This paper investigates the joint transceiver design for full-duplex (FD) multiple-input multiple-output relay systems where the direct link (DL) and nonlinear successive-interference-cancellation (SIC) detection are considered. The objective is to jointly optimize the source precoder, relay precoder, and receiver such that the resultant symbol-vector error rate (SVER) is minimized. The joint design is challenging since the coexistence of DL and FD relaying will introduce the inter symbol-vector interference. To address this problem, we first propose a novel QR-SIC receiver structure by performing the dual-filtering (DuF) and channel stacking before SIC. Then, the source and relay precoders are optimized for the proposed DuF-based QR-SIC receiver. Compared with the source precoder, optimizing the relay precoder is much more involved due to the complicated objective function. Hence, we transform the original optimization problem into a trace-minimization one, enabling a closed-form solution for the relay precoder. Furthermore, we theoretically show that the proposed design framework can be extended to the system with the DuF-based minimum mean-squared-error SIC receiver. Simulations show that our transceivers are indeed able to significantly improve the overall SVER performance.

Recently, the full-duplex multiple-input multiple-output relay (FD-MIMO-R) has been widely considered a promising way to increase the spectrum efficiency of a cooperative communication network. Under frequency-selective fading channels, an FD-MIMO-R system simultaneously exhibits the self-interference (SI) and inter-symbol interference (ISI). Most existing transceiver designs aim to cancel or mitigate these two interferences by treating them as harmful signals. However, SI and ISI are inherently generated from the desired signal, which implies that further performance improvement is possible if they can be properly exploited. This concept motivates us to propose novel finite-order space-time source and relay filters so that the end-to-end mean-square error can be minimized. Our design is based on the following two phases. First we derive the optimum spectrums of the source and relay filters with a majorization theory and alternating optimization. The finite-order filters are then developed by applying the weighted least-square criterion. Numerical experiments show that our proposed filters are indeed able to significantly improve the system performance in either flat or frequency-selective fading channels.

In this paper, novel finite-order filter designs for source and multiple full-duplex relays (FDRs) are proposed to improve the spectrum efficiency and link reliability of amplify-and-forward cooperative communication networks. In contrast to the single relay, FDRs suffer not only self-interference (SI), but inter-relay interference (IRI). By taking SI and IRI into consideration, this work aims at the joint design of the source filter, relay filters, and the linear minimum squared error (LMMSE) receiver. The finite-order filters are conducted in two stages. In the first stage, the spectrums of the source and FDRs are computed. Since the optimization problem involves the nonlinear equality constraint, we adopt the generic nonlinear equality alternating direction method of multipliers (neADMM) with a damping procedure to find the stationary solution. In the second stage, the filter coefficients are developed to approach the derived spectrums by using a weighted least square criterion. The numerical results justify the validity of the proposed designs. Interestingly, the results show that preserving a certain amount of SI at the FDRs not only reduces the implementation cost but improves the end-to-end signal-to-interference-plus-noise ratio (SINR).

This paper studies the joint common beamforming (CB) and the superposition coding (SC) design for a two-user massive multiple-input single-output (MISO) system with non-orthogonal multiple access (NOMA). Conventional beamforming design has to estimate the uplink channel state information (CSI) of the near and the far users separately. By the channel reciprocity, the CB is then constructed by combining two uplink CSIs together with certain rules. The design needs two training phases to separately estimate the CSI of the two users but combine them subsequently. In this paper, we propose a model adopting common pilots for the near and far users to estimate an uplink combined channel. In this model, only one training phase is required, and the CB is then constructed by the estimated combined channel directly. With the estimated combined channel, the joint CB and SC design is equivalent to optimizing the pilot power allocation and SC factor. However, the associated optimization is not convex. We then adopt the majorization-minimization approach to conduct the optimization problem and numerically find that the solutions meet the optimum points. Simulations verify the effectiveness of our joint design and show the superior performance not only for the transmission rate but also the reduced overhead.

