Resource allocation for statistical QoS guarantees in MIMO cellular networks

Abstract

This work considers the performance of the downlink channel of MIMO cellular networks serving multiple users with different statistical QoS requirements. The paper proposes resource allocation algorithms that aim to optimize the system performance over the sum of the optimal user utility functions by employing the effective capacity theory. Proportionally fair resource allocation among the users is performed via two different approaches and solutions, namely, the Frame Allocation Algorithm (FAA), which involves dynamic time allocation for transmit beamforming, and the Power Allocation Algorithm (PAA), which provides optimal power control for space division multiple access. In FAA, each user is assigned a distinct slot of optimal length, based on the instantaneous channel conditions of the active users in each frame; while in PAA, resource allocation is performed via power assignment by taking into account the long-term averages of the channel conditions across all users. The efficacy of the proposed algorithms are demonstrated via numerical experiments considering realistic channel models and various QoS settings.

A survey and taxonomy on nonorthogonal multiple‐access schemes for 5G networks

“…For example, energy efficiency is considered as a KPI in the work of Sun et al, where the authors maximized energy efficiency for the MIMO‐NOMA systems. Average time allocation for each link is considered as a KPI in the work of Memis et al, where a resource allocation scheme is presented to optimize the system performance. In the work of Marshoud et al, the average number of handovers is considered as a KPI where a power allocation scheme is presented while considering the channel conditions in NOMA for visible light communications.…”

“…Likewise, the benefits of NOMA to enhance energy efficiency are discussed in the work of Zhang et al The objective was to improve the energy efficiency subject to the minimum required data rate. There are other objectives as well that are considered in the literature, such as performing efficient channel quality indicator estimation in the works of Benjebbour et al and Kim et al, slot allocation in the work of Memis et al, the number of BSs, and symbol pair error rate …”

“…One of the significant aspects of optimization in NOMA is the performance and computational complexity of the solution approaches. We can categorize the optimization problems in NOMA as follows: convex, nonconvex, difference of convex, concave‐convex, and mixed‐integer linear programming . Standard optimization techniques are used to resolve the convex problem types.…”

“…A mixed‐integer nonlinear programming method for transmission scheduling and power allocation of the systems is presented in the work of Yang and Hsieh to compare the user's data rate remaining in the power domain with the iterative, greedy, and heuristic method. Previous studies used a transformation approach to renovate the problem construction in the power domain as a concave‐convex type in order to solve it with iterative methods. This helped in investigating the throughput and the time allocation of each link in the power domain.…”

Appraising the Research Trend of Energy Utilization Problem in Cellular Network

“…Mtaho and Ishengoma [43] have investigated about various essential parameters that directly influence the QoS considering the case study of cellular data from Tanzania. Memis et al [44] have carried out the study for allocating network resources for enhancing the QoS factor in cellular network. The uniqueness in this study is MIMO which is used to enhance the data rate of the cellular network.…”