This paper proposes a power allocation framework based on the per-user quality-of-experience (QoE) conditions for an aerial relay massive MIMO (mMIMO) network, assuming the direct transmission between the mMIMO base station and ground users (UEs) is unavailable. We first derive closed-form spectral efficiency expressions for the mMIMO-based system, with a UAV acting as the aerial relay. Then, we formulate a joint optimization problem of power allocation and QoE in the downlink, aiming to maximize the sum throughput of the serving ground UEs. The problem is generally hard to solve due to the non-convex constraints and non-concave objective function. To address it, we propose a two-step algorithm based on inner convex approximation (ICA) method. However, the ICA-based algorithm requires an initial feasible point, which is difficult to find by generating a random point as commonly conceived in existing approaches while satisfying the QoE constraints. To tackle this issue, we develop a max-min problem, whose solution leads to an initial feasible point that maximizes the difference between the per-user rate and its corresponding QoE threshold. Numerical results are used to demonstrate the validity of the theoretical analysis and the effectiveness of the proposed algorithms.

In overloaded Massive MIMO systems, wherein the number K of user equipments (UEs) exceeds the number of base station antennas M, it has recently been shown that non-orthogonal multiple access (NOMA) can increase performance. This paper aims at identifying cases of the classical operating regime K <; M, where code-domain NOMA can also improve the spectral efficiency of Massive MIMO. Particular attention is given to use cases in which poor favorable propagation conditions are experienced. Numerical results show that Massive MIMO with planar antenna arrays can benefit from NOMA in practical scenarios where the UEs are spatially close to each other.

Motivated by recent theoretical challenges for 5G, this study aims to position relevant results in the literature on code‐domain non‐orthogonal multiple access (NOMA) from an information‐theoretic perspective, given that most of the recent intuition of NOMA relies on another domain, that is, the power domain. Theoretical derivations for several code‐domain NOMA schemes are reported and interpreted, adopting a unified framework that focuses on the analysis of the NOMA spreading matrix, in terms of load, sparsity, and regularity features. The comparative analysis shows that it is beneficial to adopt extreme low‐dense code‐domain NOMA in the large system limit, where the number of resource elements and number of users grow unboundedly while their ratio, called load, is kept constant. Particularly, when optimum receivers are used, the adoption of a regular low‐dense spreading matrix is beneficial to the system achievable rates, which are higher than those obtained with either irregular low‐dense or dense formats, for any value of load. For linear receivers, which are more favourable in practice due to lower complexity, the regular low‐dense NOMA still has better performance in the underloaded regime (load ), while the irregular counterpart outperforms all the other schemes in the overloaded scenario (load ).

Spectral efficiency of low-density spreading non-orthogonal multiple access channels in the presence of fading is derived for linear detection with independent decoding and optimum decoding. The large system limit, where both the number of users and number of signal dimensions grow with fixed ratio, called load, is considered. In the case of optimum decoding, it is found that low-density spreading underperforms dense spreading for all loads. Conversely, linear detection is characterized by different behaviors in the underloaded versus overloaded regimes. In particular, it is shown that spectral efficiency changes smoothly as load increases. However, in the overloaded regime, the spectral efficiency of low-density spreading is higher than that of dense spreading.