In this work, we study a full-duplex (FD) cloud radio access network (C-RAN) from the aspects of infrastructure sharing and information secrecy, where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. Furthermore, the communication takes place in the presence of untrusted external receivers, i.e., eavesdropper nodes. The communicated uplink (UL) and downlink (DL) waveforms are quantized in order to comply with the limited capacity of the fronthaul links. In order to provide information secrecy, we propose a novel utilization of the quantization noise shaping in the DL, such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. In this regard, expressions describing the achievable secrecy rates are obtained. An optimization problem for jointly designing the DL and UL quantization and precoding strategies are then formulated, with the purpose of maximizing the overall system weighted sum secrecy rate. Due to the intractability of the formulated problem, an iterative solution is proposed, following the successive inner approximation and semi-definite relaxation frameworks, with convergence to a stationary point. Numerical evaluations indicate a promising gain of the proposed approaches for providing information secrecy against the untrusted infrastructure nodes and/or external eavesdroppers in the context of FD C-RAN communications.

In this work, joint design of fronthaul compression and precoding in cloud radio access networks (C-RANs) is studied for full-duplex (FD) multiple-input multiple-output (MIMO) underlay cognitive radio networks. In this system, multiple secondary uplink (UL) and downlink (DL) users equipped with multiple antennas communicate with a control unit (CU) in the “cloud” through a set of multi-antenna secondary FD radio units (RU) which are connected to the CU through limited capacity links. We address the sum-rate maximization problem subject to UL and DL fronthaul rate constraints at each RU, power constraints at each RU and UL users, and maximum allowed interference at the primary users. Casting this non-convex problem as a difference of convex (DC) problem, an iterative algorithm based on the Majorization Minimization (MM) approach that guarantees convergence to a stationary point is proposed. Numerical results demonstrate the advantage of the proposed algorithm.

We consider optimal linear precoder and decoder designs in a multi-cell multiple-input multiple-output system, where base stations and mobile users are both operating in full-duplex (FD) mode. Existing works on FD cellular systems focus on the maximization of overall throughput, which can result in unfairness between uplink and downlink channels depending on the self-interference power and inter-user interference levels. Therefore, to introduce fairness, in this paper, we consider the transmit and receive beamforming designs that maximize the harmonic-sum of signal-to-interference-plus-noise ratios (SINRs) in the uplink and downlink channels. We propose a low-complexity alternating optimization algorithm which converges to a stationary point. Moreover, in order to address practical system design aspects, we consider the transceiver design that enforces robustness against imperfect channel state information (CSI) while providing fair performance among the users. To this end, we formulate an optimization problem that maximizes the worst case SINR among all users under norm-bounded CSI errors. We devise a low-complexity iterative algorithm based on alternating optimization and semidefinite relaxation techniques. Numerical results verify the advantages of incorporating FD mode into cellular systems, and practical issues, such as CSI uncertainty and fairness performance.

In this paper, we investigate the energy harvesting (EH) technique and accordingly design transceivers for a K link multiple-input multiple-output interference channel. Each link consists of two full-duplex (FD) Internet of Things (IoT) nodes exchanging information simultaneously in a bi-directional communication channel. All the nodes suffer from interference, in particular strong self-interference and inter-node interference, due to operating in FD mode and simultaneous transmission at each link, respectively. Further, we divide the received signal at each node into two parts. While one part of the signal is used for information decoding, the other part is used for EH. We jointly design the transmit and receive beamforming vectors and receiver power splitting ratios by minimizing the total transmission power of the system, subject to both signal-to-interference-plus-noise ratio and EH threshold constraints. Furthermore, the case of multiple-input single-output interference channel is also included for the sake of comparison. We also revisit the above problems for the case when the available channel state information (CSI) at the transmitters is imperfect, where the errors of the CSI are assumed to be norm bounded. Simulation results show that the EH technique can harvest enough energy to support power consumption limited IoT devices by aiding in recharging their respective batteries.

