Fuhui Zhou

Resource Allocation in Wireless Powered Cognitive Radio Networks Based on a Practical Non-Linear Energy Harvesting Model

Wireless powered techniques have been recognized as promising techniques in future wireless communication systems, especially in cognitive radios (CRs) with energy-limit devices. However, most of the existing works focus on CRs with an ideal linear energy harvesting model. In this paper, a wireless powered wideband CR network is considered, and a practical non-linear energy harvesting model is adopted. To maximize the sum throughput of the secondary users, the energy harvesting time, channel allocation, and transmit power are jointly optimized. The closed-form expressions for the optimal transmit power and channel allocation are given. Simulation results show that there is a tradeoff between the harvesting energy and the sum throughput of the secondary users. It is also shown that the performance achieved under the non-linear energy harvesting model may equal to that achieved under the linear energy harvesting model.

Secure Beamforming Designs for Secrecy MIMO SWIPT Systems

In this letter, secure beamforming designs are investigated in a multiple-input multiple-output secrecy channels with simultaneous wireless information and power transfer. In order to achieve fairness among different multiple energy harvesting receivers, the minimum harvested energy is maximized under the secrecy rate requirements. In particular, in order to reduce the computational complexity of the semidefinite programming problem, a successive convex approximation (SCA) iterative algorithm is proposed in the perfect channel state information (CSI) case to obtain a near-optimal rank-one solution. Moreover, the original problem is extended to the imperfect CSI case by incorporating a norm-bounded error model, where an SCA-based iterative algorithm is also proposed. Simulation results reveal that the SCA-based iterative algorithm achieves the same performance as the semidefinite relaxation method with reduced complexity.

Robust Designs of Beamforming and Power Splitting for Distributed Antenna Systems With Wireless Energy Harvesting

In this paper, we investigate a multiuser distributed antenna system with simultaneous wireless information and power transmission under the assumption of imperfect channel state information (CSI). In this system, a distributed antenna port with multiple antennas supports a set of mobile stations that can decode information and harvest energy simultaneously via a power splitter. To design robust transmit beamforming vectors and the power splitting factors in the presence of CSI errors, we maximize the average worst-case signal-to-interference-plus-noise ratio (SINR) while achieving an individual energy harvesting constraint for each mobile station. First, we develop an efficient algorithm to convert the max–min SINR problem to a set of “dual” min–max power balancing problems. Then, motivated by the penalty function method, an iterative algorithm based on semidefinite programming is proposed to achieve a local optimal rank-one solution. Also, to reduce the computational complexity, we present another iterative scheme based on the Lagrangian method and the successive convex approximation technique to yield a suboptimal solution. Simulation results are shown to validate the robustness and effectiveness of the proposed algorithms.

Secure EE maximisation in green CR: guaranteed SC

Physical-layer security from an energy-efficient perspective is of crucial importance in cognitive radio (CR). A CR network is considered where a secondary user (SU) coexists with a primary user in the presence of an eavesdropper and channel fading. Secure energy efficiency (EE) maximisation problems are formulated in secure green CR based on the condition that a minimum secrecy capacity (SC) of a SU is guaranteed. A peak interference power constraint and an average (ATP)/peak transmit power (PTP) constraint are imposed in the SUs Tx. Using fractional programming and the Lagrange dual method, energy-efficient optimal power allocation strategies are proposed to efficiently solve the secure EE maximisation problems. It is shown that the secure EE of the SU achieved under the ATP constraint is higher than that obtained under the PTP constraint. The tradeoff is elucidated between the secure EE and the SC of the SU.

Improved Energy Detection With Laplacian Noise in Cognitive Radio

Contaminated by Laplacian noise, the performance of energy detection (ED) degrades. To address this problem, an improved energy detection (i-ED) is proposed. Instead of the square of the received signal amplitude in the ED, an arbitrary exponent varying from 0 to 2 is adopted in the proposed algorithm. By deriving the expressions of the false alarm probability and detection probability, we propose an approach to determine the optimal exponent for a given cognitive radio system scenario. Both theoretical analysis and simulation results demonstrate that, by selecting the optimal exponent, the proposed i-ED outperforms ED and the absolute-value cumulating detection in terms of detection performance and robustness to the noise uncertainty.

