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Relay selection for Relay Assisted (RA) networks is an economical and effective method to improve the spectrum efficiency. Relay selection performs especially well when the source node has accurate and timely Channel State Information (CSI). However, since perfect CSI knowledge is rarely available, research of relay selection with partial (statistical) CSI is of paramount importance. In this paper, relay selection for RA networks with statistical CSI is formulated as a Multiple-Decision (MD) problem. And, the cost of obtaining the CSI is also considered in the formulated problem. Two relay selection schemes, Maximal Selection Probability (MSP) and Maximal Spectrum Efficiency Expectation (MSEE), are proposed to solve the formulated MD problem under different optimal criteria assumptions based on the optimal stopping theory. The MSP scheme maximizes the probability that the Best Assisted Relay Candidate (BARC) can be selected, whereas the MSEE scheme provides the maximal expectation of the spectrum efficiency. Experimental results show that the proposed schemes effectively improve the spectrum efficiency, and the MSEE scheme is more suitable for stable communication cases. Meanwhile, the MSP scheme is more suitable for burst communication cases.

This paper studies the secure transmit design for a downlink multiple-input-single-output cognitive radio network (CRN) with a practical energy harvesting (EH) model. Due to the open architecture of a CRN, the confidential information intended for the secondary receiver is prone to be eavesdropped by external eavesdroppers (Eves). Specifically, considering imperfect channel state information, our aim is to jointly design the transmit beamforming vector and the power splitting ratio, such that the outage-constrained secrecy rate is maximized under the constraints of total power consumption, secrecy rate outage probability, EH and quality of service requirements. The original problem is not convex and intractable in general, in order to tackle this non-convexity issue, we utilize semidefinite relaxation and Bernstein-type inequality to transform the outage constraints into a deterministic form. We show that the optimal solution can be obtained by alternately optimizing two convex subproblems. Also, we prove that the optimal solution of the relaxed problem will always be rank one. Furthermore, we consider a large deviation inequality (LDI)-based approach to obtain a sub-optimal solution, which can significantly reduce the computational complexity. Finally, simulation results have been provided to demonstrate the performance of our proposed designs.

Relay assisted (RA) network with energy harvesting (EH) is a promising method to realize the green communication. Meanwhile, the relay selection (RS) is an effective method to exploit the performance advantage of EHRA network. However, the performance of RS depends on the perfect channel state information (CSI) deeply. And, there is an obvious performance gap between the RS strategy with perfect CSI and partial CSI. In order to improve the performance of EH-RA network with partial CSI, a RS strategy is proposed to optimize the spectrum efficiency (SE) under an EH constraint in this paper. The upper bounder (UB) of the proposed RS strategy is also analyzed under the perfect CSI assumption. Moreover, the statistical dynamic program (SDP) is investigated to cooperate with the proposed RS strategy by optimizing threshold under the partial CSI assumption. Numerical results show that the SE performance of the proposed RS strategy with SDP is much better than the greedy algorithm and approaches to the performance UB effectively.

Energy harvesting techniques are promising in next generation wireless communication systems. However, most of the existing works are based on an ideal linear energy harvesting model. In this paper, a multiple-input single-output cognitive radio network is studied under a practical non-linear energy harvesting model. In order to improve the security of both the primary network and the secondary network, a cooperative jamming scheme is proposed. A robust artificial noise aided beamforming design problem is formulated under the bounded channel state information error model. The formulated problem is non-convex and challenging to be solved. Using ${\mathcal{ S}}\text {-Procedure} $ and the semidefinite relaxation method, a suboptimal beamforming can be obtained. Simulation results show that the performance achieved under the non-linear energy harvesting model may be better than that obtained under the linear energy harvesting model. It is also shown that the cooperation between the primary network and the secondary network can obtain a performance gain compared with that without this cooperation.

