Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink

This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed

Physical Layer Service Integration in 5G: Potentials and Challenges

High transmission rate and secure communication have been identified as the key targets that need to be effectively addressed by fifth generation (5G) wireless systems. In this context, the concept of physical-layer security becomes attractive, as it can establish perfect security using only the characteristics of wireless medium. Nonetheless, to further increase the spectral efficiency, an emerging concept, termed physical-layer service integration (PHY-SI), has been recognized as an effective means. Its basic idea is to combine multiple coexisting services, i.e., multicast/broadcast service and confidential service, into one integral service for one-time transmission at the transmitter side. This article first provides a tutorial on typical PHY-SI models. Furthermore, we propose some state-of-the-art solutions to improve the overall performance of PHY-SI in certain important communication scenarios. In particular, we highlight the extension of several concepts borrowed from conventional single-service communications, such as artificial noise (AN), eigenmode transmission etc., to the scenario of PHY-SI. These techniques are shown to be effective in the design of reliable and robust PHY-SI schemes. Finally, several potential research directions are identified for future work.Comment: 12 pages, 7 figure

5G Fixed Wireless Access for Bridging the Rural Digital Divide

Despite the ubiquitous level of mobile and fixed broadband (FB) connectivity that exists for many people today, the availability of high quality FB services in rural communities is generally much lower than in urban communities, which has led to a digital divide. At the same time, rural communities in Canada have a high level of 4G LTE coverage and the mobile digital divide between urban and rural communities is much smaller compared to the FB divide. Traditionally, FB and mobile services were offered over separate technologies by different operators, and evolved separately from one another. However, recently, a convergence between mobile and FB has started to emerge via 4G Fixed Wireless Access (FWA), which has made it possible to take advantage of the high level of cellular coverage in rural communities to offer (limited) FB at lower costs than traditional wired FB. To bridge the digital divide, rural FWA must be able to provide the same end-to-end experience as urban FB. In in this regard, 4G FWA has been inadequate; however, the recent emergence of 5G, which brings new spectrum, a more efficient radio interface, and multi-user massive MIMO, can make a difference. In the first half of this thesis we outline a vision for how 5G could fix the rural connectivity gap by truly enabling FWA in rural regions. We examine new and upcoming improvements to each area of the 5G network architecture and how they can benefit rural users. Despite those advancements, 5G operators will face a number of challenges in planning and operating rural FWA networks. Therefore, we also draw attention to a number of open research challenges that will need to be addressed. In the latter half of this thesis, we study the planning of a rural 5G multi-user massive MIMO FWA TDD system to offer fixed broadband service to homes. Specifically, we aim to determine the user limit, i.e., the maximum number of homes that can simultaneously receive a target minimum bit rate (MBR) on the downlink (DL) and a target MBR on the uplink (UL) given a set of network resources (e.g., bandwidth, power, antennas) and given a radius. To attain that limit, we must understand how resources should be shared between the DL and UL and how user selection (as well as stream selection since both the base-station (BS) and the homes are multi-antenna), precoding and combining, and power distribution should be performed. To simplify the problem, we use block diagonalization and propose a static user grouping strategy that organizes homes into fixed groups in the DL and UL (we use different groups for the two directions); then we develop a simple process to find the user limit by determining the amount of resources required to give groups the MBRs. We study the impact of group sizes and show that smaller groups use more streams and enable more homes to receive the MBRs when using a 3.5~GHz band. We then show how the user limit at different cell radii is impacted by the system bandwidth, the number of antennas at the BS and homes, the BS power, and the DL and UL MBRs. Lastly, we offer insight into how the network could be operated for an arbitrary number of homes

Deep Q-Learning-based Resource Allocation in NOMA Visible Light Communications

Visible light communication (VLC) has been introduced as a key enabler for high-data rate wireless services in future wireless communication networks. In addition to this, it was also demonstrated recently that non-orthogonal multiple access (NOMA) can further improve the spectral efficiency of multi-user VLC systems. In this context and owing to the significantly promising potential of artificial intelligence in wireless communications, the present contribution proposes a deep Q-learning (DQL) framework that aims to optimize the performance of an indoor NOMA-VLC downlink network. In particular, we formulate a joint power allocation and LED transmission angle tuning optimization problem, in order to maximize the average sum rate and the average energy efficiency. The obtained results demonstrate that our algorithm offers a noticeable performance enhancement into the NOMA-VLC systems in terms of average sum rate and average energy efficiency, while maintaining the minimum convergence time, particularly for higher number of users. Furthermore, considering a realistic downlink VLC network setup, the simulation results have shown that our algorithm outperforms the genetic algorithm (GA) and the differential evolution (DE) algorithm in terms of average sum rate, and offers considerably less run-time complexity.publishedVersionPeer reviewe

System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems

This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio frequency (RF) chain to transmit antenna connections can be switched off for energy saving. By explicitly considering the effect of each connection on the required power for baseband and RF signal processing, we describe the total power consumption in a sparsity form of the analog precoding matrix. However, these sparsity terms and sparsity-modulus constraints of the analog precoding make the system energy-efficiency maximization problem non-convex and challenging to solve. To tackle this problem, we first transform it into a subtractive-form weighted sum rate and power problem. A compressed sensing-based re-weighted quadratic-form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints. We then exploit alternating minimization of the mean-squared error to solve the equivalent problem where the digital precoding vectors and the analog precoding matrix are updated sequentially. The energy efficiency upper bound and a heuristic algorithm are also examined for comparison purposes. Numerical results confirm the superior performances of the proposed algorithm over benchmark energy-efficiency hybrid precoding algorithms and heuristic ones.Comment: submitted to TGC