Interference Alignment Algorithm Research Articles

Distributed Interference Alignment for Mobile MIMO Systems Based on Local CSI

The requirement for global channel state information (CSI) throughout the network is a significant hurdle to overcome in interference alignment (IA) technologies for mobile applications. In this letter, a distributed IA algorithm is proposed that uses local CSI at the receivers to determine desirable precoding matrices for all interfering transmitters. These matrices are fed back to the transmitters where they are combined, using scalar weighting, to determine the next set of transmit precoding matrices. Results herein show that, with equivalent feedback limitations, the proposed algorithm provides better SINR performance than an IA algorithm requiring full CSI-based feedback, due to the smaller dimensions of the precoding matrices.

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.

Network Anti-interference Technology Based on Hybrid Optimization Algorithm

According to the cooperation reception strategy and the hybrid optimization, an interference alignment algorithm is proposed. The algorithm can minimize leakage interference and maximize ideal receiption signal power. Firstly, according to the receiver cooperation technology, the pre-coding vectors of transmitter terminal can be estimated by receiver terminal, where these vectors need not be known in advance. In addition, the sum of interference covariance matrix is enough to describe the statistical features of interference alignment problem, which can quickly devise cooperation reception structure. Therefore, based on the bargaining process of cooperation game theory, the optimal full-cooperation mechanism of all the receivers can be selected. The simulation results show that compared with traditionary distributed interference alignment mechanism, our proposed interference alignment mechanism based on cooperation reception strategy and hybrid optimization technology has excellent feasibility under various transmission-power conditions.

Throughput-Efficient Scheduling and Interference Alignment for MIMO Wireless Systems

Multiple-input multiple-output (MIMO) wireless communication systems can achieve higher throughput through interference alignment. For a small number of users, determining the maximum possible degrees of freedom as well as the feasibility of interference alignment in MIMO systems is well studied. However, the issues of scheduling in systems employing interference alignment and serving a large number of users have received little attention so far. In this paper, we study the problem of joint scheduling, interference alignment, and packet admission control in MIMO wireless systems with the goal of maximizing system throughput subject to stability constraints. We formulate a stochastic network optimization problem and propose a scheduling and interference alignment (SIA) algorithm. In each time slot, SIA schedules some users among many competing ones to transmit data, and determines encoding and decoding matrices for the selected users. Packet admission control is performed in each time slot. In addition, we propose a heuristic semi-distributed algorithm (SDSIA), which has a lower computational complexity than the SIA algorithm. Via simulation, we evaluate the performance of SIA and SDSIA for different algorithm parameters and different numbers of users. We also compare the performance of SDSIA with other approaches which do not simultaneously exploit interference alignment and scheduling and find that the combination of these two techniques increases the achievable data rate dramatically.

A Diversity Analysis for Distributed Interference Alignment Using the Max-SINR Algorithm

Gomadam recently proposed two distributed interference alignment algorithms, namely the zero-forcing and the maximal signal to interference plus noise ratio (max-SINR) algorithms. Both of them only require local channel state information and no symbol extension is needed. Then, Ning showed that when only one stream of information symbols is sent by each user, interference alignment may achieve receive diversity using the max-SINR algorithm. This result was, however, derived only based on an assumption. In this paper, using a different approach, we prove that interference alignment using the max-SINR algorithm indeed achieves receive diversity without the assumption used by Ning The result in this paper not only completes the proof of the result by Ning , but also generalizes it by allowing more than one stream of information symbols to be sent by each user.