Relay selection is a practical and effective way to improve the spectrum efficiency. However, most of the existing research about the relay selection is developed under the perfect instance channel state information (CSI) assumption. This premise limited the real application of relay selection. Fortunately, with the development of optimal stopping (OS) theory, we have a new way to overcome this shortage. In this paper, a polling relay selection strategy (PRSS) for the amplify-and-forward (AF) relay assisted (RA) network is proposed and analyzed. And, the OS theory is also investigated to cooperate with PRSS under the statistical CSI assumption. The simulation results show that PRSS with OS theory can improve the performance of AF RA network effectively.

Relay assisted (RA) network and the assisted relay (AR) selection is considered as an efficient technology to improve the communication performance since it is easy to use and do not require extra hardware cost. However, most of the previous research did not consider the imperfect state information of the AR candidate sufficiently since it is hard to be formulated. This shortage limited the application of the existing research in practice. In order to solve this problem, an AR selective (ARS) strategy is proposed and analyzed. Moreover, a threshold determination algorithm based on the stochastic dynamic program (SDP) is also developed to make the ARS strategy more practical. Numerical results show that performance of ARS strategy far superior to the performance without AR.

Relay assisted (RA) network with the assisted relay selection is a kind of effective method to improve the channel capacity and converge performance. However, most of the existing research about the assisted relay selection did not consider the statical channel information and the selective cost. This shortage limited the performance and application of RA network in practical scenario. In order to overcome this drawback, a sequential assisted relay selective scheme (ARSS) is proposed. And the performance upper bound (UB) of ARSS is also analyzed. Furthermore, in order to make ARSS more practical, a determination algorithm of the assisted relay selective threshold based on the stochastic dynamic program (SDP) is given to work with ARSS. Numerical results indicate that the performance of ARSS with SDP is close to the performance UB of ARSS effectively.

Relay selection is a crucial method that can improve the spectrum efficiency and converge performance of relay assisted (RA) network. However, most of the existing research about relay selection did not consider the effect of imperfect channel state information (CSI) and the CSI obtaining cost. Few of the study about the relay selection with imperfect CSI focused mainly on the decode-and-forward (DF) protocol. In order to overcome these shortage, a sequence relay selection strategy (SRSS) for the amplify-and-forward (AF) RA network is proposed and analyzed in this paper. Furthermore, in order to make SRSS more practical, the optimal stopping (OS) theory is investigated to work with SRSS. Numerical results indicate that the performance of SRSS with OS theory can achieve the upper bound performance of SRSS effectively.

Multiple input multiple output (MIMO) and pre-coder are the effective methods to overcome the influence of the time-varying channel since it is a key factor of the communication performance. However, the existing researches of MIMO and pre-coder focus on the spectrum efficiency under the Gaussian input assumption. This drawback limits the pre-coder technology application in the green communication. In order to overcome this shortage, we studies the energy efficiency of MIMO system in the time-varying channel based on the time-delay ability of the communication service under the equal probability finite-alphabet input assumption which is more likely to the digital communication signal. Furthermore, a feasible pre-coder algorithm based on the stochastic dynamic program is proposed. The numerical results show that the proposed algorithm performance approaches the energy efficient up bounder of the MIMO system effectively.

Relay assisted (RA) network is a kind of effective method to improve the energy efficiency. However, most of existing research is developed under the Gaussian input assumption and few concerned the equipment probability finite alphabet input assumption which is more coincident with the widely adopted digital signal. This shortage limited the application of RA network in practical scenarios. For this reason, we formulate an energy efficient (EE) problem of RA network with amplify-and-forward (AF) protocol and develop a sub-optimal power allocation algorithm which is named constant proportion power allocation (CPPA) to solve it. Numerical results are also given to examine the performance of CPPA.

Relay-assisted (RA) network with relay node selection is a kind of effective method to improve the channel capacity and convergence performance. However, most of the existing researches about the relay selection did not consider the statically channel state information and the selection cost. This shortage limited the performance and application of RA network in practical scenarios. In order to overcome this drawback, a sequence relay selection strategy (SRSS) was proposed. And the performance upper bound of SRSS was also analyzed in this paper. Furthermore, in order to make SRSS more practical, a novel threshold determination algorithm based on the stochastic dynamic program (SDP) was given to work with SRSS. Numerical results are also presented to exhibit the performance of SRSS with SDP.