vary. Hence this method is suitable for uniform traffic load conditions within the cell.

The figure below represents fixed channel assignment allocation:

One can determine the number of channels for a specific cell by the formulation given below:

: N corresponds to the number of overall channels needed to cover a specific coverage area

D is the frequency reuse distance and

R denotes the radius of the cell

This channel assignment offers maximal frequency reuse which is known to be its crucial advantageous factor. However, at the same time, major disadvantageous factor is that channel bandwidth gets wasted and congestion may be a problem when the cell traffic is non-uniform.

Therefore, if we want fixed channel assignment then it is necessary that the expected load in each cell must be estimated along with that the required number of channels must be calculated.

Dynamic Channel Assignment

In this channel assignment technique, the allocated channels to a cell is not static and thus varies depending on the requirement. Here the channel assignment is time-varying in nature and changes according to the traffic demands in the cell. It is abbreviated as DCA and sometimes called the dynamic method.

This method shows suitability in cases when the traffic demand is non-uniform. There is a resource pool that provides the channel to the cells according to the requirement of each cell.

The figure shown above clearly indicates that the channels a, b and c are used by various cells but according to the requirement as we have not preassigned the channels to the cells.

This method is known to be a comparatively more efficient one than FCA because it offers flexibility in changing the demands of the system. Day by day, with the increasing demand of mobile phone users, this approach fulfills today’s needs.

If we talk about the advantage offered by this method then it is known that the channel usage efficiency is quite high. But at the same time the disadvantageous factor is that here randomization exists thus frequency reuse is not maximized. Also, as the process involves real time computation thus, is time-consuming and complex.

It is to be noted here that in this method, once the cell demand gets fulfilled by the channel then the channel gets returned to the pool for further reusability.

The pool from where channels are demanded can be of centralized or distributed.

This is all about channel assignment strategies.

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Co-Channel and Adjacent Channel Interference in Mobile Computing

Co-channel interference is not actually an interference but more a sort of congestion when more than one device is operating on the same frequency channel. It hinders performance by increasing the wait time as the same channel is used by different devices. Sources of interference are as follows: 

• Another mobile in the same cell.
• Ongoing call in neighbor’s cell
• When a different Base Station operates on the same frequency.

Types of channels on which interference is present are- 

• On Voice Channel: leads to crosstalk which is an interference or background noise while we are talking to another person on mobile. Crosstalk is unwanted interference that should be minimized. 
• On Control Channel:  It can lead to problems in creating a connection between the sender and receiver during a call which leads to missed calls. Calls may terminate abruptly known as blocked calls. 

Interference causes the above two problems because it reduces the channel capacity and thus, affects the performance.  

Types of Interference in Mobile Communication

• Co-Channel Interference 
• Adjacent Cell Interference

Co-Channel Interference

Co-channel cells are those cells that use the same frequency in a given coverage area. Interference from these cells is called co-channel interference. In co-channel interference, the cells are clustered as close together as possible to reduce the co-channel interface and provide sufficient isolation. Increasing the co-channel reuse ratio improves the transmission quality because of the smaller level of co-channel interference. An example of co-channel interference is when a radio transmitter is operating on the same frequency. 

The reasons behind Co-channel interference are: 

• Bad weather condition 
• Poor frequency planning 

Ways we can reduce co-channel interference in cellular communication are: 

• Proper planning and implementation.
• The frequency reuse technique increases overall system capacity.

Adjacent Channel Interference

It is the interference caused to the signal which is adjacent in frequency to the desired signal. Imperfect receiver side filters allow the neighboring signal to mix with the actual pass band. if adjacent channel signal strength becomes strong, it will be difficult for Base Station to differentiate the actual mobile signal from the strong mobile signal.

The reasons behind adjacent channel interference are as follows:

• Due to multiple channels close to each other communicating using similar frequencies. 
• Irrelevant power emission from an adjacent channel.

Factors for reducing Adjacent Channel Interference are as follows:

• Proper filtering
• Careful Channel Assignments   
• By managing the space between two adjacent cells which should remain constant.

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