Energy efficiency optimization for MIMO cognitive radio network

Abstract

This paper investigates a secure energy efficiency (SEE) optimization problem in a multiple-input single-output (MISO) underlay cognitive radio (CR) network. In particular, a multi-antenna secondary transmitter (SU-Tx) simultaneously sends secured information and energy to a secondary receiver (SU-Rx) and an energy receiver (ER), respectively, in the presence of a primary receiver (PU-Rx). It is assumed that the SU-Rx, ER and PU-Rx are each equipped with a single antenna. In addition, the SU-Tx should satisfy constraints on maximum interference leakage to the PU-Rx and minimum harvested energy at the ER. In this CR network, we consider the transmit covariance matrix design with the assumption of perfect channel state information (CSI) at the SU-Tx. In addition, it is assumed that the ER is a potential passive eavesdropper due to broadcast nature of wireless transmission. On the other hand, we consider the worst-case scenario that ER's energy harvesting requirement is only satisfied when it performs only energy harvesting without intercepting or eavesdropping information intended for the SU-Rx. We formulate this transmit covariance matrix design as a SEE maximization problem which is a non-convex problem due the non-linear fractional objective function. To realize the solution for this non-convex problem, we utilize the non-linear fractional programming and difference of concave (DC) functions approaches to reformulate into a tractable form. Based on these techniques and the Dinkelbach's method, we propose iterative algorithms to determine the solution for the original SEE maximization problem. Numerical simulation results are provided to demonstrate the performance of the proposed transmit covariance matrix design and convergence of the proposed algorithms. Index Terms-Secure energy efficiency (SEE), energy harvesting , MISO, convex optimization.

Energy Efficiency and User Capacity Optimization of Cognitive MIMO Systems Via the SCMA-Based Nonorthogonal Time Slot Allocation

“…Therefore, we consider introducing MIMO technology into the CR system to improve its performance. In the literature, most of the research on energy efficiency and user capacity of MIMO-assisted CR networks can be divided into two categories: the traditional CR-MIMO network [25][26][27][28][29][30][31][32][33][34][35] and the network combining CR-MIMO with NOMA (CR-MIMO-NOMA) Technology [36][37][38][39][40][41].…”

“…In the traditional CR-MIMO network, spectrum sensing and energy harvesting technology have been the main research directions in recent years [30][31][32][33][34][35], while the EE and user capacity were mainly studied in earlier years [25][26][27][28][29]. The problems of EE optimization of a MIMO-CR system with multiple secondary receivers for the cases of single and multiple data streams was addressed in References [25], respectively.…”

“…In the latter case, a higher EE was achieved, as compared to the former one. In Reference [27], the problems of distributed and centralized energy efficiency optimization in CR-MIMO networks with interfering channels are proposed. Under the constraints of power and interference, the energy efficiency of the MIMO spectrum sharing cognitive radio system is studied in Reference [28].…”

Resource Allocation Based on Deep Neural Networks for Cognitive Radio Networks

“…The sensing time and the transmission power of the cognitive base station (CBS) were jointly optimized to maximize the EE of the SUs. In order to further improve the EE of the secondary network, multiple-input multiple-output (MIMO) techniques were exploited in CRNs and the energy-efficient precoding scheme was designed [8]. In [3], [6]- [8], the average/peak interference power constraint was applied to protect the quality of service (QoS) of the PUs.…”

“…In [3], [6]- [8], the average/peak interference power constraint was applied to protect the quality of service (QoS) of the PUs. Different from the works in [3], [6]- [8], the authors in [9] leveraged the outage probability constraint to protect the QoS of the PUs. It was shown that the EE can be further improved by using this constraint.…”