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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. Index Terms-Energy harvesting, cognitive radio, optimal processing scheduling, offline power allocation policies, online power allocation policies.

Advanced multiple-input multiple-output (MIMO) beamforming techniques are crucial in 5G New Radio (NR) to achieve the expected data rate values. Therefore, the 3rd Generation Partnership Project (3GPP) has proposed a codebook-based MIMO precoding strategy to provide high diversity, array gain, and spatial multiplexing. The main goal is to obtain a tradeoff between performance, signal overhead, and complexity. The precoding matrix is selected from a set of predefined codebooks based on the knowledge that the 5G-NR base station (gNB) acquires about the channel status. In this work, a detailed study of the precoding matrix design is provided following the guidelines reported in the technical specifications 38-211 and 38-214 of the 3GPP. An analysis of the performance in terms of spectral efficiency (SE) achieved by the 5G-NR precoding matrices is illustrated for a single-user MIMO scenario. These results are contrasted against the optimal singular value decomposition (SVD) solution in order to explore the gap between the standardized precoding proposal and the optimal one. Several values of signal-to-noise ratio (SNR) and different antenna array configurations are considered. Moreover, the multiplexing gain for a different number of parallel data streams is evaluated. Numerical results show the SE bounds that can be obtained with the 5G-NR precoding matrices. These insights are of key importance for practical implementation of precoding strategies in 5G-NR systems and beyond.

Digital precoding techniques have been widely applied in multiple-input multiple-output (MIMO) systems to enhance spectral efficiency (SE) which is crucial in 5G New Radio (NR). Therefore, the 3rd Generation Partnership Project (3GPP) has developed codebook-based MIMO precoding strategies to achieve a good trade-off between performance, complexity, and signal overhead. This paper aims to evaluate the performance bounds in SE achieved by the 5G-NR precoding matrices in single-user (SU) and multi-user (MU) MIMO systems, namely Type I and Type II, respectively. The implementation of these codebooks is covered providing a comprehensive guide with a detailed analysis. The performance of the 5G-NR precoder is compared with theoretical precoding techniques such as singular value decomposition (SVD) and block-diagonalization to quantify the margin of improvement of the standardized methods. Several configurations of antenna arrays, number of antenna ports, and parallel data streams are considered for simulations. Moreover, the effect of channel estimation errors on the system performance is analyzed in both SU and MU-MIMO cases. For a realistic framework, the SE values are obtained for a practical deployment based on a clustered delay line (CDL) channel model. These results provide valuable insights for system designers about the implementation and performance of the 5G-NR precoding matrices.

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.