Min-max Decoding Error Probability Optimization in RIS-Aided Hybrid TDMA-NOMA Networks

One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this objective, we examine a hybrid multi-access scheme inside the finite blocklength (FBL) regime. This system combines the benefits of non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes with the aim of fulfilling the objectives of future wireless communication networks. In addition, a reconfigurable intelligent surface (RIS) is utilized to facilitate the establishment of the uplink transmission between the base station and mobile devices in situations when impediments impede their direct communication linkages. This paper aims to minimize the worst-case decoding-error probability for all mobile users by jointly optimizing power allocation, receiving beamforming, blocklength, RIS reflection, and user pairing. To deal with the coupled variables in the formulated mixed-integer non-convex optimization problem, we decompose it into three sub-problems, namely, 1) decoding order determination problem, 2) joint power allocation, receiving beamforming, RIS reflection, and blocklength optimization problem, and 3) optimal user pairing problem. Then, we provide the sequential convex approximation (SCA) and semidefinite relaxation (SDR)-based algorithms as potential solutions for iteratively addressing the deconstructed first two sub-problems at a fixed random user pairing. In addition, the Hungarian matching approach is employed to address the challenge of optimizing user pairing. In conclusion, we undertake a comprehensive simulation, which reveals the advantageous qualities of the proposed algorithm and its superior performance compared to existing benchmark methods.Comment: 11 pages, 7 figure

A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha: IRSA-RM

International audienceWireless communications play an important part in the systems of the Internet of Things (IoT). Recently, there has been a trend towards long-range communications systems for the IoT, including cellular networks. For many use cases, such as massive machine-type communications (mMTC), performance can be gained by moving away from the classical model of connection establishment and adopting random access methods. Associated with physical layer techniques such as Successive Interference Cancellation (SIC), or Non-Orthogonal Multiple Access (NOMA), the performance of random access can be dramatically improved, giving rise to novel random access protocol designs. This article studies one of these modern random access protocols: Irregular Repetition Slotted Aloha (IRSA). Since optimizing its parameters is not an easily solved problem, in this article we use a reinforcement learning approach for that purpose. We adopt one specific variant of reinforcement learning, Regret Minimization, to learn the protocol parameters. We explain why it is selected, how to apply it to our problem with centralized learning, and finally, we provide both simulation results and insights into the learning process. The results obtained show the excellent performance of IRSA when it is optimized with Regret Minimization

Low Complexity WMMSE Power Allocation In NOMA-FD Systems

In this paper we study the problem of power and channel allocation with the objective of maximizing the system sum-rate for multicarrier non-orthogonal multiple access (NOMA) full duplex (FD) systems. Such an allocation problem is non-convex and, thus, with the goal of designing a low complexity solution, we propose a scheme based on the minimization of the weighted mean square error, which achieves performance reasonably close to the optimum and allows to clearly outperforms a conventional orthogonal multiple access approach. Numerical results assess the effectiveness of our algorithm.Comment: 5 pages conference paper, 3 figures. Submitted on ICASSP 202