This paper considers the finite-order filter design problem for the source and multiple full-duplex (FD) relays in a cooperative communication system under frequency-selective fading channels. The goal is to optimize the source and relay filters such that the end-to-end signal-to-interference-plus-noise ratio (SINR) of minimum mean-squared error decision-feedback equalizer (MMSE-DFE) can be maximized. The resultant design problem is very difficult since we need to deal with self interference (SI), inter-relay interference (IRI), and inter-symbol interference (ISI) at the same time. Novel designs are then proposed to overcome the difficulty in this work. Transforming the signals into the frequency domain and using some optimization techniques, we first theoretically derive the power spectrum of the source filter and the spectrums of the relay filters. Then, the finite-order design is developed to approach the derived spectrums. Based on the weighted least-square (WLS) criterion, the Steiglitz-McBride method is exploited to obtain the filter coefficients in finite lengths. Numerical results demonstrated that complete removal of SI may not be a good strategy; preserving a certain amount of SI at each relay instead provides better SINR performance.

This paper investigates beamforming and power allocation problems in the dynamic time division duplex (TDD) cellular networks. Based on the dynamic TDD coexistence scheme, the network comprises uplink and downlink networks serving machine-type devices (MTDs) and human-type devices (HTDs), respectively. The design goal is to minimize the total system power consumption by optimizing the transmit/receive beamforming and uplink power under MTD and HTD quality-of-service (QoS) constraints. The resulting optimization problem is challenging to solve because the variables to be designed are tightly coupled in the constraints. By using the uplink-downlink duality (UDD) and alternating optimization (AO) algorithm, we propose a novel algorithm to overcome the difficulty in this work. Numerical results demonstrate the superiority of the proposed algorithm.

To boost the spectral efficiency of cooperative communication systems, full duplex relays (FDRs) have been widely considered due to the concurrent signal transmission and reception at the relay. However, the self-interference (SI) is always a main problem that deteriorates the system performance. Most conventional FDR designs focus on narrowband transmission and treat SI as a harmful signal, consequently aiming to cancel SI as clear as possible. However, the relay transceiver can be inherently modeled as an infinite impulse response (IIR) filter by recognizing SI as a delayed desired signal. Based on this concept, we propose a novel design where the finite-length source, FDR filters, and the linear minimum mean-squared error (MMSE) equalizer are jointly optimized for frequency-selective fading channels. Simulations demonstrate the effectiveness of our design that preserving partial SI indeed enables further performance improvement.

Compared with half-duplex relaying, the full-duplex relay (FDR) system provides higher spectral efficiency due to the concurrent transmission and reception at the relay node. As known, the full-duplex operation will introduce the self-interference (SI) that significantly degrades the system performance. Conventionally, SI is treated as a harmful signal that needs to be removed from the system as completely as possible. The conventional design concept, however, may not be efficient since SI is in fact a delayed version of the desired signal. Specifically, it is possible to have further performance improvement if SI can be exploited appropriately. In this paper, we will investigate the source/relay filter design problem where the source and relay are considered as finite impulse response (FIR) and infinite impulse response (IIR) filters, respectively. The design goal is to optimize the end-to-end performance for the linear minimum mean-square error (MMSE) and nonlinear MMSE decision-feedback equalizers. To reduce the implementation cost, we further propose a finite-order filter design for the source and relay precoders. Simulations demonstrate that our designs outperform the conventional ones.

In this paper, we investigate the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled hierarchical cognitive radio (HCR) where the primary receiver (PR) is allowed to harvest energy when the secondary system (SS) radiates its information to the secondary receiver (SR). The design objective is to maximize the transmission rate of SS provided that the harvested energy and outage probability of the primary system (PS) are guaranteed. The optimization problem, however, is not convex due to the probability-based constraints introduced by the imperfect channel state information (CSI). To obtain the tractable solution, we apply the Bernstein-type inequality and sphere bounding so that the problem can be approximated by convex formulations. Then, the resultant problems can be efficiently solved with CVX tools. Simulation results demonstrate that the designs can effectively improve the system performance under imperfect CSI environments.

This paper considers the precoder design for energy-efficient data transmissions in energy harvesting (EH)-aided device-to-device (D2D) communications underlaid multiple-input multiple-output (MIMO) cellular networks. We aim to maximize the energy efficiency (EE) of the network, defined as the ratio of the system sum rate to the system power consumption, under EH and transmit power constraints for both cellular and D2D users. The considered problem is nonconvex due to the concave-convex and fractional form of the objective. We propose to apply the concave-convex procedure (CCCP) and the Dinkelbach method to find tractable, approximate solutions. Numerical results demonstrate the performance of the proposed method from various perspectives.

