Channel allocation schemes

About: Channel allocation schemes is a research topic. Over the lifetime, 10656 publications have been published within this topic receiving 182117 citations.

Optimal QoS-Aware Channel Assignment in D2D Communications With Partial CSI

Abstract: In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e., the base station (BS) is assumed to be able to acquire “partial” instantaneous CSI of the cellular and D2D links, as well as, the interference links. In contrast to the existing works, multiple D2D links are allowed to share the same channel, and the quality of service (QoS) requirements for both the cellular and D2D links are enforced. We first develop an optimal channel assignment algorithm based on dynamic programming, which enjoys a much lower complexity compared with exhaustive search and will serve as a performance benchmark. To further reduce complexity, we propose a cluster-based sub-optimal channel assignment algorithm. New closed-form expressions for the expected weighted sum rate and the successful transmission probabilities are also derived. Simulation results verify the effectiveness of the proposed algorithms. Moreover, by comparing different partial CSI scenarios, we observe that the CSI of the D2D communication links and the interference links from the D2D transmitters to the BS significantly affects the network performance, while the CSI of the interference links from the BS to the D2D receivers only has a negligible impact.

Spectrum sharing between public safety and commercial users in 4G-LTE

Abstract: In this paper, we consider resource allocation optimization problem in fourth generation long term evolution (4G-LTE) for public safety and commercial users running elastic or inelastic traffic. Each mobile user can run delay-tolerant or real-time applications. In our proposed model, each user equipment (UE) is assigned a utility function that represents the application type running on the UE. Our objective is to allocate the resources from a single evolved node B (eNodeB) to each user based on the user application that is represented by the utility function assigned to that user. We consider two groups of users, one represents public safety users with elastic or inelastic traffic and the other represents commercial users with elastic or inelastic traffic. The public safety group is given priority over the commercial group and within each group the inelastic traffic is prioritized over the elastic traffic. Our goal is to guarantee a minimum quality of service (QoS) that varies based on the user type, the user application type and the application target rate. A rate allocation algorithm is presented to allocate the eNodeB resources optimally among public safety and commercial users. Finally, the simulation results are presented on the performance of the proposed rate allocation algorithm.

Optimal base station placement and fixed channel assignment applied to wireless local area network projects

Abstract: The project of a wireless local area network (WLAN) has two major issues: determining the best placement of the base stations (BS) and assigning the frequency channels for the stations. The correct BS placement minimizes the number of stations necessary to cover the desired attendance area, reducing installation costs. The channel assignment determines the frequency band to be used by each BS, minimizing interference signals between them and improving the network throughput. This work relates a real experience where we applied the concepts of two outdoor environment classic problems, the optimal base placement problem for indoor environments and the fixed channel assignment problem, to build a wireless local area network in an indoor environment. We describe the hardware we used, the integer linear programming models developed and the results obtained.

Energy-Efficient Resource Allocation in Single-Cell OFDMA Systems: Multi-Objective Approach

Abstract: In this paper, we investigate the energy-efficient resource allocation problem in a single-cell orthogonal frequency division multiple access (OFDMA) system to achieve the energy efficiency (EE) tradeoff among users Rather than overall system EE, our objective is to maximize the EE for each individual user Therefore, a multiple-objective optimization problem is formulated, which in general has many Pareto optimal solutions and is hard to solve To find its solution, we first convert it into two different single-objective optimization problems using the weighted-sum approach and the max-min approach, respectively The single-objective optimization problems are non-convex due to the combinatorial channel allocation variables Therefore, for both problems, we first provide an upper bound algorithm by relaxing the combinatorial variables and then develop a suboptimal heuristic algorithm The sum-of-ratios optimization and the generalized fractional programming are utilized for the weighted-sum problem and the max-min problem, respectively Numerical results demonstrate that both the weighted-sum and the max-min approaches can effectively solve the EE maximization problem, and the suboptimal heuristic algorithms can achieve a close performance to the corresponding upper bound algorithm

Minimizing cellular handover failures without channel utilization loss

Abstract: In cellular systems, a primary goal is the reduction of handover failures in the system. Channel allocation policies that address this by setting aside guard channels for the exclusive use of handover requests suffer from a lower utilization of the scarce channel resources. We define a class of non-reserving policies that efficiently utilize the channels. Within this class, we identify a most critical first policy that is optimal with respect to handover failure probability. Moreover, this policy is shown to have the best channel utilization in its class as well. The proofs of optimality are accompanied by the description of a technique to realize the policy in practice. Simulation results that demonstrate its improvement over previous policies are presented as well.