Robust Chance-Constrained Secure Transmission for Cognitive Satellite–Terrestrial Networks

Cognitive satellite–terrestrial networks (CSTNs) have been recognized as a promising network architecture for addressing spectrum scarcity problem in next-generation communication networks. In this paper, we investigate the secure transmission for CSTNs where the terrestrial base station serving as a green interference resource is introduced to enhance the security of the satellite link. Adopting a stochastic model for the channel state information uncertainty, we propose a secure and robust beamforming framework to minimize the transmit power, while satisfying a range of outage (probabilistic) constraints concerning the signal-to-interference-plus-noise ratio (SINR) recorded at the satellite user and the terrestrial user, the leakage-SINR recorded at the eavesdropper, as well as the interference power recorded at the satellite user. The resulting robust optimization problem is highly intractable and the key observation is that the highly intractable probability constraints can be equivalently reformulated as the deterministic versions with Gaussian statistics. In this regard, we develop two robust reformulation methods, namely

Energy Beamforming Design and User Cooperation for Wireless Powered Communication Networks

mathcal{S}

An Adaptive State Assignment Mechanism Based on Joint Data Detection and Channel Estimation on Fading Meteor Channel

-procedure and Bernstein-type inequality restriction techniques, to obtain a safe approximate solution. In the meantime, the computational complexities of the proposed schemes are analyzed. Finally, the effectiveness of the proposed schemes are demonstrated by numerical results with different system parameters.

Max-min fair harvested energy based beamforming designs for MISO SWIPT secrecy system

We investigate user cooperation (UC) for wireless powered communication networks, where two single-antenna users are first wirelessly powered by a multi-antenna hybrid access point (H-AP), and then cooperatively transmit information signals to this H-AP via the harvested energy. To incorporate user fairness in the system performance, we formulate the weighted sum-rate maximization problem for both users to jointly optimize the energy beamforming vector, time allocation, and power allocation. Due to the non-convexity of this problem, we propose two UC schemes by employing a series of efficient variable substitutions and semidefinite programming relaxation to solve this problem. In addition, we show that the solution of the proposed schemes can achieve the global optimum by exploiting the rank-one characteristic of the energy beamforming matrix. Finally, numerical results are provided to validate our proposed schemes, which quantifies the impact of UC schemes by comparing them with the non-cooperative scheme.

Multi-objective resource allocation in NOMA cognitive radios based on a practical non-linear energy harvesting model

Integrating adaptive modulation and coding techniques into time-varying meteor burst channels with joint data detection and channel estimation at the receiver can greatly increase the average throughput of a meteor burst communication system (MBCS). Although the per-survivor processing algorithm (PSPA) based on joint data and channel estimation provides superior performance and robustness for MBCS, it is very difficult to implement it into practice due to the extremely high computational complexity. An adaptive state reduction scheme using a dimension-down PSPA (ADPSP) with few states in the trellis diagram is proposed based on the slow-fading characteristic of the meteor burst channel. It has the advantages of the dimension-down PSPA (D-PSPA) and the adaptive state reduction scheme based on PSPA proposed in our previous work. The scheme is proposed to reduce the computation complexity and memory size for exponentially decaying meteor burst channels and make the maximum likelihood sequence detection available for adaptive data transmission. Based on the estimation of parameters of the meteor burst channel, the adaptive threshold is obtained in order to decrease the computation compleixty of PSPA, where the states close to the correct one in the trellis is selected. Simulation results are presented to validate the theoretical analysis. It is shown that the proposed adaptive state reduction scheme using a D-PSPA can obtain a good tradeoff between the achievable performance and its dynamically computation complexity, as well as provide reliable data transmission for MBCS using adaptive coding and modulation.