Full-Duplex Cooperative Cognitive Radio Networks with Wireless Energy Harvesting

This paper proposes and analyzes a new full-duplex (FD) cooperative cognitive radio network with wireless energy harvesting (EH). We consider that the secondary receiver is equipped with a FD radio and acts as a FD hybrid access point (HAP), which aims to collect information from its associated EH secondary transmitter (ST) and relay the signals. The ST is assumed to be equipped with an EH unit and a rechargeable battery such that it can harvest and accumulate energy from radio frequency (RF) signals transmitted by the primary transmitter (PT) and the HAP. We develop a novel cooperative spectrum sharing (CSS) protocol for the considered system. In the proposed protocol, thanks to its FD capability, the HAP can receive the PT's signals and transmit energy-bearing signals to charge the ST simultaneously, or forward the PT's signals and receive the ST's signals at the same time. We derive analytical expressions for the achievable throughput of both primary and secondary links by characterizing the dynamic charging/discharging behaviors of the ST battery as a finite-state Markov chain. We present numerical results to validate our theoretical analysis and demonstrate the merits of the proposed protocol over its non-cooperative counterpart.Comment: 6 pages, 3 figures, conferenc

Beamforming optimization for full-duplex cooperative cognitive radio networks

This paper considers the problem of beamforming optimization in a cognitive cooperative energy harvesting network, in which the secondary transmitter (ST) harvests energy from the primary transmitter (PT) and relays the information for the primary user (PU) with amplify-and-forward (AF) relay protocol. When the channel of the primary system is affected with deep fading or shadowing effects, the ST can assist the primary information transmission. It is particularly useful to employ the energy harvesting protocol to avoid that the ST does not have enough energy to assist the PU. Based on the self-energy recycling relay protocol, we study the beamforming optimization problem. We develop a semidefinite programming (SDP) relaxation method to solve the proposed problem. We also use SDP and one-dimension (1-D) optimization to solve the beamforming optimization based on a time-switching relaying protocol (TSR) as a benchmark. The simulation results are presented to verify that the self-energy recycling protocol achieves a significant rate gain compared to the TSR protocol and the power-splitting relaying (PSR) protocol

Transceiver design for wireless energy harvesting cooperative networks

In this thesis, the RF energy harvesting technique is studied in the cooperative wireless network, and different optimization studies are investigated. First, an energy-efficient optimization is considered in the cooperative system with the time switching relaying and power splitting relaying protocols. Then, a security issue in the cooperative network with energy harvesting is proposed, in which the optimization problem is designed to maximize the secrecy rate. We also consider the application of energy harvesting in the full-duplex relaying network with a self-energy recycling protocol. Finally, the energy harvesting is studied in the full-duplex cooperative cognitive radio network. The system performance of all studies is verified in the thesis with MATLAB simulation results

Resource allocation and secure communication design in simultaneous wireless information and power transfer systems

Radio frequency (RF) energy transfer techniques have been regarded as the key enabling solutions to supply continuous and stable energy for the energy-constrained wireless devices. Simultaneous wireless information and power transfer (SWIPT) has been developed as a more promising RF energy transfer technique since it enables wireless information and wireless energy to access users from a same transmitted signal. Therefore, SWIPT has received remarkable attention. This thesis provides an investigation on applications and security issues of this emerging technology in various wireless communication scenarios. First, this thesis examines the application of SWIPT to a multi-user cooperative network in which the amplify-and-forward (AF) relay protocol is employed at the multi-antenna relay. A power splitting (PS) receiver architecture is utilized at each destination node to implement energy harvesting (EH) and information decoding (ID) simultaneously. The aim of this chapter is to minimize the relay transmit power by jointly designing relay beamforming vectors and PS ratios based on channel uncertainty models. The non-convex problem is converted into a semidefinite programming (SDP) problem by using the semidefinite relaxation (SDR) approach. In addition, a rank-one proof presents that the solution generated by the relaxed problem is optimal to the original problem. Second, a security issue about the SWIPT system is investigated in a cooperative network in the presence of potential eavesdroppers. The AF relay protocol and a PS receiver architecture are adopted at the multi-antenna relay and the desired destination node, respectively. Based on the system setup and the assumption of perfect channel state information (CSI), a transmit power minimization problem combined with the secrecy rate and harvested energy constraints is proposed to jointly optimize the beamforming vector and the PS ratio. The proposed optimization problem is non-convex and hard to tackle due to the issues of the quadratic terms and the coupled variables. To deal with this non-convex problem, two algorithms are proposed. In the first algorithm case, the proposed problem can be globally solved by using a two-level optimization approach which involves the SDR method and the one-dimensional (1-D) line search method. In addition, a rank reduction theorem is introduced to guarantee the tightness of the relaxation of the proposed scheme. In the second algorithm case, the proposed problem can be locally solved by exploiting a low complexity iterative algorithm which is embedded in the sequential parametric convex approximation (SPCA) method. Furthermore, the proposed optimization problem is extended to the imperfect CSI case. Third, a secure communication case is studied in an underlay multiple-input multiple-output (MIMO) cognitive radio (CR) network where the secondary transmitter (ST) provides SWIPT to receivers. In this chapter, two uncertainty channel models are proposed. One is based on the assumption that the ST has the perfect channel knowledge of the secondary information receiver (SIR) and the imperfect channel knowledge of secondary energy receivers (SERs) and primary receivers (PUs). The other one assumes that the ST only has the imperfect channel knowledge of all receivers. In each uncertainty channel model, an outage-constrained secrecy rate maximization (OC-SRM) problem combined with probability constraints is proposed to jointly optimizing the transmit covariance matrix and the artificial noise (AN)- aided covariance matrix. The designed OC-SRM problem for both models is non-convex due to the unsolvable probabilistic constraints. To solve this non-convex problem, the log determinant functions are first approximated to the easy handle the functions that the channel error terms are included in the trace function. Then, the probability constraints are converted into the deterministic constraints by exploiting the Bernstein-type inequality (BTI) approach. Finally, the reformulated problem for both models is solvable by using the existing convex tools. Last, a novel security issue is investigated in a MIMO-SWIPT downlink network where nonlinear energy receivers (ERs) are considered as the potential eavesdroppers. In this chapter, two uncertainty channel models, namely partial channel uncertainty (PCU) and full channel uncertainty (FCU), are proposed. An OC-SRM problem of each model is proposed to design the transmit signal covariance matrix while satisfying probabilistic constraints of the secrecy rate and the harvested energy. To surmount the non-convexity of the proposed OC-SRM problem in each model, several transformations and approximations are utilized. In the PCU model, the OC-SRM problem is first converted into two subproblems by introducing auxiliary variables. Then, three conservative approaches are adopted to obtain the safe approximation expressions of the probabilistic constraints, which are deterministic constraints. Moreover, an alternating optimization (AO) algorithm is proposed to iteratively solve two convex conic subproblems. In the FCU model, log determinant functions are first approximated to the trace functions. Then, the three approaches aforementioned are employed to convert probabilistic constraints into deterministic ones. The bisection method is utilized to solve the reformulated problem. Finally, the computational complexity of the proposed three approaches based on the PCU and FCU model is analyzed

