Optimal channel allocation with dynamic power control in cellular networks

Techniques for channel allocation in cellular networks have been an area of intense research interest for many years. An efficient channel allocation scheme can significantly reduce call-blocking and calldropping probabilities. Another important issue is to effectively manage the power requirements for communication. An efficient power control strategy leads to reduced power consumption and improved signal quality. In this paper, we present a novel integer linear program (ILP) formulation that jointly optimizes channel allocation and power control for incoming calls, based on the carrier-to-interference ratio (CIR). In our approach we use a hybrid channel assignment scheme, where an incoming call is admitted only if a suitable channel is found such that the CIR of all ongoing calls on that channel, as well as that of the new call, will be above a specified value. Our formulation also guarantees that the overall power requirement for the selected channel will be minimized as much as possible and that no ongoing calls will be dropped as a result of admitting the new call. We have run simulations on a benchmark 49 cell environment with 70 channels to investigate the effect of different parameters such as the desired CIR. The results indicate that our approach leads to significant improvements over existing techniques.Comment: 11 page

Application of genetic algorithm to wireless communications

Wireless communication is one of the most active areas of technology development of our time. Like all engineering endeavours, the subject of the wireless communication also brings with it a whole host of complex design issues, concerning network design, signal detection, interference cancellation, and resource allocation, to name a few. Many of these problems have little knowledge of the solution space or have very large search space, which are known as non-deterministic polynomial (NP) -hard or - complete and therefore intractable to solution using analytical approaches. Consequently, varied heuristic methods attempts have been made to solve them ranging from simple deterministic algorithms to complicated random-search methods. Genetic alcyorithm (GA) is an adaptive heuristic search algorithm premised on the evolutionary ideas of evolution and natural selection, which has been successfully applied to a variety of complicated problems arising from physics, engineering, biology, economy or sociology. Due to its outstanding search strength and high designable components, GA has attracted great interests even in the wireless domain. This dissertation is devoted to the application of GA to solve various difficult problems spotlighted from the wireless systems. These problems have been mathematically formulated in the constrained optimisation context, and the main work has been focused on developing the problem-specific GA approaches, which incorporate many modifications to the traditional GA in order to obtain enhanced performance. Comparative results lead to the conclusion that the proposed GA approaches are generally able to obtain the optimal or near-optimal solutions to the considered optimisation problems provided that the appropriate representation, suitable fitness function, and problem-specific operators are utilised. As a whole, the present work is largely original and should be of great interest to the design of practical GA approaches to solve realistic problems in the wireless communications systems.EThOS - Electronic Theses Online ServiceBritish Council (ORS) : Newcastle UniversityGBUnited Kingdo

A quasi-static cluster-computing approach for dynamic channelassignment in cellular mobile communication systems

Efficient management of the radio spectrum can be accomplished by making use of channel assignment techniques, which work by allocating different channels of the spectrum to the cells of the network in a conflict-free manner (i.e., the co-channel interference is minimized). The problem of dynamically reallocating the channels in response to change in user location patterns, which occurs frequently for a microcell network architecture, is even more difficult to tackle in a timely manner. Most existing approaches use various sequential search-based heuristics which cannot produce high-quality allocation fast enough to cope with the frequent traffic requirement variations. In this paper, we propose a quasi-static approach which combines the merits of both static and dynamic schemes. The static component of our approach uses a parallel genetic algorithm to generate a suite of representative assignments based on a set of different estimated traffic scenarios. At on-line time, the dynamic component observes the actual traffic requirement and retrieves the representative assignment of the closest scenario from the off-line table. The retrieved assignment is then quickly refined by using a fast parallel local search algorithm. Our extensive simulation experiments have indicated that the proposed quasi-static system outperforms other dynamic channel assignment techniques significantly in terms of both blocking probabilities and computational overhead.published_or_final_versio

