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In this paper, an intelligent reflecting surface (IRS) assisted spectrum sharing underlay cognitive radio (CR) wiretap channel (WTC) is studied, and we aim at enhancing the secrecy rate of secondary user in this channel subject to total power constraint at secondary transmitter (ST), interference power constraint (IPC) at primary receiver (PR) as well as unit modulus constraint at IRS. Due to extra IPC and eavesdropper (Eve) are considered, all the existing solutions for enhancing secrecy rate of IRS-assisted non-CR WTC as well as enhancing transmission rate in IRS-assisted CR channel without eavesdropper fail in this work. Therefore, we propose new numerical solutions to optimize the secrecy rate of this channel under full primary, secondary users’ channel state information (CSI) and three different cases of Eve’s CSI: full CSI, imperfect CSI with bounded estimation error, and no CSI. To solve the difficult non-convex optimization problem, an efficient alternating optimization (AO) algorithm is proposed to jointly optimize the beamformer at ST and phase shift coefficients at IRS. In particular, when optimizing the phase shift coefficients during each iteration of AO, a Dinkelbach based solution in combination with successive approximation and penalty based solution is proposed under full CSI and a penalty convex-concave procedure solution is proposed under imperfect Eve’s CSI. For no Eve’s CSI case, artificial noise (AN) aided approach is adopted to help enhancing the secrecy rate. Simulation results show that our proposed solutions for the IRS-assisted design greatly enhance the secrecy performance compared with the existing numerical solutions with and without IRS under full and imperfect Eve’s CSI. And positive secrecy rate can be achieved by our proposed AN aided approach given most channel realizations under no Eve’s CSI case so that secure communication also can be guaranteed. All of the proposed AO algorithms are guaranteed to monotonic convergence. [ABSTRACT FROM AUTHOR]

Hardware non-idealities are among the main performance restrictions for upcoming wireless communication systems. Asymmetric hardware distortions (HWD) happen when the impairments of the I/Q branches are correlated or imbalanced, which in turn generate improper additive interference at the receiver side. When the interference is improper, as well as in other interference-limited scenarios, improper Gaussian signaling (IGS) has been shown to provide rate and/or power efficiency benefits. In this paper, we investigate the rate benefits of IGS in a two-user interference channel (IC) with additive asymmetric HWD when interference is treated as noise. We propose two iterative algorithms to optimize the parameters of the improper transmit signals. We first rewrite the rate region as an pseudo-signal-to-interference-plus-noise-ratio (PSINR) region and employ majorization minimization and fractional programming to find a suboptimal solution for the achievable user rates. Then, we propose a simplified algorithm based on a separate optimization of the powers and complementary variances of the users, which exhibits lower computational complexity. We show that IGS can improve the performance of the two-user IC with additive HWD. Our proposed algorithms outperform proper Gaussian signaling and competing IGS algorithms in the literature that do not consider asymmetric HWD. [ABSTRACT FROM AUTHOR]

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a cooperative diffusion-based molecular communication (MC) network consisting of single source, single decode-and-forward relay, and single destination. The source and the relay nodes use different types of molecules for information transmission. Considering both the relay and the direct links at the destination node, we have two different received signals that can be used by the destination node to retrieve the data transmitted by the source node. Therefore, diversity arises at the destination node. For detection, we adopt the energy detector applying the diversity combining technique. Since the optimum relay position in relay-aided networks plays a crucial role to enhance their performance, we study the relay location optimization problem. We first derive a closed-form expression for the bit error probability of the proposed detection scheme in which the received energy from the source and the relay nodes are linearly combined at the destination node. Then, in order to find the optimum relay position as well as the optimal destination decision threshold for a given number of molecules released by the source and the relay nodes, we formulate a joint optimization problem whose objective is to minimize the bit error probability of the network. To solve this optimization problem, we propose an iterative algorithm based on the block coordinate descent algorithm and study its convergence behavior. Numerical results show that the error performance can be improved by optimizing the position of the relay node. Our analyses help us in designing a reliable cooperative diffusion-based MC network. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes–Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the design of linear precoders for simultaneously wireless information and power transfer (SWIPT) in a multi-input multi-output (MIMO) broadcasting system with discrete input signals. The considered system model consists of one base station (BS), one information receiver (IR), and one energy receiver (ER). The design objective is to maximize the input–output mutual information of the IR subject to the power constraint and the harvested energy requirement for the ER. We derive the structure of the optimal linear precoder by using manifold optimization, and propose an algorithm to find the optimal precoder. Simulation results show that the proposed algorithm can achieve better performance than the time sharing scheme and the Gaussian optimal precoder when Gaussian inputs are replaced by discrete input signals. [ABSTRACT FROM PUBLISHER]

