Performance Analysis of Uplink NOMA-Relevant Strategy Under Statistical Delay QoS Constraints

A new multiple access (MA) strategy, referred to as non orthogonal multiple access - Relevant (NOMA-R), allows selecting NOMA when this increases all individual rates, i.e., it is beneficial for both strong(er) and weak(er) individual users. This letter provides a performance analysis of the NOMA-R strategy in uplink networks with statistical delay constraints. Closed-form expressions of the effective capacity (EC) are provided in two-users networks, showing that the strong user always achieves a higher EC with NOMA-R. Regarding the network's sum EC, there are distinctive gains with NOMA-R, particularly under stringent delay constraints

NOMA-Relevant Clustering and Resource Allocation for Proportional Fair Uplink Communications

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Optimal Power Allocation for the Two-Way Relay Channel with Data Rate Fairness

Abstract—This letter studies data rate fairness on the twoway relay channel. It analytically determines the optimal power values at both nodes and at the relay, that lead to a maximization of the sum rate under the fairness constraint. Amplify-and-Forward (AF) and Decode-and-Forward (DF) relaying protocols are considered. For AF, the optimization problem is turned into a single-variable convex optimization problem. For DF, rate balancing between Multiple Access and broadcast phases must be performed prior to setting nodes powers. Both optimized protocols are compared with reference AF and DF in terms of data rates through numerical simulations. Index Terms—Two-way relay channel, power optimization, fairness, amplify-and-forward, decode-and-forward. I

Clustering and Power Optimization for NOMA Multi-Objective Problems

This paper considers uplink multiple access (MA) transmissions, where the MA technique is adaptively selected between Non Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA). Two types of users, namely Internet of Things (IoT) and enhanced mobile broadband (eMBB) coexist with different metrics to be optimized, energy efficiency (EE) for IoT and spectral efficiency (SE) for eMBB. The corresponding multi-objective power allocation problems aiming at maximizing a weighted sum of EE and SE are solved for both NOMA and OMA. Based on the identification of the best MA strategy, a clustering algorithm is then proposed to maximize the multi-objective metric per cluster as well as NOMA use. The proposed clustering, power allocation and MA selection algorithm is shown to outperform other clustering solutions and non-adaptive MA techniques