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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]

In this correspondence paper, we design a massive nonorthogonal multiple access (NOMA) scheme with delay provisioning for cellular internet-of-things. In the context of massive access, the severe interuser interference decreases the quality of the received signal, such that it is a nontrivial task to provide a delay provisioning for heterogeneous wireless services. To solve this challenge, we propose a massive NOMA scheme to meet the delay requirements of a large number of users by exploiting the properties of rateless codes. Especially, we present a power allocation algorithm to minimize the power consumption while achieving delay-constrained massive access. Finally, simulation results validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

We investigate the performance of multiuser multiple-input-single-output (MU-MISO) downlink in the presence of the mutual coupling effect at the transmitter. Contrary to traditional approaches that aim at eliminating this effect, in this paper we propose a joint analog–digital (AD) beamforming scheme that exploits this effect to further improve the system performance. A jointly optimal AD beamformer is first obtained by iteratively maximizing the minimum received signal-to-interference-plus-noise ratio in the digital domain, followed by an optimization on the load impedance of each antenna element in the analog domain. We further introduce a decoupled low-complexity approach, with which existing closed-form beamforming schemes can also be efficiently applied. For the consideration of hardware imperfections in practice, we study the case where the analog load values are quantized, and propose a sequential search scheme based on greedy algorithm to efficiently obtain the desired quantized load values. Moreover, we also investigate the imperfect channel state information (CSI) scenarios, where we prove the optimality for closed-form beamformers, and further propose the robust schemes for two typical CSI error models. Simulation results show that with the proposed schemes the mutual coupling effect can be exploited to further improve the performance for both perfect CSI and imperfect CSI. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the performance of a dual-hop multiple-input–multiple-output (MIMO) amplify-and-forward (AF) relay network, where the source, relay, and destination are all equipped with multiple antennas. By using maximum ratio transmission (MRT) at the transmitter and maximum ratio combining (MRC) at the receiver, we first obtain the output signal-to-interference-plus-noise ratio (SINR) of the dual-hop AF relay system, considering multiple cochannel interferences (CCIs), as well as noise at the relay. Then, we derive an exact closed-form expression for the outage probability (OP), and the asymptotic result of OP at high SNR, which can be used to calculate the array gain and diversity order. Finally, computer simulations are conducted to validate the performance analysis. Our new analytical expressions not only provide a fast and efficient method to evaluate the system performance but enable us to gain valuable insights into the effects of key parameters on the MIMO AF relaying network performance that benefits from implementing multiple antennas at each of the three nodes as well. [ABSTRACT FROM PUBLISHER]

Multicell coordinated beamforming (CB) can mitigate intercell interference (ICI). However, existing work on CB focuses on systems that maximize overall throughput. This paper considers CB for systems with multiple receive antennas and further extends the CB to multiuser and multistream transmission. We consider fairness and pay close attention to cell-edge users. CB that maximizes the harmonic sum of signal-to-interference-plus-noise ratio (SINR) can be derived into a low-complexity algorithm that favors cell-edge users. Two iterative algorithms are developed. From the simulation results, the proposed algorithms can provide a better fifth-percentile user throughput compared with the CB algorithms minimizing mean square error, maximizing uplink SINR, and maximizing weighted sum rate. Furthermore, the computational complexity of the proposed algorithms is significantly lower than that maximizing the minimum SINR of users. [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]

In this paper, we consider unicast/multicast multicell cooperative transmission with interference among users/user groups. In this context, we derive the optimal minimum variance distortionless response (MVDR)-based successive minimum variance beamforming (SMVB) scheme that achieves a superior sum-rate performance compared with the block diagonalization scheme. Subsequently, we derive an optimal joint power allocation and broadcast beamforming scheme for multicell cooperative broadcast transmission for a pooled power constraint at the base stations. This scheme is extended to include per base station power constraints by formulating it as a relaxed semi-definite program (SDP). We also consider the multicell cooperative uplink scenario and derive a successive multiuser (MU) uplink beamforming (SMUB) scheme that maximizes the signal-to-interference-plus-noise ratio (SINR) of each user. Next, we consider imperfect channel state information (CSI) and derive a robust beamforming scheme for the downlink, based on minimizing the worst-case interference by formulating it as a second-order cone program. It is demonstrated that the corresponding robust beamformer for the uplink transmission can be derived by employing the multidimensional covariance fitting approach. Simulation results are presented to demonstrate the superior sum-rate performance of the proposed multicell transmission schemes. The performance is also compared with several bounds for cooperative multicell scenarios. [ABSTRACT FROM AUTHOR]