Ultra-dense networks (UDNs) have been proposed to achieve high data rates and energy efficiency, but their performance is limited by the increase of inter-user interference. To overcome this problem, interference alignment (IA) algorithms have been widely researched. However, reported IA techniques neglect the nonlinear distortion (NLD) induced by power amplifiers. Thus, their performance is severely degraded in power-efficient transmissions operating close to the saturation point. Distortion-aware precoding techniques have been studied to reduce NLD in single transmitter scenarios either single-user or multi-user. Nevertheless, its extension to UDNs with multiple transmitters and users is not straightforward. In this work, a novel IA algorithm, named NLD-IA, is proposed to alternatively design the precoding and combining vectors to reduce the interference and NLD by maximizing the sum-rate through the signal-to-interference-plus-noise ratio (SINR). Precoding vectors are obtained via a non-convex optimization problem that models the NLD correlation. An analytical solution based on the Newton method over the complex field is developed to solve this problem with low computational complexity. Simulation results show that the proposed NLD-IA significantly reduces interference and NLD outperforming previous IA algorithms. The proposed method is an attractive solution for UDNs commonly found in the context of Internet of Things applications.

Fully digital (FD) beamforming strategies become prohibitive in massive multiple-input multiple-output (mMIMO) systems due to their hardware complexity. Therefore, hybrid digital-analog beamforming (HB) is considered in millimeter waves (mmWaves) to reduce the number of radio frequency (RF) chains. When the analog stage is implemented with low-resolution phase shifters (PSs), which implies quantized phase constraints, the spectral efficiency (SE) is severely affected. To overcome this degradation, the reported designs in the literature improve performance at the cost of adding more RF chains than data streams. However, this solution reduces the energy-efficiency of the system. This work proposes a novel HB method to achieve a near-optimal FD performance for mMIMO systems with the minimum required number of RF chains. The reduction of RF chains arises as a key procedure to obtain an energy-efficient design. The proposed HB scheme is based on a quantization-aware matrix composition (QMC) to systematically compensate for the residual quantization errors. Furthermore, a quantization strategy, called sum Euclidean quantization (SEQ), is introduced to improve the accuracy of the analog stage. Numerical results reveal that unlike previous reported HB methods, a near-optimal SE can be achieved with our proposal while minimizing the number of RF chains and thus, the hardware cost and power consumption.

Network densification is one of the most promising solutions to address the high data rate demands in 5G and beyond (B5G) wireless networks while ensuring an overall adequate quality of service. In this scenario, most users experience significant interference levels from neighbouring mobile stations (MSs) and access points (APs) making the use of advanced interference management techniques mandatory. Clustered interference alignment (IA) has been widely proposed to manage the interference in densely deployed scenarios with a large number of users. Nonetheless, the setups considered in previous works are still far from the densification levels envisaged for 5G/B5G networks that are considered in this paper. Moreover, prior designs of clustered‐IA systems relied on oversimplified channel models and/or enforced single‐stream transmission. In this paper, we explore an ultradense deployment of small cells (SCs) to provide coverage in 5G/B5G wireless networks. A novel cluster design based on a size‐restricted k‐means algorithm to divide the SCs into different clusters is proposed taking into account path loss and shadowing effects, thus providing a more realistic solution than those available in the current literature. Unlike previous works, this clustering method can also cater for spatial multiplexing scenarios. Also, several design parameters such as the number of transmit antennas, multiplexed data streams, and deployed APs are analyzed in order to identify trade‐offs between performance and complexity. The relationship between density of network elements per area unit and performance is investigated, thus allowing to illustrate that there is an optimal coverage area value over which the network resources should be distributed. Moreover, it is shown that the spectral‐efficiency degradation due to the intercluster interference in ultradense networks (UDNs) points to the need of designing an interference management algorithm that accounts for both intracluster and intercluster interferences. Simulation results provide key insights for the deployment of small cells in interference‐limited dense scenarios.

Energy harvesting (EH) emerges as a novel technology to promote green energy policies. Based on Cognitive Radio (CR) paradigm, nodes are designed to operate with harvested energy from radio frequency signals. CR-EH systems state several strategies based on sensing and access policies to maximize throughput and protect primary users from interference, simultaneously. However, reported solutions do not consider to maximize detection performance to detect spectrum holes which represent a major drawback whenever available energy is not efficiently used. In this concern, this paper addresses optimal sensing policies based on energy harvesting schemes to maximize probability of detection of available spectrum. These novel policies may be incorporated to previous reported solutions to maximize performance. Optimal processing scheduling schemes are proposed for offline and online scenarios based on convex optimization theory, Dynamic Programming (DP) algorithm and heuristic solutions (Constant Power and Greedy policies). Performance of proposed policies are validated by simulations for common detection techniques such as Matched Filter (MF), Quadrature Matched Filter (QMF) and Energy Detector (ED). As a result, it is shown that the best detection scheme theoretically addressed by MF, does not always perform better than the poorest detection scheme, given by the ED, in an energy harvesting scenario.