In wireless network resource allocation, the radio power control problems are often solved by fixed point algorithms. Although these algorithms give feasible problem solutions, such solutions often lack notion of problem optimality. This paper reconsiders well-known fixed-point algorithms, such as those with standard and type-II standard interference functions, and investigates the conditions under which they give optimal solutions. The optimality is established by the recently proposed fast-Lipschitz optimization framework. To apply such a framework, the analysis is performed by a logarithmic transformation of variables that gives tractable fast-Lipschitz problems. It is shown how the logarithmic problem constraints are contractive by the standard or type-II standard assumptions on the power control problem, and how sets of cost functions fulfill the fast-Lipschitz qualifying conditions. The analysis on nonmonotonic interference function allows establishing a new qualifying condition for fast-Lipschitz optimization. The results are illustrated by considering power control problems with standard interference function, problems with type-II standard interference functions, and a case of subhomogeneous power control problems. Given the generality of fast-Lipschitz optimization compared to traditional methods for resource allocation, it is concluded that such an optimization may help to determine the optimality of many resource allocation problems in wireless networks. [ABSTRACT FROM AUTHOR]

Optimal signaling for secrecy rate maximization in Gaussian MIMO wiretap channels is considered. While this channel has attracted a significant attention recently and a number of results have been obtained, including the proof of the optimality of Gaussian signalling, an optimal transmit covariance matrix is known for some special cases only and the general case remains an open problem. An iterative custom-made algorithm to find a globally-optimal transmit covariance matrix in the general case is developed in this paper, with guaranteed convergence to a global optimum. While the original optimization problem is not convex and hence difficult to solve, its minimax reformulation can be solved via the convex optimization tools, which is exploited here. The proposed algorithm is based on the barrier method extended to deal with a minimax problem at hand. Its convergence to a global optimum is proved for the general case (degraded or not) and a bound for the optimality gap is given for each step of the barrier method. The performance of the algorithm is demonstrated via numerical examples. In particular, 20 to 40 Newton steps are already sufficient to solve the sufficient optimality conditions with very high precision (up to the machine precision level), even for large systems. Even fewer steps are required if the secrecy capacity is the only quantity of interest. The algorithm can be significantly simplified for the degraded channel case and can also be adopted to include the per-antenna power constraints (instead or in addition to the total power constraint). It also solves the dual problem of minimizing the total power subject to the secrecy rate constraint. [ABSTRACT FROM PUBLISHER]

In this paper we consider orthogonal frequency division multiplexing (OFDM) based cognitive radio systems that operate as secondary users (SUs) in a spectrum overlay approach and study joint spectral shaping and power control subject to specified target signal-to-interference+noise ratio (SINR) as a constrained optimization problem. We discuss necessary and sufficient conditions for the optimal solution of this problem where the spectrum overlay constraints are satisfied and the specified target SINR value is achieved with minimum transmit power. We also present an algorithm that incrementally adapts the OFDM transmitter to reach the optimal solution and study the convergence speed of the proposed algorithm through simulations. [ABSTRACT FROM PUBLISHER]

In this paper, we study the capacity-achieving input covariance matrices for the multiuser multiple-input multiple-output (MIMO) uplink channel under jointly-correlated Rician fading when perfect channel state information (CSI) is known at the receiver, or CSIR while only statistical CSI at the transmitter, or CSIT, is available. The jointly-correlated MIMO channel (or the Weichselberger model) accounts for the correlation at two link ends and is shown to be highly accurate to model real channels. Classically, numerical techniques together with Monte-Carlo methods (named stochastic programming) are used to resolve the problem concerned but at a high computational cost. To tackle this, we derive the asymptotic sum-rate of the multiuser (MU) MIMO uplink channel in the large-system regime where the numbers of antennas at the transmitters and the receiver go to infinity with constant ratios. Several insights are gained from the analytic asymptotic sum-rate expression, based on which an efficient optimization algorithm is further proposed to obtain the capacity-achieving input covariance matrices. Simulation results demonstrate that even for a moderate number of antennas at each link, the new approach provides indistinguishable results as those obtained by the complex stochastic programming approach. [ABSTRACT FROM AUTHOR]