Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV’s (i) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of (i) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

The design of feedback channels in frequency division duplex (FDD) systems is a major challenge because of the limited available feedback bits. We consider non-orthogonal multiple access (NOMA) systems that incorporate reconfigurable intelligent surfaces (RISs). In limited feedback RIS-aided NOMA systems, the RIS-aided channel and the direct channel gains should be quantized and fed back to the transmitter. This paper investigates the rate loss of the overall RIS-aided NOMA systems suffering from quantization errors. We first consider random vector quantization for the overall RIS-aided channel and identical uniform quantizers for the direct channel gains. We then obtain an upper bound for the rate loss, due to the quantization error, as a function of the number of feedback bits and the size of RIS. Our numerical results indicate the sum rate performance of the limited feedback system approaches that of the system with full CSI as the number of feedback bits increases.

The use of directional transmission in millimeter-Wave (mmWave) frequencies results in limited channel coherence time. In this paper, we take the limited channel coherence time into account for non-orthogonal multiple access (NOMA) in mmWave hybrid beamforming systems. Due to the limited coherence time, the beamwidth of the hybrid beamformer affects the beam-training time, which in turn directly impacts the data transmission rate. To investigate this trade-off, we utilize a combined beam-training algorithm. Then, we formulate a sum-rate expression which considers the channel coherence time and beam-training time as well as users' power and other system parameters. Further, a joint power and beamwidth optimization problem is solved by iterating between the power allocation and the beamwidth optimization. When allocating the power, we use the log-exponential reformulation and the sequential parametric convex approximation (SPCA) methods to solve the non-convex problem. Since beamwidth optimization involves too many variables, we propose an algorithm which iterates between clusters of users. Numerical results show that the optimized mmWave hybrid beamforming-NOMA system can achieve much higher sum-rates compared to NOMA with analog beamforming and traditional multiple access techniques.

In this paper, we propose a generalized millimeter-Wave (mmWave) reconfigurable antenna multiple-input multiple-output (RA-MIMO) architecture that takes advantage of lens antennas. The considered antennas can generate multiple independent beams simultaneously using a single RF chain. This property, together with RA-MIMO, is used to combat small-scale fading and shadowing in mmWave bands. To this end, first, we derive a channel matrix for RA-MIMO. Then, we use rate-one space-time block codes (STBCs), together with phase-shifters at the receive reconfigurable antennas, to suppress the effect of small-scale fading. We consider two kinds of phase shifters: i) ideal which is error-free and ii) digital which adds quantization error. The goal of phase-shifters is to convert a complex-valued channel matrix into real-valued. Hence, it is possible to use rate-one STBCs for any dimension of RA-MIMO. We investigate diversity gain and derive an upper bound for symbol error rate in cases of ideal and digital phase-shifters. We show that RA-MIMO achieves the full-diversity gain with ideal phase-shifters and the full-diversity gain for digital phase-shifters when the number of quantization bits is higher than one. We investigate RA-MIMO in the presence of shadowing. Our analysis demonstrates that, by increasing the dimension of RA-MIMO, the outage probability decreases which means the effect of shadowing decreases. Numerical results verify our theoretical derivations.

The effect of beam misalignment on rate performance of the downlink hybrid beamforming-based non-orthogonal multiple access (HB-NOMA) systems is analyzed in this paper. First, an HB-NOMA framework is introduced in the context of multiuser millimeter wave (mmWave) communications, a sum-rate maximization problem is formulated for that, and an algorithm is introduced to design digital and analog precoders and power allocation. Then, a lower bound for the achievable rate of the perfectly aligned line-of-sight (LoS) channels is derived. Subsequently, the impact of beam misalignment is evaluated when the users experience misaligned LoS or non-LoS channels. To this end, a misalignment factor is modeled and each misaligned effective channel is described in terms of the perfectly aligned effective channel parameters and the misalignment factor. Furthermore, a lower bound for the achievable rate, and an upper bound for the rate gap expression between the aligned and misaligned HB-NOMA systems are established. The analyses reveal that a large misalignment can remarkably degrade the rate. Extensive numerical simulations are conducted to verify the findings.

Self-organizing networks (SONs) can help to manage the severe interference in dense heterogeneous networks (HetNets). Given their need to automatically configure power and other settings, machine learning is a promising tool for data-driven decision making in SONs. In this paper, a HetNet is modeled as a dense two-tier network with conventional macrocells overlaid with denser small cells (e.g. femto or pico cells). First, a distributed framework based on the multi-agent Markov decision process is proposed that models the power optimization problem in the network. Second, we present a systematic approach for designing a reward function based on the optimization problem. Third, we introduce Q-learning-based distributed power allocation algorithm (Q-DPA) as a self-organizing mechanism that enables the ongoing transmit power adaptation as new small cells are added to the network. Furthermore, the sample complexity of the Q-DPA algorithm to achieve ϵ-optimality with high probability is provided. We demonstrate, at the density of several thousands femtocells per km2, the required quality of service of a macrocell user can be maintained via the proper selection of independent or cooperative learning and appropriate Markov state models.

The large spectrum available in the millimeter- Wave (mmWave) band has emerged as a promising solution for meeting the huge capacity requirements of the 5th generation (5G) wireless networks. However, to fully harness the potential of mmWave communications, obstacles such as severe path loss, channel sparsity and hardware complexity should be overcome. In this paper, we introduce a generalized reconfigurable antenna multiple-input multiple-output (MIMO) architecture that takes advantage of lens-based reconfigurable antennas. The considered antennas can support multiple radiation patterns simultaneously by using a single RF chain. The degrees of freedom provided by the reconfigurable antennas are used to, first, combat channel sparsity in MIMO mmWave systems. Further, to suppress high path loss and shadowing at mmWave frequencies, we use a rate- one space-time block code. Our analysis and simulations show that the proposed reconfigurable MIMO architecture achieves full-diversity gain by using linear receivers and without requiring channel state information at the transmitter. Moreover, simulations show that the proposed architecture outperforms traditional MIMO transmission schemes in mmWave channel settings.

This paper aims to study the utilization of non-orthogonal multiple access (NOMA) in hybrid beamforming (HB) multiuser systems, called HB-NOMA, to serve a large number of mobile users. First, a sum-rate expression for the HB-NOMA problem is formulated. Second, an effective algorithm is proposed to maximize the sum-rate. Then, a lower bound is derived under two cases: i) the angle between the effective channel vectors of the MU with the highest channel gain and other MUs located inside a cluster is zero (or close to it) which is denoted by perfect correlation, and ii) this angle is non-zero, which we denote by imperfect correlation. For the second case, the relationship between the effective channels of two HB-NOMA users is modeled. The lower bound for the first case indicates that only the presence of inter-cluster interference and the use of an analog precoder negatively impact the sum-rate. However, in the case of imperfect correlation, which is more realistic, an inefficient MU cluster, can cause severe intra-cluster interference in the network. To verify our findings, numerical simulations have been conducted.