Optimal channel assignment and power control in wireless cellular networks

Wireless mobile communication is a fast growing field in current telecommunication industry. In a wireless cellular network, channel assignment is a mechanism that assigns channels to mobile users in order to establish a communication between a mobile terminal and a base station. It is important to determine an optimal allocation of channels that makes effective use of channels and minimizes call-blocking and call-dropping probabilities. Another important issue, the power control, is a problem of determining an optimal allocation of power levels to transmitters such that the power consumption is minimized while signal quality is maintained. In wireless mobile networks, channels and transmitter powers are limited resources. Therefore, efficient utilization of both those resources can significantly increase the capacity of network. In this thesis, we solve such optimizations by the hybrid channel assignment (HCA) method using integer linear programming (ILP). Two novel sets of ILP formulation are proposed for two different cases: Reuse Distance based HCA without power control, and Carrier-to-Interference Ratio based HCA combined with power control. For each of them, our experimental results show an improvement over other several approaches

A distributed channel allocation scheme for cellular network using intelligent software agents

PhDAbstract not availabl

Performance analysis of the interference adaptation dynamic channel allocation technique in wireless communication networks

Dynamic channel allocation (DCA) problem is one of the major research topics in the wireless networking area. The purpose of this technique is to relieve the contradiction between the increasing traffic load in wireless networks and the limited bandwidth resource across the air interface. The challenge of this problem comes from the following facts: a) even the basic DCA problem is shown to be NP-complete (none polynomial complete); b) the size of the state space of the problem is very large; and c) any practical DCA algorithm should run in real-time. Many heuristic DCA schemes have been proposed in the literature. It has been shown through simulation results that the interference adaptive dynamic channel allocation (IA-DCA) scheme is a promising strategy in Time Devision [sic] Multiple Accesss/Frequency Devision [sic] Multiple Accesss [sic] (TDMA/FDMA) based wireless communication systems. However, the analytical work on the IA-DCA strategy in the literature is nearly blank. The performance of a, DCA algorithm in TDMA/FDMA wireless systems is influenced by three factors: representation of the interference, traffic fluctuation, and the processing power of the algorithm. The major obstacle in analyzing IA-DCA is the computation of co-channel interference without the constraint of conventional channel reuse factors. To overcome this difficulty, one needs a representation pattern which can approximate the real interference distribution as accurately as desired, and is also computationally viable. For this purpose, a concept called channel reuse zone (CRZ) is introduced and the methodology of computing the area of a CRZ with an arbitrary, non-trivial channel reuse factor is defined. Based on this new concept, the computation of both downlink and uplink CO-channel interference is investigated with two different propagation models, namely a simplified deterministic model and a shadowing model. For the factor of the processing power, we proposed an idealized Interference Adaptation Maximum Packing (IAMP) scheme, which gives the upper bound of all IA-DCA schemes in terms of the system capacity. The effect of traffic dynamics is delt [sic] with in two steps. First, an asymptotic performance bound for the IA-DCA strategy is derived with the assumption of an arbitrarily large number of channels in the system. Then the performance bound for real wireless systems with the IA-DCA strategy is derived by alleviating this assumption. Our analytical result is compared with the performance bound drawn by Zander and Eriksson for reuse-partitioning DCA1 and some simulation results for IA-DCA in the literature. It turns out that the performance bound obtained in this work is much tighter than Zander and Eriksson\u27s bound and is in agreement with simulation results. 1only available for deterministic propagation model and downlink connection

Fixed channel assignment in cellular radio networks using a modified genetic algorithm

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict-free channel assignment with the minimum channel span is NP hard. Therefore, we formulate the problem by assuming a given channel span. Our objective is to obtain a conflict-free channel assignment among the cells, which satisfies both the electromagnetic compatibility (EMC) constraints and traffic demand requirements. We propose an approach based on a modified genetic algorithm (GA). The approach consists of a genetic-fix algorithm that generates and manipulates individuals with fixed size (i.e., in binary representation, the number of ones is fixed) and a minimum-separation encoding scheme that eliminates redundant zeros in the solution representation. Using these two strategies, the search space can be reduced substantially. Simulations on the first four benchmark problems showed that this algorithm could achieve at least 80%, if not 100%, convergence to solutions within reasonable time. In the fifth benchmark problem, our algorithm found better solutions with shorter channel span than any existing algorithms. Such significant results indicate that our approach is indeed a good method for solving the channel-assignment problem. © 1998 IEEE.published_or_final_versio