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The main resource for any telecom operator is the physical radio cell network. We present two related methods for optimizing utilization in radio networks: Tetris optimization and selective cell expansion. Tetris optimization tries to find the mix of users from different market segments that provides the most even load in the network. Selective cell expansion identifies hotspot cells, expands the capacity of these radio cells, and calculates how many subscribers the radio network can handle after the expansions. Both methods are based on linear programming and use mobility data, i.e., data defining where different categories of subscribers tend to be during different times of the week. Based on real-world mobility data from a region in Sweden, we show that Tetris optimization based on six user segments made it possible to increase the number of subscribers by 58% without upgrading the physical infrastructure. The same data show that by selectively expanding less than 6% of the cells we are able to increase the number of subscribers by more than a factor of three without overloading the network. We also investigate the best way to combine Tetris optimization and selective cell expansion. [ABSTRACT FROM AUTHOR]

The present study deals with the double orbit finite capacity retrial queues with unreliable server. The system facilitates the arrival of two types of customers known as priority and non priority customers and can hold a maximum of L priority customers and K non-priority customers as per its capacity. The priority customers are served prior to the non-priority customers. Moreover, the server is unreliable which may breakdown while servicing either priority or non-priority customer. The failed server is sent for repair following threshold recovery policy to become as good as earlier. Both transient as well as steady state analysis of the model has been done using by matrix method. Various performance measures including queue length, reliability metrics, long run probabilities, etc. have been obtained using various state probabilities. The application of the model to cellular radio network has been discussed. The cost function has been constructed and optimized using meta heuristic approach. The sensitivity analysis of various performance indices has been performed as an illustration. [ABSTRACT FROM AUTHOR]

Uplink communication in a cellular radio network is considered where the base station in each cell employs linear or decision feedback multiuser receivers. We address the problem of minimizing the total of the user transmit powers over all linear or decision feedback multiuser receivers, such that all the users achieve their signal-to-interference ratio (SIR) targets. A distributed algorithm is obtained that is shown to converge, both in the deterministic and stochastic formulations, to the jointly optimal pair of power allocations and multiuser receivers. [ABSTRACT FROM PUBLISHER]

An important problem that arises in the design of cellular radio networks is the channel assignment problem that addresses the key issue of the scheduling of the transmissions of all stations in the network while avoiding interference. The problem consists of identifying a frame of minimum length that avoids interference while satisfying channel demands of the stations and maximizing channel utilization. The problem is known to be NP-hard and is formulated as a nonlinear integer program in prior research. This paper develops, for the first time, an integer programming formulation for the problem and presents a Probabilistic Greedy Algorithm that avoids the basic shortcoming of the classical greedy approach which is getting trapped in local optima. The algorithm incorporates simultaneously two diversification techniques, namely randomization and perturbation. Computational experiments are conducted on benchmark data sets and randomly generated problem instances with up to 500 stations. The results show the proposed algorithm is very effective in generating good solutions in short computing time. While a general-purpose branch-and-bound algorithm fails to find feasible solutions even for small instances, the proposed algorithm produces solutions that are far better than those obtained using a classical greedy approach in acceptable time. [ABSTRACT FROM PUBLISHER]