Reliable and Efficient Cognitive Radio Communications Using Directional Antennas

Cognitive Radio (CR) is a promising solution that enhances spectrum utilization by allowing an unlicensed or Secondary User (SU) to access licensed bands in a such way that its imposed interference on a license holder Primary User (PU) is limited, and hence fills the spectrum holes in time and/or frequency domains. Resource allocation, which involves scheduling of available time and transmit power, represents a crucial problem for the performance evaluation of CR systems. In this dissertation, we study the spectral efficiency maximization problem in an opportunistic CR system. Specifically, in the first part of the dissertation, we consider an opportunistic CR system where the SU transmitter (SUtx) is equipped to a Reconfigurable Antenna (RA). RA, with the capabilities of dynamically modifying their characteristics can improve the spectral efficiency, via beam steering and utilizing the spectrum white spaces in spatial (angular) domain. In our opportunistic CR system, SUtx relies on the beam steering capability of RA to detect the direction of PU\u27s activity and also to select the strongest beam for data transmission to SU receiver (SUrx). We study the combined effects of spectrum sensing error and channel training error as well as the beam detection error and beam selection error on the achievable rates of an opportunistic CR system with a RA at SUtx. We also find the best duration for spectrum sensing and channel training as well as the best transmit power at SUtx such that the throughput of our CR system is maximized subject to the Average Transmit Power Constraint (ATPC) and Average Interference Constraint (AIC). In the second part of the dissertation, we consider an opportunistic Energy Harvesting (EH)-enabled CR network, consisting of multiple SUs and an Access Point (AP), that can access a wideband spectrum licensed to a primary network. Assuming that each SU is equipped with a finite size rechargeable battery, we study how the achievable sum-rate of SUs is impacted by the combined effects of spectrum sensing error and imperfect Channel State Information (CSI) of SUs–AP links. We also design an energy management strategy that maximizes the achievable sum-rate of SUs, subject to a constraint on the average interference that SUs can impose on the PU

Full-duplex cooperative cognitive radio networks

In this paper, we study the impact of a full-duplex secondary node on a cognitive cooperative network with Multipacket Reception (MPR) capabilities at the receivers. Motivated by recent schemes that make full-duplex communication feasible, we study a model with one primary and one secondary transmitter-receiver pair, where the secondary transmitter is able to relay primary unsuccessful packets. Cooperation between primary and secondary users has been previously shown to be beneficial for the primary and the secondary users in terms of stable throughput. Our model assumes an imperfect full-duplex secondary node that can transmit and receive simultaneously, cancelling self-interference to a certain extent. Furthermore, we assume that the secondary transmitter chooses between cooperating with the primary user and transmitting secondary packets probabilistically according to some optimized probabilities that depend on both the channels in the network and the state of the primary user. We determine these probabilities by formulating a constrained optimization problem with the secondary throughput as the objective function and the stability of the primary queues as constraints. Using the dominant system approach, we show that the optimization problem has a quasi-concave structure, to which the optimal solution can be easily found. Using Numerical results, we characterize the cases where the full-duplex capability is beneficial to the system, namely, we show that the full-duplex secondary node greatly increases both the secondary throughput and the primary maximum stable throughput in channels with receivers that have strong MPR capability