Throughput and Energy Optimization for Future Wireless Networks, beyond 5G

Abstract

Recently, the next generation wireless networks (5G) is integrated by some novel paradigms such as cognitive radio, full-duplex, simultaneous wireless information and power transfer (SWIPT), multi-input multi-output (MIMO) and large-scale antenna arrays, etc. In cognitive radio networks, a secondary user opportunistically utilizes the licensed spectrum unused by a primary user in overlay mode or completely utilizes the licensed spectrum as long as the interference power caused by the secondary transmitter to each primary user is below a prescribed interference threshold in underlay mode. Moreover, with full-duplex technology, a transceiver is able to communicate in both directions over the same frequency channel. Simultaneous wireless information and power transfer (SWIPT), where transmitters can simultaneously provide both data and energy to receivers. In addition, multi-input multi-output systems can exploit spatial multiplexing or beamforming to focus the information or energy beam on wireless receivers. The high performance in large-scale MIMO systems is achieved when the transmitter are equipped with a very large number of antennas. The throughput and energy have key roles in the operation of these novel paradigms. We need to investigate the answer to the fundamental questions: how to use minimum energy while ensuring the throughput, how to achieve the maximum throughput with a given maximum energy. The problems become complicated when there are combinations of some paradigms such as cognitive and SWIPT, cognitive and full-duplex, or SWIPT and MIMO. Therefore, it is very important to study the solutions for optimizing throughput and energy in the above novel paradigms.

First, we consider the throughput maximization of a secondary user (SU) in a realistic cognitive radio (CR) network where the battery suffers from constant energy leakage. We investigate two different CR scenarios where the primary user (PU) switches between idle and active states in a time-slotted manner. In the first scenario, the SU knows the exact state of the PU at the beginning of each time slot, whereas in the second scenario, the SU attempts to detect state of the PU by spectrum sensing. For both scenarios, we determine the maximum throughput of the SU with consideration of battery leakage of the SU and interference constraint of the PU. The optimal solutions of transmitting power and sensing duration are achieved by using golden section search method and a simplified brute-force search method.

Second, we consider throughput maximisation for a secondary user (SU) in a full-duplex cognitive radio network (FD-CRN) when the SU has two separate antennas and a self-interference suppression capability. In the FD-CRN, the SU can simultaneously sense the spectrum throughout the whole time slot and transmit data. They propose algorithms based on brute-force search and particle swarm optimisation methods to help the SU achieve optimal detection thresholds for spectrum sensing in two different FD-CRN scenarios. In the first scenario, the SU individually performs spectrum sensing, whereas in the second scenario the SU’s sensing results are improved by means of cooperative spectrum sensing. Theoretical and simulation results herein show that, for certain values of the system parameters in the above two scenarios, the system under consideration provides much higher throughput than previously proposed systems in conditions of high-transmission power or low signal-to-noise ratio of the primary signal

Next, we study a simultaneous wireless information and power transfer multi-input single-output cognitive radio network in which a multi-antenna secondary transmitter sends data streams to multiple single-antenna secondary receivers (SRs) equipped with a power-splitting (PS) structure for information decoding and energy harvesting in the presence of multiple single-antenna primary users (PUs). First, the max-min fair SRs' harvested energy problem is formulated and solved by combining the tight semidefinite relaxation (SDR)-based solution of the transmit power minimization problem with the bisection search method. Second, the balancing problem examines the tradeoff between the worst-user harvested energy at the SR and the interference power at the PU. The proposed solution for this challenging non-convex problem includes two steps. First, the problem with fixed PS ratios is solved using the SDR technique and the tight solution is proved; then, the approximately optimal PS ratios are found using the particle swarm optimization method. Additionally, the closed-form solutions of transmit power minimization and harvested energy maximization problems are derived for the special case where only one SR and one PU are present. Finally, the numerical results demonstrate the effectiveness of the proposed approaches in comparison with two baseline schemes.

Then, we study a simultaneous wireless information and power transfer (SWIPT) system in which the transmitter not only sends data and energy to many types of wireless users, such as multiple information decoding users, multiple hybrid power-splitting users (i.e., users with a power-splitting structure to receive both information and energy), and multiple energy harvesting users, but also prevents information from being intercepted by a passive eavesdropper. The transmitter is equipped with multiple antennas, whereas all users and the eavesdropper are assumed to be equipped with a single antenna. Since the transmitter does not have any channel state information (CSI) about the eavesdropper, artificial noise (AN) power is maximized to mask information as well as to interfere with the eavesdropper as much as possible. The non-convex optimization problem is formulated to minimize the transmit power satisfying all signal-to-interference-plus-noise (SINR) and harvested energy requirements for all users so that the remaining power for generating AN is maximized. With perfect CSI, a semidefinite relaxation (SDR) technique is applied, and the optimal solution is proven to be tight. With imperfect CSI, SDR and a Gaussian randomization algorithm are proposed to find the suboptimal solution. Finally, numerical performance with respect to the maximum SINR at the eavesdropper is determined by a Monte-Carlo simulation to compare the proposed AN scenario with a no-AN scenario, as well as to compare perfect CSI with imperfect CSI.

After that, the combination of large-scale antenna arrays and simultaneous wireless information and power transfer, which can provide enormous increase of throughput and energy efficiency is a promising key in next generation wireless system (5G). This paper investigates efficient transceiver design to minimize transmit power, subject to users’ required data rates and energy harvesting, in large-scale SWIPT system where the base station utilizes a very large number of antennas for transmitting both data and energy to multiple users equipped with time-switching (TS) or power-splitting (PS) receive structures. We first propose the well-known semidefinite relaxation (SDR) and Gaussian randomization techniques to solve the minimum transmit power problems. However, for these large-scale SWIPT problems, the proposed scheme, which is based on conventional SDR method, is not suitable due to its excessive computation costs, and a consensus alternating direction method of multipliers (ADMM) cannot be directly applied to the case that TS or PS ratios are involved in the optimization problem. Therefore, in the second solution, our first step is to optimize the variables of TS or PS ratios, and to achieve simplified problems. After then, we propose fast algorithms for solving these problems, where the outer loop of sequential parametric convex approximation (SPCA) is combined with the inner loop of ADMM. Numerical simulations show the fast convergence and superiority of the proposed solutions.

Finally, we consider the harvested-energy fairness problem in cognitive multicast systems with simultaneous wireless information and power transfer. In the cognitive multicast system, a cognitive transmitter with multi-antenna sends the same information to cognitive users in the presence of licensed users, and cognitive users can decode information and harvest energy with a power-splitting structure. The harvested-energy fairness problem is formulated and solved by using two proposed algorithms, which are based on semidefinite relaxation (SDR) and sequential parametric convex approximation (SPCA), respectively. At last, the performances of the proposed solutions and baseline schemes are verified by simulation results.