Efficient channel management is a challenge that next-generation wireless networks need to meet in order to satisfy increasing bandwidth demand and transmission rate requirements. Non-orthogonal multiple access (NOMA) is one of such efficient channel allocation methods used in 5G backhaul wireless mesh networks. In this paper, we propose a power demand-based channel allocation method for 5G backhaul wireless mesh networks by employing NOMA and considering traffic demands in small cells, thereby improving channel utility. In this scheme, we work with physical layer transmission. The foremost aim is to mutually optimize the uplink/downlink NOMA channel assignment in order to increase user fairness. The approach concerned may be divided into two steps. First, initial channel allocation is performed by employing the traveling salesman problem (TSP), due to its similarity to many-to-many double-side user-channel allocation. Second, the modified particle swarm optimization (PSO) method is applied for allocation updates, by introducing a decreasing coefficient which may have the form of a standard stochastic estimate algorithm. To enhance exploration capacity of modified the PSO, a random velocity is included to optimize the convergence rate and exploration behavior. The performance of the designed scheme is estimated through simulation, taking into account such parameters as through put, spectral efficiency, sum-rate, outage probability, signal to-interference plus noise ratio (SINR), and fairness. The proposed scheme maximizes network capacity and improves fairness between the individual stations. Experimental results show that the proposed technique performs better than existing solutions.
In cellular networks, cells are grouped more densely around highly populated areas to provide more capacity. Antennas are pointed in accordance with local terrain and clutter to reduce signal shadows and interference. Hardware parameters are easily set during installation but difficult to change thereafter. In a dynamic environment of population migration, there is need to continuously tune network parameters to adapt the network performance. Modern mobile equipment logs network usage patterns and statistics over time. This information can be used to tune soft parameters of the network. These parameters may include frequency channel assignment or reuse, and transmitter radiation power assignment to provide more capacity on demand. The paper proposes that by combining the frequency and power assignments, further optimisation in resource allocation can be achieved over a traditional frequency assignment. The solution considers the interference, traffic intensity and use of priority flags to bias some edges. An Edge Weight Power and Frequency Assignment Algorithm is presented to solve the resource allocation problem in cellular networks. The paper also analyses the performance improvements obtained over that of the Edge Weight Frequency Assignment Algorithm. The results show that the proposed algorithm improves the performance of the Edge Weight Frequency Assignment Algorithm depending on the initial structure of the graph.
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The problem of non-uniform traffic demand in different cells of a cellular network may lead to a gross imbalance in the system performance. Thus, the users in hot cells may suffer from low throughput. In this paper, an effective and simple load balancing scheme CAC_DPLB_MCN is proposed that can effectively reduce the overall call blocking. This model considers dealing with multi-media traffic as well as time-varying geographical traffic distribution. The proposed scheme uses the concept of cell-tiering thereby creating fractional frequency reuse environment. A message exchange based distributed scheme instead of centralized one is used which help the proposed scheme be implemented in a multiple hot cell environment also. Furthermore, concept of dynamic pricing is used to serve the best interest of the users as well as for the service providers. The performance of the proposed scheme is compared with two other existing schemes in terms of call blocking probability and bandwidth utilization. Simulation results show that the proposed scheme can reduce the call blocking significantly in highly congested cell with highest bandwidth utilization. Use of dynamic pricing also makes the scheme useful to increase revenue of the service providers in contrast with compared schemes.
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In cellular networks, while the motivation behind all basic channel assignment strategies is the better utilization of the available frequency spectrum with the consequent reduction of the call blocking probability in each cell, very few of them deal with the problem of non-uniform traffic demand in different cells which may lead to a gross imbalance in the system performance. Mobile users in hot cells (the cells with heavy traffic loads) may suffer from low throughput due to the load imbalance problem. In this paper, we propose a cost effective and simple load balancing scheme that can effectively reduce the overall call blocking. A common set of channels are determined dynamically which can be used simultaneously in all the cells. Cell tiers with different radii are used to cope with the interference introduced by using same set of channels simultaneously in all cells. The performance of the proposed scheme is presented in terms of call blocking probability and channel utilization. Simulation results show that the proposed scheme can reduce the call blocking significantly in highly congested cell.
This paper addresses the problem of fixed channel assignment in cellular communication systems with nonuniform traffic distribution. The objective of the channel assignment is to minimise the average blocking probability. Methods for finding a good allocation can be based on first building a number of sets of cochannel cells or allocation patterns and then assigning them to channels. This usually implies that only a subset of the feasible region is attainable. The approach suggested in this paper uses the concept of packed pattern, since all patterns in an optimal solution will be of that kind. With a constructive method, the entire set of packed patterns is built and used in the optimisation process. The complexity (large-scale and nonlinearity) of the resulting problem suggested the use of general search procedures (local search, tabu search, simulated annealing, etc.), which have the further advantage of flexibility when dealing with extensions to the problem. A neighbouring structure was used, that facilitated the calculations while still allowing for the search in the entire solution space. A summary of extensive numerical experiments is presented. The outcome is an improvement over previous results.
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