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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]

With interference alignment (IA), the achievable degrees of freedom (DoFs) in wireless networks can be linearly scaled up with the number of users. However, to attain the full DoF, the availability of perfect network channel state information (CSI) is mandatory, which is not pragmatic in general. In this paper, we quantify the performance of IA under CSI mismatch where the variance of the CSI measurement error depends on the signal-to-noise ratio (SNR). We show that when this error variance is proportional to the inverse of the SNR, the full DoF is achievable, and an upper bound on asymptotic mean loss in sum rate compared with the perfect CSI case is derived. We also derive a bound on the achievable DoF when the CSI error variance is proportional to the inverse of the SNR to a power of a constant. Furthermore, we investigate the effect of CSI mismatch on the performance of the maximum signal-to-interference-plus-noise ratio (Max-SINR) algorithm. We show that with perfect CSI, the Max-SINR algorithm outperforms interference leakage minimization algorithms, but with the presence of imperfect CSI, its comparative improvement becomes negligible. We then propose an adaptive Max-SINR algorithm that can notably improve the performance of the original Max-SINR algorithm under CSI mismatch. [ABSTRACT FROM AUTHOR]

Interference alignment (IA) is a promising technique for interference management and can be applied to spectrum sharing in cognitive radio (CR) networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR), and the quality of service (QoS) of the primary user (PU) may not be guaranteed. In addition, power allocation (PA) in IA-based CR networks is largely ignored, which can further improve its performance. Thus, in this paper, PA in IA-based CR networks is studied. To guarantee the QoS requirement of the PU, its minimal transmitted power is derived. Then, we propose three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency (EE) of the network, and the requirements of SUs, respectively, while guaranteeing the QoS of the PU. To reduce the complexity, the closed-form solutions of these algorithms are further studied in detail. The outage probability of the PU according to its rate threshold is also derived to analyze the performance of these algorithms. Moreover, we propose a transmission-mode adaptation scheme to further improve the PU's performance when its QoS requirement cannot be guaranteed at low SNR, and it can be easily combined with the proposed PA algorithms. Simulation results are presented to show the effectiveness of the proposed adaptive PA algorithms for IA-based CR networks. [ABSTRACT FROM AUTHOR]