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For massive multiuser multiple input multiple output (MIMO) (MU-MIMO) systems, pilot contamination (PC) and data interference (DI) are two main factors that affect the accuracy of the superimposed pilot (SP) based channel estimation, provided that the channel coherence time is less than the total number of users. In this paper, to tackle the effect of the PC, we propose a block-diagonal Grassmannian line packing (GLP) approach, in which the specific sequences for different cells are first designed based on the GLP, and then are block-diagonally extended to form the SP matrices for the users in different cells. To also alleviate the effect of DI on the channel estimation, with the designed SP matrices, an iterative channel estimation (ICE) method based on Tikhonov regularization is presented. The impact that the proposed ICE method has on the signal-to-interference-plus-noise ratio with matched filtering receiver is theoretically analyzed. Moreover, the optimal power allocation factor to the SP by maximizing the spectral efficiency (SE) of the target cell is also derived. Numerical results show that the proposed ICE method with the designed SP matrices effectively mitigates the effects of the PC and DI, and hence, improves the accuracy of channel estimation and further the SE of the massive MU-MIMO systems. [ABSTRACT FROM AUTHOR]

A major challenge in designing a spectral co-existence between radar and communication systems is how to simultaneously provide efficient utilization of the shared spectrum while maintaining a reliable performance for both systems. Due to its flexibility in using linear independent waveforms and superiority in terms of target detection and estimation, the collocated multiple-input-multiple-output (MIMO) radars provide a paramount capability in realizing spectrum sharing with communication systems. In this paper, we consider the use of joint transmit and receive beamformers for both the MIMO radar and MIMO communication systems. Specifically, we design a two-tier alternating optimization spectrum sharing framework that is based on interference alignment (IA) approach. The effectiveness of spectrum sharing between radar and communication systems is guaranteed by mitigating the mutual interference signals in the shared spectral band. Subsequently, motivated by the excellent performance provided by IA in different wireless network scenarios, we propose a spectrum sharing framework that consists of a main iterative algorithm with two subalgorithms included within its operation. The first subalgorithm is used to maximize the signal-to-interference-plus-noise ratio of the radar system, whereas the second one is utilized to maximize the average sum-rate of the communication system. The main iterative algorithm is proposed to jointly optimize the transmit and receive beamforming filters of both systems by performing alternating optimization between the two subalgorithms. The convergences of the proposed algorithm and its subalgorithms are numerically verified. Simulation results are provided to confirm the superiority of the proposed framework for both systems. [ABSTRACT FROM AUTHOR]

Recently, multiple concurrent transmission techniques have been proposed for smartly exploiting interference to improve network throughput. This paper considers zero-forcing (ZF) precoding that allows two interfering links to concurrently transmit under certain situations. This paper focuses on identifying appropriate schedule schemes that efficiently utilize the ZF precoding opportunities to improve network throughput. With this motivation, we define novel link models to formulate our concerned problem. We also develop a distributed scheduling algorithm by using belief propagation. Finally, we conduct extensive simulation experiments to evaluate the efficiency of ZF in improving network throughput in multihop wireless networks. Our simulations confirm that intelligently utilizing ZF in multihop networks is very effective in improving throughput. [ABSTRACT FROM PUBLISHER]

Full-duplex (FD) multiple-input multiple-output relaying has been considered an effective scheme to increase the spectral efficiency for wireless communications. As known, the main problem for the FD system is the cancellation of loop interference (LI). In this paper, we propose using the joint source/relay precoding to reduce the influence of LI. Therein, linear precoders are used at the source and relay, while the minimum mean-squared-error receiver is adopted at the destination. The joint precoder design is complicated when spatial multiplexing is exploited for signal transmission. To solve the problem, we propose an iterative method in which the original problem is split into two subproblems. With some matrix properties, we then show that each subproblem can be formulated as a convex optimization. Finally, a closed-form solution can be obtained with the Karush–Kuhn–Tucker conditions. Using a mean-squared-error upper bound, we also propose a low-complexity method to reduce the computational complexity. The proposed precoders have closed-form expressions, which is a great advantage in real-world implementation. Simulation results show that the proposed methods significantly outperform existing ones. [ABSTRACT FROM AUTHOR]

In this paper, we introduce advancements of the conventional blanking nonlinearity (BN) for orthogonal frequency-division multiplexing (OFDM)-based systems, which is referred to in the following as advanced BN. Blanking is a common measure for mitigating impulsive interference that often occurs in wireless communication systems. Although the BN removes impulsive interference reliably, it possesses various drawbacks for OFDM-based systems. In particular, the choice of the blanking threshold (BT), to decide whether a received sample is blanked, is a critical issue. We present an algorithm for determining the optimal BT to maximize the signal-to-noise-and-interference ratio (SINR) after blanking. Another drawback is that the entire received signal is discarded during a blanking interval, despite the fact that only a fraction of the spectrum of the OFDM signal might be affected by interference. We show how blanking can be limited to subcarriers that are actually affected by interference. Further, we show how these measures can be combined and how a priori information obtained in an iterative loop can be incorporated into the proposed scheme. Simulation results incorporating realistic channel and interference models demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In emerging heterogeneous networks (HetNets), a wide range of the underlying performance evaluation problems is related to wireless interference characterization and can be reduced to investigating the distribution of a side length in a random triangle. In this paper, we address the task of calculating the side-length distribution in such a triangle determined by the known distributions of its two other sides and the distribution of the angle between them. No restrictions on the distributions of input random variables (RVs) are imposed except for their statistical independence. Our solution delivers a crucial building block for interference analysis in multitier and multiradio HetNets, including mobile device-to-device communication as one of the most extreme interference-limited cases. [ABSTRACT FROM AUTHOR]