In this paper, we consider a full-duplex (FD) and amplify-and-forward (AF) relay with multiple antennas, where hardware impairments of the FD relay are taken into account. Due to the inter-dependence of the transmit relay power and the residual self-interference in an FD-AF relay, we observe a distortion loop that degrades the system performance when relay dynamic range is not high. In this paper, we analyze the relay function, and an optimization problem is formulated to maximize the signal to distortion-plus-noise ratio under relay and source transmit power constraints. Due to the problem complexity, we propose a gradient-projection-based (GP) algorithm to obtain an optimal solution. Moreover, a non-alternating sub-optimal solution is proposed by assuming a rank-1 relay amplification matrix, and separating the design of the relay process into multiple stages (MuStR1). The proposed MuStR1 method is then enhanced by introducing an alternating update over the optimization variables, denoted as AltMuStR1 algorithm. Numerical simulations show that compared to GP, the proposed (Alt)MuStR1 algorithms significantly reduce the required computational complexity at the expense of a slight performance degradation. Moreover, as the hardware impairments increase, or for a system with a high transmit power, the impact of applying a distortion-aware design is significant.

We consider the beamforming design to maximize the energy-efficiency (EE) of a K link multiple-input multiple-output (MIMO) interference channel, where each link consists of two full-duplex (FD) nodes. We first transform the original fractional form optimization problem into an equivalent subtractive-form optimization problem by exploiting the properties of fractional programming, and then perform a two-layer optimization scheme. In the outer layer, the EE parameter is searched using a simple one-dimensional search, and in the inner layer, the relationship between Weighted-Sum-Rate (WSR) and Weighted Minimum-Mean-Squared-Error (WMMSE) problems is exploited to solve the subtractive form optimization problem. Simulation results show that the EE of FD systems is lower than that of half-duplex (HD) systems, as FD nodes need to overcome self-interference (SI) which leads to high power consumption.

The fifth-generation (5G) wireless communication systems envision the deployment of massive multiple-input multiple-output (MIMO) technology for enhanced network coverage and capacity. With frequency division duplex (FDD) being the prominent mode of operation, addressing the challenges for massive MIMO systems in FDD is extremely important. One such challenge is the downlink channel estimation, which should be accomplished with minimal pilot overhead. To address this challenge, we propose a compressive sensing (CS) algorithm based on alternating direction method of multipliers (ADMM). We demonstrate how our proposed algorithm fully exploits the block sparsity inherent in the spatial correlations of MIMO channels to provide improved performance than the conventional CS based algorithms used for massive MIMO FDD systems.

In this paper we address the linear precoding and decoding design problem for a bidirectional orthogonal-frequency-division-multiplexing (OFDM) communication system, between two multiple-input-multiple-output (MIMO) full-duplex (FD) nodes. The effects of hardware distortions, leading to residual self-interference and inter-carrier leakage, are taken into account. In the first step, the operation of a FD MIMO OFDM transceiver is modeled under the impact of known hardware impairments. An alternating quadratic convex program (AltQCP) is then provided to obtain a minimum-mean-squared-error (MMSE) design for the defined system. The proposed design is then extended to maximize the system sum rate, applying the weighted-MMSE (WMMSE) method. The proposed AltQCP methods result in a monotonic improvement, leading to a necessary convergence to a stationary point. Finally, the performance of the defined system is evaluated under various system conditions, and in comparison to the other approaches in the literature. A significant gain is observed via the application of the proposed method as the hardware inaccuracy, and consequently inter-carrier leakage, increases.

In this paper, we focus on a multi-user multi-cell scenario with full-duplex (FD) base-stations and half-duplex (HD) downlink (DL) and uplink (UL) users, where all nodes are equipped with multiple antennas. Our goal is to design filters for weighted sum rate (WSR) maximization whilst taking into consideration the effect of transmitter and receiver distortion. Since WSR problems are non-convex, we exploit the relationship between rate and mean squared error in order to propose low complexity alternating optimization algorithms, which are guaranteed to converge. While the initial design assumes perfect channel state information (CSI), we also move beyond this assumption and consider WSR problems under imperfect CSI. This is done using two types of error models; the first is a norm-bounded error model, suitable for cases where the CSI error is dominated by quantization issues, and the second is a stochastic error model, suitable for errors that occur during the channel estimation process itself. Results show that rates achieved in FD mode are higher than those achieved by the baseline HD schemes and demonstrate the robust performance of the proposed imperfect CSI designs. In addition, we also extend our original WSR problem to one which maximizes the total DL rate subject to each UL user achieving a desired target rate. This latter design can be used to overcome potential unfairness issues and ensure that all UL users are equally served in every time slot.