Interference Alignment Algorithm Based on Orthogonal-triangular Decomposition for Edge Users in 3-cell MIMO Cellular Networks

针对3小区每小区2个边缘用户的MIMO下行链路，在不改变系统自由度的前提下对联合信道进行下三角分解得到系统的等效模型；在此基础上，根据不同小区受到的干扰不同，该文提出一种线性干扰对齐算法同时消除小区间干扰和小区内干扰。该算法利用联合信道分解可以先消除一半的小区间干扰，并根据不同小区的用户设计其干扰抑制矩阵，减少了用户端接收矩阵个数及相应处理，从而降低了接收端的复杂度。仿真表明，随着数据流的增加，系统容量和能量效率都有明显的提高，在自由度相同的情况下，天线数不同对系统容量和能量效率有较大影响。

New robust iterative minimum mean squared error‐based interference alignment algorithm

Interference alignment (IA) is a promising technique for multiple input multiple output interference channels based systems, achieving the theoretical bound on degrees of freedom. However, these gains are reduced in the presence of imperfect channel state information (CSI), because of quantisation or channel estimation errors. In this Letter, the authors propose a new robust iterative IA minimum mean squared error-based algorithm, which includes these channel errors in the IA design. The results show that the proposed robust IA algorithm outperforms the known IA-MMSE algorithms, for low-to-moderate variance of CSI errors.

Beam Tracking for Interference Alignment in Time-Varying MIMO Interference Channels: A Conjugate-Gradient-Based Approach

In this paper, an adaptive beam tracking algorithm for interference alignment (IA) in time-varying multiple-input-multiple-output (MIMO) interference channels is presented. It is shown that obtaining a set of interference-aligning transmit beamforming matrices is equivalent to minimizing a certain Rayleigh quotient, and an approach based on the conjugate gradient (CG) method combined with metric projection is applied to this minimization problem to construct an adaptive algorithm for interference-aligning beam design. The convergence of the proposed algorithm in static channels is established, and the steady-state behavior of the proposed algorithm in time-varying channels is investigated by numerical simulations. The performance of the proposed algorithm is evaluated numerically, and numerical results show that the proposed algorithm performs well with low computational complexity.

Two novel iterative algorithms for interference alignment with symbol extensions in the MIMO interference channel

Interference alignment (IA) with symbol extensions in the quasi-static flat-fading K-user multipleinput multiple-output (MIMO) interference channel (IC) is considered in this paper. In general, long symbol extensions are required to achieve the optimal fractional degrees of freedom (DOF). However, long symbol extensions over orthogonal dimensions produce structured (diagonal or block diagonal) channel matrices from transmitters to receivers. Most of existing approaches are limited in cases where the channels have some special structures, because they align the interference without preserving the dimensionality of the desired signal explicitly. To overcome this common drawback of most existing IA algorithms, two novel iterative algorithms for IA with symbol extensions are proposed. The first algorithm designs transceivers for IA based on the mean square error (MSE) criterion which minimizes the total MSE of the system while preserving the dimensionality of the desired signal. The novel IA algorithm is a constrained optimization problem which can be solved by Lagrangian method. Its convergence is proven as well. Utilizing the reciprocity of alignment, the second algorithm is proposed based on the maximization of the multidimensional case of the generalized Rayleigh Quotient. It maximizes each receiver’s signal to interference plus noise ratio (SINR) while preserving the dimensionality of the desired signal. In simulation results, we show the superiority of the proposed algorithms in terms of four aspects, i.e., average sum rate, the fraction of the interfering signal power in the desired signal subspace, bit error rate (BER) and the relative power of the weakest desired data stream.

Region of Feasibility of Interference Alignment in Underwater Sensor Networks

To enable underwater applications such as coastal and tactical surveillance, undersea explorations, and real-time picture/video acquisition, there is a need to achieve high data-rate and reliable communications underwater, which translates into attaining high acoustic channel spectral efficiencies. Interference alignment (IA), which has been recently proposed for radio-frequency multiple-input-multiple-output (MIMO) terrestrial communication systems, aims at improving the spectral efficiency by enabling nodes to transmit data simultaneously at a rate equal to half of the interference-free channel capacity. The core of IA in the space domain lies in designing transmit precoding matrices for each transmitter such that all the interfering signals align at the receiver along a direction different from that of the desired signal. While promising, however, there are still challenges to solve for the practical use of IA underwater, i.e., imperfect acoustic channel knowledge, high computational complexity, and high communication delay. In this paper, a feasibility study on the employment of IA underwater is presented. A novel distributed computing framework for sharing processing resources in the network so to parallelize and speed IA algorithms up is proposed; also, such framework enables “ensemble learning” of various precoding matrices computed using different (competing) IA algorithms so to achieve efficient alignment of the interference at the receiver. The robustness of the IA technique against imperfect acoustic channel knowledge is also quantified by estimating precoding matrices based on predicted channel coefficients. Finally, the performance of an algorithm to predict the underwater acoustic channel impulse response is presented using real data sets.