Interference alignment (IA) is a promising transmission technology enabling, essentially, the maximum achievable degrees of freedom (DOF) in $K$-user multiple-input–multiple-output (MIMO) interference channels. The ideal DOF of IA systems have been obtained using independent MIMO channels, which is, usually rarely observed in reality, particularly in indoor environments. Therefore, the data sum rate and symbol error-rate of IA are dramatically degraded in real-world scenarios since the correlation between MIMO channels decreases the signal-to-noise ratio (SNR) of the received signal after alignment. For this reason, an acceptable sum rate of IA in real MIMO-orthogonal frequency-division multiplexing (MIMO-OFDM) interference channels was obtained in the literature by modifying the distance between network nodes and the separation between the antennas within each node to minimize the spatial correlation. In this paper, we propose to apply transmit antenna selection to MIMO-OFDM IA systems either through bulk or per-subcarrier selection, aiming at improving the sum-rate and/or error-rate performance under real-world channel circumstances, while keeping the minimum spatial antenna separation of 0.5 wavelengths within each node. Two selection criteria are considered: maximum sum rate (Max-SR) and minimum error rate (Min-ER). To avoid subcarrier imbalance across the antennas of each user, which is caused by per-subcarrier selection, a constrained per-subcarrier antenna selection is operated. Furthermore, a suboptimal antenna selection algorithm is proposed to reduce the computational complexity of the optimal algorithm. An experimental validation of MIMO-OFDM IA with antenna selection in an indoor wireless network scenario is presented. The experimental results are compared with deterministic channels that are synthesized using hybrid electromagnetic (EM) ray-tracing models. Our performance evaluation shows that the practical feasibility of MIMO-OFDM IA systems is significantly increased by antenna selection in real-world scenarios. [ABSTRACT FROM AUTHOR]

Cognitive radio allows secondary users (SUs) to exploit the underutilized radio spectrum of the primary networks. To fully utilize the primary spectrum and maximize spectral efficiency, overlay and underlay transmissions, which exploit the white and gray spaces, respectively, should be used together. However, for underlay, the SUs need to transmit at low power to avoid causing harmful interference to the primary users (PUs), whereas the PUs will cause high interference to the SUs. Thus, interference mitigation is a major issue in underlay spectrum utilization. In this paper, a hybrid transmission system that exploits both overlay and underlay is proposed using multicarrier code-division multiple access due to its interference rejection and diversity exploitation capabilities. Unlike existing approaches, which separate the use of overlay and underlay spectrum, the proposed schemes utilize the entire spectrum for underlay transmission to minimize the PU interference while using the spectrum holes for overlay transmission to maximize the data rate. Two techniques that operate at full-load and overload scenarios are proposed. The overload system is then extended to the multiuser underlay case to further improve spectrum utilization. [ABSTRACT FROM AUTHOR]

In the presence of perfect channel state information (CSI), the achievable degrees of freedom (DoF) in wireless interference networks can be linearly scaled up with the number of users. Achievability is based on the idea of interference alignment (IA). However, in the presence of imperfect CSI, the sum rate becomes degraded, and full DoF may no longer be achievable. In this paper, we propose novel least squares (LS)- and minimum mean square error (MMSE)-based IA schemes that adaptively design beamformers by relying on the availability of imperfect CSI and knowledge of the channel estimation error variance in advance. Interestingly and unlike the other robust algorithms, the proposed adaptive schemes do not impose extra computational complexity compared to their nonadaptive counterparts. It is shown that the LS-based IA is able to outperform interference leakage minimization algorithms under both perfect and imperfect CSI. Furthermore, we compare the performance of the proposed MMSE-based IA with maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. We show that while under perfect CSI, the MMSE-based IA achieves the same performance as that of Max-SINR, the former outperforms the latter under CSI mismatch. Meanwhile, it is shown that the proposed MMSE-based IA needs less CSI to be available and has less computational complexity compared to Max-SINR. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency (EE) aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation (PA) and transmission-mode adaptation for green IA-based wireless networks. The PA problem for IA is first analyzed; then, we propose a PA scheme that optimizes the EE of IA-based wireless networks. When the SNR is low, the transmitted power of some users may become zero. Thus, the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the EE of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the EE of IA-based wireless networks. [ABSTRACT FROM PUBLISHER]

This paper investigates decode-and-forward (DF) buffer-aided relay selection for underlay cognitive relay networks (CRNs) in the presence of both primary transmitter and receiver. We propose a novel buffer-aided relay selection scheme for the CRN, where the best relay is selected with the highest signal-to-interference ratio (SIR) among all available source-to-relay and relay-to-destination links while keeping the interference to the primary destination within a certain level. A new closed-form expression for the outage probability of the proposed relay selection scheme is obtained. Both simulation and theoretical results are shown to confirm performance advantage over the conventional max–min relay selection scheme, making the proposed scheme attractive for CRNs. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of spectrum leasing in cognitive radio (CR) networks, while incorporating two advanced physical-layer technologies, i.e., multiple-input multiple-output (MIMO) and distributed interference alignment. We present a cooperative spectrum leasing scheme for primary and secondary users to balance the tension between data transmission and revenue collection/payment. A Stackelberg game is formulated, where the primary user is the leader, and secondary users are followers. With backward induction, we derive the optimal strategies for primary and secondary users that can achieve the unique Stackelberg equilibrium, where no player can gain by a unilaterally changing strategy. We find that spectrum leasing is always beneficial to enhancing the utilities of primary and secondary users. The proposed scheme outperforms a no-spectrum-leasing scheme and a cooperative scheme presented in the literature with considerable gains, which demonstrate the benefits of spectrum leasing and distributed interference alignment and validates the efficacy of the proposed scheme. [ABSTRACT FROM PUBLISHER]