This study investigates a non-orthogonal multiple access (NOMA)-assisted cell-free massive multiple-input multiple-output (MIMO) system, considering the impact of both individual and linear-combination channel estimations. To make the best use of NOMA as an enabler for cell-free massive MIMO systems, user pairing should be employed effectively. Random user pairing naturally leads to a non-optimal solution, whereas an exhaustive search approach is unfavorable for practical systems owing to the high complexity. In this study, we propose an optimal user pairing strategy to group users that jointly optimize the minimum downlink rate per user and power allocation at an acceptable cost of complexity. To address this problem, we first relax the binary variables to continuous variables and then develop an iterative algorithm based on the inner approximation method, yielding at least one locally optimal solution. Numerical results show that the proposed user pairing algorithm outperforms existing counterparts, such as conventional beamforming, random pairing, far pairing, and close-pairing strategies, while it can be performed dynamically, that is, two arbitrary users satisfying the formulated problem can be paired regardless of geographical distance. Finally, our approach demonstrates that the combination channel estimation-based NOMA-assisted cell-free massive MIMO achieves the best result in terms of the downlink rate per user when associated with the proposed algorithm.

Multi-armed bandit (MAB) models are a viable approach to describe the problem of best wireless network selection by a multi-Radio Access Technology (multi-RAT) device, with the goal of maximizing the quality perceived by the final user. The classical MAB model does not allow, however, to properly describe the problem of wireless network selection by a multi-RAT device, in which a device typically performs a set of measurements in order to collect information on available networks, before a selection takes place. The MAB model foresees in fact only one possible action for the player, which is the selection of one among different arms at each time step; existing arm selection algorithms thus mainly differ in the rule according to which a specific arm is selected. This work proposes a new MAB model, named measure-use-MAB (muMAB), aiming at providing a higher flexibility, and thus a better accuracy in describing the network selection problem. The muMAB model extends the classical MAB model in a twofold manner; first, it foresees two different actions: to measure and to use; second, it allows actions to span over multiple time steps. Two new algorithms designed to take advantage of the higher flexibility provided by the muMAB model are also introduced. The first one, referred to as measure-use-UCB1 (muUCB1) is derived from the well known UCB1 algorithm, while the second one, referred to as Measure with Logarithmic Interval (MLI), is appositely designed for the new model so to take advantage of the new measure action, while aggressively using the best arm. The new algorithms are compared against existing ones from the literature in the context of the muMAB model, by means of computer simulations using both synthetic and captured data. Results show that the performance of the algorithms heavily depends on the Probability Density Function (PDF) of the reward received on each arm, with different algorithms leading to the best performance depending on the PDF. Results highlight, however, that as the ratio between the time required for using an arm and the time required to measure increases, the proposed algorithms guarantee the best performance, with muUCB1 emerging as the best candidate when the arms are characterized by similar mean rewards, and MLI prevailing when an arm is significantly more rewarding than others. This calls thus for the introduction of an adaptive approach capable of adjusting the behavior of the algorithm or of switching algorithm altogether, depending on the acquired knowledge on the PDF of the reward on each arm.

In overloaded Massive MIMO (mMIMO) 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 the sum spectral efficiency. This paper aims at identifying cases where code-domain NOMA can improve the spectral efficiency of mMIMO in the classical regime where K <; M. Novel spectral efficiency expressions are provided for the uplink and downlink with arbitrary spreading signatures and spatial correlation matrices. Particular attention is devoted to the planar arrays that are currently being deployed in pre-5G and 5G networks (in sub-$6$ GHz bands), which are characterized by limited spatial resolution. Numerical results show that mMIMO with such planar arrays can benefit from NOMA in scenarios where the UEs are spatially close to each other. A two-step UE grouping scheme is proposed for NOMA-aided mMIMO systems that is applicable to the spatial correlation matrices of the UEs that are currently active in each cell. Numerical results are used to investigate the performance of the algorithm under different operating conditions and types of spreading signatures (orthogonal, sparse and random sets). The analysis reveals that orthogonal signatures provide the highest average spectral efficiency.

Spectral efficiency of low-density spreading nonorthogonal multiple access channels in the presence of fading is derived for linear detection with independent decoding as well as 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 vs. 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.

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 ).

This study investigates the integration of an intelligent reflecting surface (IRS) into an unmanned aerial vehicle (UAV) platform to utilize the advantages of these leading technologies for sixth-generation communications, e.g., improved spectral and energy efficiency, extended network coverage, and flexible deployment. In particular, we investigate a downlink IRS–UAV system, wherein single-antenna ground users (UEs) are served by a multi-antenna base station (BS). To assist the communication between UEs and the BS, an IRS mounted on a UAV is deployed, in which the direct links are obstructed owing to the complex urban channel characteristics. The beamforming at the BS, phase shift at the IRS, and the 3D placement of the UAV are jointly optimized to maximize the sum rate. Because the optimization variables, particularly the beamforming and IRS phase shift, are highly coupled with each other, the optimization problem is naturally non-convex. To effectively solve the formulated problem, we propose an iterative algorithm that employs block coordinate descent and inner approximation methods. Numerical results demonstrate the effectiveness of our proposed approach for a UAV-mounted IRS system on the sum rate performance over the state-of-the-art technology using the terrestrial counterpart.