This paper investigates the beamforming design for sum-rate maximization in energy harvesting (EH)-aided device-to-device (D2D) communications underlaid cellular networks. In the considered system, each receiving cellular or D2D user performs EH while decoding information from the base station (BS) or the paired transmitting D2D user. The objective is to derive optimal beamforming strategies at the BS and power allocations at transmitting D2D users, such that the network sum rate is maximized under EH and transmit power constraints. The original nonconvex problem is convexified by the semidefinite relaxation technique and a reformulation of the objective function with first-order approximation in each algorithm iteration, and solved by an iterative algorithm, based on the concept of the Frank-Wolfe algorithm. Simulation provides numerical validation of the proposed method from various perspectives.

This paper investigates nonlinear transceiver design for full-duplex multiple-input multiple-output (FD-MIMO) relay systems. A dual-hop amplify-and-forward relaying protocol is considered. At the destination, nonlinear successive-interference-cancellation (SIC) is used for signal detection. The goal is to find the source and relay precoders such that the symbol-vector error rate (SVER) can be minimized. Due to the loop interference (LI), optimizing the relay precoder in FD systems is much more involved. In this paper, we propose novel designs to solve this problem. Starting from the QR-SIC receiver, we theoretically show that the relay precoder can be solved with a closed-form expression even when the system incurs LI. Then, we consider the system with a minimum mean-squared-error SIC receiver, where the relay precoder design entails a different problem formulation and introduces new challenges. We propose a novel iterative method, with closed-form solutions in each iteration, to solve this problem. Simulations show that our designs can significantly improve the SVER performance for FD-MIMO relay systems.

Full-duplex (FD) relaying has been considered an effective scheme to increase the spectral efficiency of multiple- input multiple-output (MIMO) relay systems. As well-known, the main concern for the FD system is the cancellation of loop interference (LI). In this paper, we consider the joint source/relay precoding to mitigate the LI problem in FD-MIMO relay systems. In our system, spatial multiplexing is exploited for the signal transmission, and the QR successive-interference-cancellation (SIC) receiver is adopted at the destination. Linear precoders are considered at the source and relay, and the block error rate is used as the criterion for the precoders design. To facilitate the optimization, we propose using the primal decomposition, translating the original problem into a subproblem and a master problem. In the subproblem, the source precoder is first solved with the geometric mean decomposition (GMD) method. Then, the master problem can be formulated as a convex optimization so that the relay precoder can be solved with Karush-Kuhn-Tucker (KKT) conditions. The proposed precoders have closed-form expressions, facilitating real- world implementation. Simulation results show that the proposed method significantly improves the performance of FD-MIMO relay systems.

Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission scheme in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as generalized SM (GSM), can use the maximum-likelihood (ML) detector jointly detecting the antenna-subset (AS) index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity can be prohibitively high when the dimension of the GSM system is large. In this paper, we propose new methods to solve the problem. The main idea is to split the detection into two stages, one for the AS index and the other for the symbol vector. For the detection of the AS index, we develop two methods, referred to as Gaussian approximation and QR projection. Once the AS index is detected, conventional low-complexity ML detectors can be applied for the detection of the symbol vector. The diversity order for the proposed methods are further analyzed and an enhanced method is also proposed to achieve near-optimum performance. Finally, the proposed methods are extended to conduct soft detection of GSM systems. Simulations show that our methods significantly outperform existing ones while the detection complexity remains similar.

Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission technique in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as SM-MIMO, can use the maximum-likelihood (ML) detector jointly detecting the antenna index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity may become prohibitively high when the dimension of MIMO system or symbol constellation is large. In this paper, we propose a new method to solve the problem. The main idea is to conduct a two-stage detection, antenna index and symbol. We develop a Gaussian approximation method such that the antenna-index can be first reliably detected. An enhanced method is also proposed so that the near-optimum performance can be obtained. Simulations show that our methods outperform existing methods while the detection complexity remains low.

