Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems

In this paper, to maximize the energy efficiency (EE) in the two-hop multi-relay cooperative decoding and forwarding (DF) system for simultaneous wireless information and power transmission (SWIPT), an optimal power allocation algorithm is proposed, in which the relay energy harvesting (EH) adopts a nonlinear model. Under the constraints, including energy causality, the minimum transmission quality of information and the total transmission power at the relays, an optimization problem is constructed to jointly optimize the transmit power and power-splitting (PS) ratios of multiple relays. Although this problem is a nonlinear fractional programming problem, an iterative algorithm is developed to obtain the optimal power allocation. In particular, the joint power allocation at multiple relays is first decoupled into a single relay power allocation, and then single-relay power allocation is performed by the Dinkelbach iteration algorithm, which can be proven that it is a convex programming problem. Its closed form solutions for different polylines of EH models are obtained by using mathematical methods, such as monotonicity, Lagrange multipliers, the KKT condition and the Cardan formula. The simulation results show the superiority of the power allocation algorithm proposed in this paper in terms of EE.

A Deep Learning-Assisted Cooperative Diversity Method under Channel Aging

Single-relay selection is a simple but efficient scheme for cooperative diversity among multiple user devices. However, the wrong selection of the best relay due to aged channel state information (CSI) remarkably degrades its performance, overwhelming this cooperative gain. Multi-relay selection is robust against channel aging but multiple timing offset (MTO) and multiple carrier frequency offset (MCFO) among spatially-distributed relays hinder its implementation in practical systems. In this paper, therefore, we propose a deep learning-based cooperative diversity method coined predictive relay selection (PRS) that chooses a single relay with the largest predicted CSI, which can alleviate the effect of channel aging while avoiding MTO and MCFO. Performance is evaluated analytically and numerically, revealing that PRS clearly outperforms the existing schemes with a negligible complexity burden.

A Deep Learning-Assisted Cooperative Diversity Method under Channel Aging

Single-relay selection is a simple but efficient scheme for cooperative diversity among multiple user devices. However, the wrong selection of the best relay due to aged channel state information (CSI) remarkably degrades its performance, overwhelming this cooperative gain. Multi-relay selection is robust against channel aging but multiple timing offset (MTO) and multiple carrier frequency offset (MCFO) among spatially-distributed relays hinder its implementation in practical systems. In this paper, therefore, we propose a deep learning-based cooperative diversity method coined predictive relay selection (PRS) that chooses a single relay with the largest predicted CSI, which can alleviate the effect of channel aging while avoiding MTO and MCFO. Performance is evaluated analytically and numerically, revealing that PRS clearly outperforms the existing schemes with a negligible complexity burden.

Bit Error Rate Performance Analysis of a Multiple Amplify and Forward Relaying Aided Cooperative MIMO-OFDM System

In wireless systems the orthogonal frequency division multiplexing (OFDM) is one of the most influential means of transmission techniques. Data channel having higher speed are converted into some parallel channels by OFDM. For better performance of wireless network, cooperative communication using multiple relay is widely used. Substantial improvement can be achieved in case of reliability and throughput for wireless network if multiple relays are implemented between the source and destination by developing cooperative communication with multiple input multiple output (MIMO) OFDM. A mathematical model has been developed based on amplify and forward (AF) multiple relay for the system of cooperative MIMO-OFDM. Along with the detection system based on minimum mean squared error, the cooperative MIMO-OFDM, with multiple relays, can provide better performance than transmission system using single relay, which can be found from analyzing simulation. A considerable amount of deterioration was also noticed in the color image with changing the number of relays. Signal salvation is better for multiple relay than single relay.