We study a full duplex (FD) multiple-input multiple-output (MIMO) cognitive cellular network, in which a secondary base-station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) secondary users (SUs) operating in half-duplex (HD) mode simultaneously. The spectrum is shared between secondary and primary networks, and thus uplink SUs and secondary BS generate interference on primary users (PUs). We assume that the channel state information (CSI) available at the transmitters is imperfect, and the errors of the CSI are assumed to be norm bounded. Under the impact of channel uncertainty, we address the robust minimization of the sum of mean-squared-errors (MSE) of all estimated symbols subject to power constraints at the uplink SUs and secondary BS, and interference constraints projected to each PU. We show that this problem can be cast as a Semidefinite programming (SDP), and joint design of transceiver matrices can be obtained through an iterative algorithm. Numerical results are presented to show the effectiveness and robustness of the proposed robust algorithm.

Energy consumption in wireless communication systems is exponentially increasing due to growing wireless traffic. Combating adverse effects of excessive energy consumption calls for energy- aware system design, leading to a new research paradigm, green communications. To achieve energy awareness in a full-duplex (FD) bi-directional multiple antenna system, in this paper, we study a Quality-of-Service (QoS) problem, where the total transmit power is minimized subject to minimum rate constraints at each node, and propose two algorithms. We first apply a penalty-based method in order to obtain an efficient optimization strategy. Afterwards, in the second algorithm, we generalize the well-known relationship between Weighted-Sum-Rate (WSR) andWeightedMinimum-Mean- Squared-Error (WMMSE) problems, originally used to solve the sum-rate maximization problem, to tackle the sum-power minimization problem.

We consider a single cell multiple-input multiple-output (MIMO) system, where a base-station (BS) operating in full-duplex (FD) mode serves multiple uplink (UL) and downlink (DL) mobile users operating in half-duplex (HD) mode, simultaneously. The self-interference at the BS, and co-channel interference (CCI) from UL users to DL users are both taken into account. We consider the transmit and receive filter design for sum- rate maximization subject to a sum-power constraint at the BS and individual power constraints at each UL user of the system under the limited dynamic range considerations at the transmitters and receivers. By reformulating this non-convex problem as an equivalent component-wise convex optimization problem with the addition of two auxiliary variables, we propose a low complexity algorithm which converges to a stationary point.

We consider a K link multiple-input multiple-output interference channel where each link consists of two full-duplex (FD) nodes. Two transmit beamforming design problems are solved: 1) sum-power minimization problem subject to rate constraints and 2) energy-efficiency maximization problem subject to individual power constraints. To tackle the sum-power minimization problem, we first generalize the well-known relationship between weighted-sum-rate (WSR) and weighted minimum-mean-squared-error (WMMSE) problems, originally used to solve the sum-rate maximization problems, and then propose a low complexity centralized algorithm, which converges to a stationary point. To decrease the exchange of a huge amount of data and excessive signaling traffic among the nodes, a distributed algorithm is also proposed. For the energy-efficiency maximization problem, the original fractional form optimization problem is first transformed into an equivalent subtractive-form optimization problem by exploiting the properties of fractional programming, and then performs a dual-layer optimization scheme. In the outer layer, the energy-efficiency parameter is searched using a simple 1-D search, and in the inner layer, the relationship between WSR and WMMSE is exploited to solve the subtractive form optimization problem. Since the proposed algorithms require perfect channel-state-information, which is difficult to acquire in practice, we also propose a robust design, by taking the imperfect channel knowledge into consideration. It is shown in the simulations that the sum-power achieved in FD mode depends heavily on the transmitter/receiver distortion. Also the energy-efficiency of FD systems is lower than that of half-duplex systems, as FD nodes need to overcome self-interference and increased inter-user interference, which leads to high power consumption.

In this paper, we address the proportional fair (PF) issue of a K link full-duplex (FD) multiple-input multiple-output (MIMO) interference channel, where each link consists of two FD nodes exchanging information simultaneously. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from the nodes in other links due to simultaneous transmission from each link. The PF issue is important for networks with asymmetric topology and/or asymmetric traffic demands. We demonstrate that the proposed algorithm provides a good trade-off between sum achievable rate and rate distribution for asymmetric links, and moreover we show that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline half-duplex (HD) schemes.