Interference alignment based on subspace tracking in MIMO cognitive networks with multiple primary users

The interference alignment (IA) algorithm based on FDPM subspace tracking (FDPM-ST IA) is proposed for MIMO cognitive network (CRN) with multiple primary users in this paper. The feasibility conditions of FDPM-ST IA is also got. Futherly, IA scheme of secondary network and IA scheme of primary network are given respectively without assuming a priori knowledge of interference covariance matrices. Moreover, the paper analyses the computational complexity of FDPM-ST IA. Simulation results and theoretical calculations show that the proposed algorithm can achieve higher sum rate with lower computational complexity.

Performance Optimization of MIMO Y Channels: Interference Alignment and Signal Detection

In this letter, we propose a combined scheme of interference alignment and signal detection to optimize the performance of a multiple-input multiple-output (MIMO) Y channel, which consists of three users and a middle relay equipped with nU and nR antennas, respectively. First, an iterative interference alignment algorithm is introduced to optimize both beamforming and combining vectors by minimizing the spillage of the interference signals, and ensuring that the desired signals fall into the orthogonal complement of the interference subspace. Then, a signal detection scheme, which employs feed-forward and feedback filters, is applied at the relay to further deal with system interferences. Simulation results show that the proposed combined scheme for MIMO Y channels produces a significant improvement over the existing schemes.

Precoding method interference management for quasi-EVD channel.

The Cholesky decomposition-block diagonalization (CD-BD) interference alignment (IA) for a multiuser multiple input multiple output (MU-MIMO) relay system is proposed, which designs precoders for the multiple access channel (MAC) by employing the singular value decomposition (SVD) as well as the mean square error (MSE) detector for the broadcast Hermitian channel (BHC) taken advantage of in our design. Also, in our proposed CD-BD IA algorithm, the relaying function is made use to restructure the quasieigenvalue decomposition (quasi-EVD) equivalent channel. This approach used for the design of BD precoding matrix can significantly reduce the computational complexity and proposed algorithm can address several optimization criteria, which is achieved by designing the precoding matrices in two steps. In the first step, we use Cholesky decomposition to maximize the sum-of-rate (SR) with the minimum mean square error (MMSE) detection. In the next step, we optimize the system BER performance with the overlap of the row spaces spanned by the effective channel matrices of different users. By iterating the closed form of the solution, we are able not only to maximize the achievable sum-of-rate (ASR), but also to minimize the BER performance at a high signal-to-noise ratio (SNR) region.

Interference alignment in K ‐user multiple‐input–multiple‐output interference channels with partially coordinated receivers

In a K-user multiple-input multiple-output (MIMO) interference channel where each N-antenna transmitter communicates with its corresponding M-antenna receiver by using d degrees of freedom (DoF), the feasibility condition for conventional interference alignment (IA) implies that M + N > d(K + 1). In this paper, we consider a K-user MIMO interference channel where averagely half of the total decoded data can be shared by receive nodes via partial coordination. We propose IA algorithms such that first, their feasibility condition can now be expressed as M + N > d(K + 4)/2, which further implies less number of antennas is needed in comparison with conventional IA techniques to achieve the same number of DoF; and second, even with this reduced number of antennas, their throughput is still comparable to that of conventional IA methods. This is particularly pronounced for asymptotically large K where the required number of antennas for the proposed IA schemes can be half of that of conventional IA techniques to secure the same number of DoF. Moreover, we also examine the performance of the proposed IA algorithms in the presence of both error propagations and channel estimation errors. Simulation results show that in these cases, the throughput of the proposed algorithms can be comparable to that of conventional IA techniques.

