Existing Interference Alignment Schemes Research Articles

An Interference Alignment Design with Higher Multiplexing Gain by the Reduced Channel Extension

For a K-user Single Input Single Output fading interference channel, Cadambe and Jafar proposed a scheme that achieves K=2 multiplexing gain asymptotically employing interference alignment technique. This scheme is improved by Choi et al. in which for reduced channel extension, higher multiplexing gain is achievable. But the existing designs require an infinite number of channel extension to achieve the upperbound. Our proposed design attains higher multiplexing gain that is arbitrarily close to the upper-bound by using a lot lower number of channel extension in comparison with the other existing interference alignment schemes. On the other hand, due to the great symbol extension reduction of the channel, the computational complexity to achieve K=2 degrees of freedom (desired upper-bound) is also reduced significantly. Moreover, it can be shown that the proposed design achieves higher interference alignment quality. For a limited channel extension, the proposed scheme leads to a better allocation of the signal space at each receiver to the desired and interfered signal. This results to a higher quality of alignment.

Non‐cooperative interference alignment for multicell multiuser MIMO uplink channels

Linear interference alignment (IA) is considered a promising interference management technique for future cellular networks. However, its application to practical cellular networks still faces many challenges, two of which are the overhead of sharing global channel state information (CSI) and the cost of signalling dimensionality. These two challenges are resolved in this study via a new linear IA scheme that not only prevents global CSI sharing but also keeps the number of signalling dimensions manageable. While making linear IA more applicable, the proposed scheme provides comparable degrees of freedom to the existing IA schemes in symmetric multicell multiuser multiple-input multiple-output (MIMO) uplink channels.

Interference Alignment Based on Antenna Selection With Imperfect Channel State Information in Cognitive Radio Networks

Interference alignment (IA) is a promising technique that can eliminate interference in wireless networks effectively and has been applied to spectrum sharing in cognitive radio (CR) networks. However, most existing IA schemes neglect the quality of the desired signal, which may lead to poor performance, particularly at poor channel status. In this paper, we analyze the problem of the decrease in the signal-to-interference-plus-noise ratio (SINR) of the desired signal and propose a novel IA scheme based on antenna selection (AS) to improve the received SINR of each user in IA-based CR networks. In the proposed scheme, multiple antennas are equipped at each secondary receiver, and some of them are chosen to achieve optimal performance. Furthermore, the condition of imperfect channel state information (CSI) is also considered, which can impact the performance of IA-AS. To face this problem, a scheme called CSI filtering is proposed to weaken the influence of the imperfect CSI. Moreover, considering the considerable computational complexity brought by the selection among mass of antenna combinations, an efficient IA-AS algorithm based on discrete stochastic optimization (DSO) is thus proposed, which can converge quickly to the optimum with low computational complexity. To further improve the tracking performance of the algorithm under a time-varying channel environment, we propose an adaptive DSO scheme with window CSI filtering for IA-AS to give the algorithm a good tracking capability. Simulation results are presented to show that the proposed schemes can significantly improve the performance of IA-based CR networks.

Achieving Maximum Degrees of Freedom of Two-Hop MIMO Alternate Half-Duplex Relaying System For Linear Transceivers: A Unified Transmission Framework for DF and AF Protocols

In this paper, a novel unified transmission framework in the context of both decode-and-forward (DF) and amplify-and-forward (AF) protocols was proposed for a two-hop multiple-input–multiple-output (MIMO) alternate half-duplex (HD) relaying system with $M$ antennas for each node, which can achieve the maximum $M$ degrees of freedom (DoFs) of the system, compared with the existing interference alignment (IA) schemes that can only get $\hbox{3}M/\hbox{4} $ DoF. Furthermore, the proposed schemes require only two relays against three relays invoked in the existing IA schemes. Moreover, the proposed schemes are valid for the cases where the number of antennas $M$ can be either even or odd, whereas the existing IA schemes can only apply for an even number of antennas $M$ .

