User Fairness Performance Research Articles

Reinforcement learning-based resource allocation for dynamic aggregated WiFi/VLC HetNet

High transmission rates and low power consumption make visible light communication (VLC) a highly promising supplementary technology for the next generation of mobile communication. Taking into account the limited coverage area, VLC can be combined with WiFi as a heterogeneous network (HetNet), thanks to their non-overlapping spectrum. In order to fully utilize the advantages of the WiFi and VLC technologies, a new aggregated WiFi/VLC HetNet consisting of a single WiFi AP and multiple VLC APs, is designed, where the user equipment (UE), for the first time, can access multiple VLC APs and one WiFi AP simultaneously, and be allowed to acquire multiple resource blocks (RB) in each AP at the same time. To optimize the performance of the above designed HetNet, a multi-objective optimization problem (MOOP) is formulated, which aims to maximize system throughput while reducing the handover rate. Since the above MOOP is non-convex and nonlinear, the traditional resource allocation (RA) algorithm has a complex calculation process and poor timeliness performance to deal with this problem. To solve the above MOOP, a reinforcement learning (RL)-based RA algorithm is proposed, and considering the RB handover overhead in the aggregated WiFi/VLC HetNet, a reward function related to the system throughput and the UE handover rate is carefully designed. System throughput, system handover rate, user satisfaction, as well as user fairness performance are analyzed under three typical indoor illumination layouts. Finally, compared with the greedy algorithm and the hypergraph-based carrier aggregation algorithm, numerical results show that the proposed RL-based RA algorithm could improve the system throughput over the former two algorithms by 30.26% and 19.71%, while reducing the system handover rate by 0.15% and 0.02%, respectively.

Proportional rate constrained resource allocation in multicarrier nonorthogonal multiple access systems

AbstractIn multicarrier nonorthogonal multiple access (MC‐NOMA) systems, resource allocation for user rate fairness is an inherent challenge due to the huge difference in channel conditions among different users. As the general case of proportional fairness, proportional rate constraint is usually adopted in resource allocation optimization problems to ensure that achievable rates of users are distributed proportionally according to a set of arbitrarily predefined portion. In this article, we investigate joint subcarrier and power allocation in MC‐NOMA systems under proportional rate constraints. The formulated resource allocation optimization problem belongs to a mixed integer nonlinear programming (MINLP) problem due to its binary subcarrier allocation variables and nonaffine equality constraints. By introducing an auxiliary variable in proportional rate constraints and substituting power variable with rate variable, we convert this nonconvex problem into a convex optimization problem and solve it by using Karush‐Kuhn‐Tucker (KKT) conditions and the dual decomposition method. Based on the optimal solutions, we divide the resource allocation process into two phases, that is, subcarrier allocation and power allocation, and develop an optimal subcarrier allocation solution based resource allocation algorithm (OSSRA). Furthermore, we also propose a heuristic algorithm, referred to as proportional rate constrained subcarrier allocation algorithm (PCSAA), as a benchmark. Numerical results show that, substantial gains can be achieved by OSSRA over the existing work and the traditional orthogonal multiple access (OMA) schemes in terms of both sum rate and user fairness. Besides, PCSAA has better user fairness performance than OSSRA.

A Novel NOMA Solution With RIS Partitioning

Reconfigurable intelligent surface (RIS) empowered communications with non-orthogonal multiple access (NOMA) has recently become an appealing research direction for next-generation wireless communications. In this paper, we propose a novel NOMA solution with RIS partitioning, where we aim to enhance the spectrum efficiency by improving the ergodic rate of all users, and to maximize the user fairness. In the proposed system, we distribute the physical resources among users such that the base station (BS) and RIS are dedicated to serve different clusters of users. Furthermore, we formulate an RIS partitioning optimization problem to slice the RIS elements between the users such that the user fairness is maximized. The formulated problem is shown to be a non-convex and non-linear integer programming (NLIP) problem with a combinatorial feasible set, which is challenging to solve. Therefore, we exploit the structure of the problem to bound its feasible set and obtain a sub-optimal solution by sequentially applying three efficient search algorithms. Furthermore, we derive exact and asymptotic expressions for the outage probability. Simulation results clearly indicate the superiority of the proposed system over the considered benchmark systems in terms of ergodic sum-rate, outage probability, and user fairness performance.

Sub-Channel and Power Allocation for Non-Orthogonal Multiple Access Relay Networks With Amplify-and-Forward Protocol

In this paper, we study the resource allocation problem for a single-cell non-orthogonal multiple access (NOMA) relay network where an OFDM amplify-and-forward relay allocates the spectrum and power resources to the source–destination (SD) pairs. We aim to optimize the resource allocation to maximize the average sum-rate. The optimal approach requires an exhaustive search, leading to an NP-hard problem. To solve this problem, we propose two efficient many-to-many two-sided SD pair-subchannel matching algorithms, in which the SD pairs and sub-channels are considered as two sets of players chasing their own interests. The proposed algorithms can provide a sub-optimal solution to this resource allocation problem in affordable time. Both the static matching algorithm and the dynamic matching algorithm converge to a pair-wise stable matching after a limited number of iterations. Simulation results show that the capacity of both proposed algorithms in the NOMA scheme significantly outperforms the conventional orthogonal multiple access scheme. The proposed matching algorithms in NOMA scheme also achieve a better user-fairness performance than the conventional orthogonal multiple access.

Multi Objective Resource Scheduling in LTE Networks Using Reinforcement Learning

The use of the intelligent packet scheduling process is absolutely necessary in order to make the radio resources usage more efficient in recent high-bit-rate demanding radio access technologies such as Long Term Evolution (LTE). Packet scheduling procedure works with various dispatching rules with different behaviors. In the literature, the scheduling disciplines are applied for the entire transmission sessions and the scheduler performance strongly depends on the exploited discipline. The method proposed in this paper aims to discuss how a straightforward schedule can be provided within the transmission time interval (TTI) sub-frame using a mixture of dispatching disciplines per TTI instead of a single rule adopted across the whole transmission. This is to maximize the system throughput while assuring the best user fairness. This requires adopting a policy of how to mix the rules and a refinement procedure to call the best rule each time. Two scheduling policies are proposed for how to mix the rules including use of Q learning algorithm for refining the policies. Simulation results indicate that the proposed methods outperform the existing scheduling techniques by maximizing the system throughput without harming the user fairness performance.