Interference Alignment Algorithm over Partially connected MIMO Interference Broadcast Channels Network

Conversely to the fully connected case, it has been proved in theory that interference alignment (IA) can be achievable in a partially connected multi-cell multiple input and multiple output (MIMO) interfering broadcast channels (IBC) network of arbitrary size efficiently. For this applicable significance, based on the L-interfering MIMO IBC model, we present three iterative IA algorithms to solve the alignment problem for this type of model in this paper. Then we discuss the feasibility conditions and the computational complexity of the algorithms. Simulations show that, with a finite antenna number per transmitter and receiver pair between base station (BS) and user, the proposed algorithms can achieve the optimal degrees of freedom (DoF) and can be applied to a partially connected MIMO IBC network with arbitrary number of cells and users per cell.

A Novel Interference Alignment Scheme Based on Sequential Antenna Switching in Wireless Networks

Interference alignment (IA) is a promising technique that can effectively eliminate the interference in wireless networks. However, in traditional IA schemes, the signal to interference plus noise ratio (SINR) may significantly degrade, and the quality of service (QoS) may be unacceptable. In this paper, a novel IA scheme based on antenna switching (AS-IA) is proposed to improve the SINR of the received signal while guaranteeing the QoS in IA wireless networks. In the proposed scheme, some of the antennas are replaced by reconfigurable ones that can switch among preset modes, and the best channel coefficients are selected. Furthermore, to reduce the computational complexity, a sequential antenna switching IA (SAS-IA) scheme is proposed with only one antenna switching in each time slot, and the communication proceeds during the process of searching for the optimal solution. To further improve the performance of the SAS-IA scheme under imperfect channel state information (CSI), a filtering SAS-IA scheme is proposed through averaging the estimated CSI during the iterations of the distributed IA algorithm. Simulation results are presented to show the effectiveness and efficiency of the proposed schemes in improving the QoS of IA wireless networks.

A New Interference Alignment Algorithm in the MIMO-OFDM System

A New Interference Alignment Algorithm in the MIMO-OFDM System

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 for MIMO cognitive networks: a complex FDPM-based subspace tracking approach

Interference alignment (IA) for multiple-input multiple-output (MIMO) cognitive networks is considered by modelling the unlicensed secondary transmitter-receiver pairs which coexist with the licensed multi-antenna primary user as a K-user MIMO interference channel. Starting from investigating the constraint conditions of IA scheme in MIMO cognitive networks, a practical IA algorithm is developed by using a subspace tracking approach based on the complex fast data projection method (FDPM). In the proposed algorithm, first, each secondary transmitter aligns its transmitted signal into the null space of the channel matrix from itself to the primary user without causing any interference to the primary user. Then secondary transmitters and receivers, requiring no channel knowledge of secondary networks, alternately design the precoding and postprocessing matrices through a training period which exploits the complex FDPM-based minor subspace tracking, thus eliminating interference among secondary users. Moreover, the case where secondary transmitters have partial knowledge of channels from themselves to the primary user is also discussed. Simulation results reveal that the proposed algorithm can achieve a high sum rate performance while requiring low computational complexity.

제한된 궤환 채널 기반 상향링크 MIMO/FDD 시스템에서의 효율적인 간섭 정렬 기법

Assuming perfect channel state information (CSI), the conventional interference alignment (IA) algorithm in the uplink cellular system suppresses inter-cell interference (ICI) by aligning ICI to a randomly selected reference vector. However, IA in practice relies on limited feedback between base stations and users, resulting in residual ICI. In this paper, we propose the optimization of the reference vector that minimizes the upper-bound of residual ICI power. Secondly, the iterative IA that designs the direction of transmit and receive filter is proposed to minimize the residual ICI as well as maximize the desired signals. Moreover, we propose the user scheduling method combined with proposed IA schemes which provides the multiuser diversity gain in multi-cell environments. Finally, the performance gain of the proposed IA algorithms compared with the existing IA are analyzed and demonstrated by simulation results.