In this paper, we focus on the joint subcarrier and power allocation for an orthogonal frequency division multiple access (OFDMA) full-duplex (FD) system with the goal of maximizing the sum-rate subject to power constraints at the base station (BS) and uplink users, and subcarrier constraints. A greedy subcarrier allocation algorithm based on the necessary conditions of the optimization problem and a power allocation algorithm based on the iterative water-filling (IWF) are proposed. A hybrid scheduler that can switch between FD, half-duplex (HD) uplink and HD downlink mode at each time-slot to maximize the sum-rate is presented. Simulation results reveal that the proposed hybrid scheduling switches to FD scheduling at high self-interference cancellation values, and to HD-time-division- duplexing (TDD) scheduling at low self-interference cancellation values, and thus improves the sum-rate over the traditional HD-TDD scheduling.

We consider a K link multiple-input multiple-output (MIMO) interference channel, where each link consists of two full-duplex (FD) nodes exchanging information simultaneously in a bi-directional communication fashion. The nodes in each pair suffer from self-interference due to operating in FD mode, and inter-user interference from other links due to simultaneous transmission at each link. We consider the transmit and receive filter design for weighted sum-rate (WSR) maximization problem subject to sum-power constraint of the system or individual power constraints at each node of the system. Based on the relationship between WSR and weighted minimum-mean-squared-error (WMMSE) problems for FD MIMO interference channels, we propose a low complexity alternating algorithm which converges to a local WSR optimum point. Moreover, we show that the proposed algorithm is not only applicable to FD MIMO interference channels, but also applicable to FD cellular systems in which a base station (BS) operating in FD mode serves multiple uplink (UL) and downlink (DL) users operating in half-duplex (HD) mode, simultaneously. It is shown in simulations that the sum-rate achieved by FD mode is higher than the sum-rate achieved by baseline HD schemes.

We study two scenarios of full-duplex (FD) multiple-input-multiple-output cognitive radio networks: FD cognitive ad hoc networks and FD cognitive cellular networks. In FD cognitive ad hoc networks (also referred as interference channels), each pair of secondary users (SUs) operate in FD mode and communicate with each other within the service range of primary users (PUs). Each SU experiences not only self-interference but also interuser interference from all other SUs, and all SUs generate interference on PUs. We address two optimization problems: one is to minimize the sum of mean-squared errors (MSE) of all estimated symbols, and the other is to minimize the maximum per-SU MSE of estimated symbols, both of which are subject to power constraints at SUs and interference constraints projected to each PU. We show that these problems can be cast as a second-order cone programming, and joint design of transceiver matrices can be obtained through an iterative algorithm. Moreover, we show that the proposed algorithm is not only applicable to interference channels but also to FD cellular systems, in which a base station operating in FD mode simultaneously serves multiple uplink and downlink users, and it is shown to outperform HD scheme significantly.

In this paper, we establish the signal-to-interference-noise-ratio (SINR) duality between multiple access (MAC) and broadcast (BC) multihop amplify-and-forward multiple-input-multiple-output relay systems under an imperfect channel state model, which is a generalization of several previously established MAC-BC duality results. We show that identical SINRs in the MAC and BC systems can be achieved by two approaches. The first one is to use the Hermitian transposed MAC relay amplifying matrices at the relay nodes in the BC system under the same total network transmission power constraint. The second one is to use the scaled and Hermitian transposed MAC relay amplifying matrices in the BC system under the transmission power constraint at each node of the system, where the scaling factors are obtained by swapping the power constraints of the nodes in the MAC system. Moreover, we derive the MAC-BC mean-squared error and achievable sum-rate [or mutual information] duality properties based on the SINR duality. Numerical results show the utility of the duality results established.

We present a time-domain transmit beamforming (TDTB) method for self-interference cancelation (SIC) at the radio frequency (RF) frontend of the receivers on broadband full-duplex MIMO radios. It is shown that the conventional frequency-domain transmit beamforming (FDTB) method along with the orthogonal frequency division multiplexing (OFDM) framework does not generally perform SIC in the prefix region of a transmitted frame. A hardware based test of the TDTB method shows a 50 dB SIC over a bandwidth of 30 MHz.