EIV-Based Interference Alignment Scheme with CSI Uncertainties

A novel interference alignment (IA) scheme based on the errors-in-variables (EIV) mathematic model has been proposed to overcome the channel state information (CSI) estimation error for the MIMO interference channels. By solving an equivalently unconstrained optimization problem, the proposed IA scheme employing a weighted total least squares (WTLS) algorithm can obtain the solution to a constrained optimization problem for transmit precoding (TPC) matrices and minimizes the distortion caused by imperfect CSI according to the EIV model. It is shown that the design of TPC matrices can be realized through an efficient iterative algorithm. The convergence of the proposed scheme is presented as well. Simulation results show that the proposed IA scheme is robust over MIMO interference channels with imperfect CSI, which yields significantly better sum rate performance than the existing IA schemes such as distributed iterative IA, maximum signal-to-interference-plus-noise ratio (Max SINR), and minimum mean square error (MMSE) schemes.

A Signal Subspace Interference Alignment Scheme with Sum Rate Maximization and Altruistic-Egoistic Bayesian Gaming

In this paper, we propose a distributed signal subspace interference alignment algorithm for single beam K?user (3K≥) MIMO interference channel based on sum rate maximization and game theory. A framework of game theory is provided to study relationship between interference signal subspace and altruistic-egoistic bayesian game cost function. We demonstrate that the asymptotic interference alignment under proposed scheme can be realized through a numerical algorithm using local channel state information at transmitters and receivers. Simulation results show that the proposed scheme can achieve the total degrees of freedom that is equivalent to the Cadambe-Jafar interference alignment algorithms with perfect channel state information. Furthermore, proposed scheme can effectively minimize leakage interference in desired signal subspace at each receiver and obtain a moderate average sum rate performance compared with several existing interference alignment schemes.

Adaptive Interference Alignment with CSI Uncertainty

Interference alignment (IA) is known to significantly increase sum-throughput at high SNR in the presence of multiple interfering nodes, however, the reliability of IA is little known, which is the subject of this paper. We study the error performance of IA and compare it with conventional orthogonal transmission schemes. Since most IA algorithms require extensive channel state information (CSI), we also investigate the impact of CSI imperfection (uncertainty) on the error performance. Our results show that under identical rates, IA attains a better error performance than the orthogonal scheme for practical signal to noise ratio (SNR) values but is more sensitive to CSI uncertainty. We design bit loading algorithms that significantly improve error performance of the existing IA schemes. Furthermore, we propose an adaptive transmission scheme that not only considerably reduces error probability, but also produces robustness to CSI uncertainty.

Interference Alignment With Differential Feedback

Interference alignment (IA) has been recognized as a promising technique to obtain large multiplexing gain in multiple-input-multiple-output (MIMO) interference channels. Most existing IA schemes require global channel state information (CSI) at the transmitter to design precoding vectors and, thus, result in significant capacity overhead in the feedback link. To reduce the feedback overhead, in this paper, we investigate the IA scheme employing differential CSI feedback over time-correlated MIMO channels. Specifically, we analyze the achievable sum-rate performance using differential feedback and derive the minimum differential feedback rate to achieve the maximum multiplexing gain. We also provide the upper bound of the sum-rate performance loss due to the limited differential feedback rate. Finally, the analytical results are verified by simulations in a practical IA scheme with differential CSI feedback using Lloyd's quantization algorithm.

MSE-Based Transceiver Designs for the MIMO Interference Channel

Interference alignment (IA) has evolved as a powerful technique in the information theoretic framework for achieving the optimal degrees of freedom of interference channel. In practical systems, the design of specific interference alignment schemes is subject to various criteria and constraints. In this paper, we propose novel transceiver schemes for the MIMO interference channel based on the mean square error (MSE) criterion. Our objective is to optimize the system performance under a given and feasible degree of freedom. Both the total MSE and the maximum per-user MSE are chosen to be the objective functions to minimize. We show that the joint design of transmit precoding matrices and receiving filter matrices with both objectives can be realized through efficient iterative algorithms. The convergence of the proposed algorithms is proven as well. Simulation results show that the proposed schemes outperform the existing IA schemes in terms of BER performance. Considering the imperfection of channel state information (CSI), we also extend the MSE-based transceiver schemes for the MIMO interference channel with CSI estimation error. The robustness of the proposed algorithms is confirmed by simulations.