One problem in multiuser multiple-input multiple-output (MU-MIMO) systems is that the transmitted signal from a base station to a user is interfered by the signals to other users. MU-MIMO precoding has been proposed as an effective way to solve the problem. However, most existing precoding methods are mainly designed with zero-forcing (ZF) or minimum mean-square-error (MMSE) criterion and the performance is not optimal. In this paper, we consider a precoding scheme for MU-MIMO systems where maximum-likelihood (ML) detection is used for each user. We propose using a newly developed X-structured precoder which is specifically optimized for the ML detection. In our method, multiuser interference is first mitigated by using a regularized block diagonalization (RBD) technique. Then, an iterative method is used to construct the X-structured precoder so that the performance of the ML detection can be enhanced. Numerical results show that the proposed method can significantly outperform existing precoding methods.

In multiple-input multiple-output (MIMO) transmission, precoding has been considered a promising method to improve the system performance. In general, the precoder design criterion depends on the detector used at the receiver. For the maximum-likelihood (ML) detector, the optimum precoder design criterion is equivalent to maximizing the minimum distance of received signal constellations. Several precoding methods have been developed in the literature. However, most of them use numerical searches to derive the precoders and require table look-ups in realtime applications. In this paper, we propose a simple but effective method to solve the problem. The proposed precoder has a simple closed-form expression and no tables are required to store. Simulation results show that the proposed precoder can provide almost the same performance as existing precoders.

The hybrid of spatial multiplexing and transmit beamforming is a simple scheme to combat channel fading in multiple-input multiple-output (MIMO) transmission. In this scheme, a set of transmit antennas is used to transmit a bit stream and antenna grouping is then required. How to conduct the grouping optimally is the main concern. For maximum-likelihood (ML) receivers, the grouping criterion is equivalent to maximizing the minimum distance of receive signal constellations, referred to as free distance. However, finding the free distance needs an exhaustive search which is not desirable. In the literature, a singular-value-decomposition (SVD) based method was proposed to obtain a lower bound of the free distance. Using the lower bound as the grouping criterion, one can solve the problem much easier. In this paper, we first propose using a QR decomposition (QRD) based method to obtain another lower bound. It can be shown that the lower bound yielded by the QRD is tighter than that by the SVD. We then propose using a basis-transformation method such that the QRD-based lower bound can be further tightened. Simulations show that the basis-transformed QRD-based method can yield near-optimum performance.

Precoding is an effective method to improve the transmission quality in multiple-input multiple-output (MIMO) systems. In a real-world system, the precoder is selected from a codebook, and its index is fed back to the transmitter. For a maximum-likelihood (ML) receiver, the criterion for precoder selection is equivalent to maximizing the minimum distance of the received signal constellation. The derivation of the optimum solution, however, may be of high computational complexity due to the requirement of the exhaustive search. To reduce the computational complexity, a suboptimum solution based on singular value decomposition (SVD) has been proposed in literature. In this paper, we propose using a QR decomposition (QRD) based method for precoder selection. To further improve the system performance, we also propose an enhanced QRD-based selection method. With Givens rotations, the computational complexity of the enhanced QRD-based method can be effectively reduced. Finally, we combine precoding with receive antenna selection, and use the proposed QRD-based methods to solve this joint optimization problem. Simulation results show that the proposed approaches can significantly improve the system performance.

Antenna selection is a simple but effective method to exploit the transmit diversity in multiple-input multiple-output (MIMO) wireless communications. For maximum-likelihood (ML) detectors, the criterion for the selection is to maximize the free distance of the MIMO system. Since the optimum selection is difficult to conduct, a lower bound of the free distance is typically used as the selection criterion instead. The singular-value-decomposition (SVD) based selection criterion is well known in the literature. In this paper, we propose a QR decomposition (QRD) based selection criterion for antenna selection with the ML detector. Using some matrix properties, we theoretically prove that the lower bound achieved with the QRD-based criterion is tighter than that with the SVD-based criterion. We also propose another QRD-based criterion that can further tighten the lower bound. The proposed algorithms can be directly applied to the receive, and joint transmit/receive antenna selection schemes. Simulations show that the performance of the proposed selection criteria can significantly outperform the SVD-based selection criterion.