Iterative Interference Alignment Algorithm Research Articles

Interference alignment in two‐hop multiple‐input multiple‐output interference channel under various CSI and antenna correlation

In this paper, the problem of interference management is investigated in the downlink of multi-user multiple-input multiple-output two-hop interference channel under various scenarios of channel state information (CSI) and antenna correlation. Interference alignment (IA) in the space dimension is a powerful tool to solve this problem. Iterative IA algorithms are adopted in both the first and the second hops transmissions independently. After aligning interfering signals, the recovery of the transmitted signal vector is performed based on zero-forcing and semi-definite relaxation detectors. Furthermore, the impacts of the imperfect CSI and antenna correlation on sum-rate are investigated for decode-and-forward (DF) and amplify-and-forward relaying protocols. When the system is proper but IA is not completely feasible, the performance of minimum interference leakage (MIL) is compared with rank-constrained rank minimisation (RCRM) algorithm in DF protocol. RCRM algorithm is more sensitive to CSI such that the channel uncertainty easily leads to an increase in the interference matrix rank. According to the simulation results, for the case of perfect CSI, RCRM makes further interference-free dimensions which leads to better performance than MIL, but for the case of imperfect CSI, close performance is observed.

An Iterative Interference Alignment Algorithm for the General MIMO X Channel

Interference alignment (IA) has been shown as an important technique to achieve a linear capacity scaling in wireless communications. However, the IA scheme over finite signaling dimensions for a general multiple-input-multiple-output (MIMO) X channel is still rarely studied. The main challenge of MIMO X channels is that the two sets of conditions for IA, namely the interference nulling conditions and the rank preservation conditions, get coupled. The usual IA methods for the interference channel and the broadcasting channel cannot be applied anymore. In this paper, we show that the rank preservation conditions can be replaced by a group of specific rank conditions, under which the IA problem is simplified. Then, based on this technique, an iterative algorithm of IA is designed for the MIMO X channel. The algorithm is designed with limited signaling dimensions. From the simulation results, we find that the algorithm has good performances even under limited signal-to-noise ratio.

Robust Iterative Interference Alignment for Cellular Networks With Limited Feedback

In theory coordinated multi-point transmission (CoMP) promises vast gains in spectral efficiency. But industrial field trials show rather disappointing throughput gains, whereby the major limiting factor is proper sharing of channel state information. Many recent papers consider this so-called limited feedback problem in the context of CoMP. Usually taking the assumptions: 1) infinite SNR regime, 2) no user selection and 3) ideal link adaptation; rendering the analysis too optimistic. In this paper we make a step forward towards a more realistic assessment of the limited feedback problem by introducing an improved metric for the performance evaluation which better captures the throughput degradation. We find the relevant scaling laws (lower and upper bounds) and how that they are different from existing ones. Moreover, we provide a robust iterative interference alignment algorithm and corresponding feedback strategies achieving the obtained scaling laws. The main idea is that instead of sending the complete channel matrix each user fixes a receive filter and feeds back a quantized version of the effective channel. Finally we underline our findings with simulations for the proposed system.

On feasibility of linear interference alignment for single‐input–single‐output multi‐frequency interference channel

This study explores the feasibility of linear interference alignment (IA) for a ( M × M , 1) K system with multi-frequency independent channels. The authors prove that number of users K ≤ 2 M - 2 is a necessary condition for ( M × M , 1) K multi-frequency IA system, which is less than that of ( M × M , 1) K multiple-input-multiple-output system (i.e. K ≤ 2 M - 1). The conclusion can be extended to all cases with the diagonal channel matrix. They also reveal that the traditional frequency division multiplexing method is inefficient in spectrum utilisation. They apply max-SINR (signal to interference plus noise ratio) and iterative IA algorithms and the author's simulation results verify that IA algorithms work well in ( M × M , 1) 2M-2 IA system.

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.

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.

Subspace optimisation-based iterative interference alignment algorithm on the Grassmann manifold

An iterative interference alignment (IA) algorithm for the multi-user multi-input multi-output interference channel is proposed, which optimises on the Grassmann manifold to improve the performance of conventional interference subspace (ISS) alignment algorithm without the assumption of channel reciprocity. The proposed algorithm combines the extreme eigenvalues method and the modified steepest descent method on the Grassmann manifold to minimise the distances not only between the ISS and the subspace spanned by interference, but also between the desired signal subspace and the subspace spanned by useful signal. Utilising the subspace optimisation above, both the interference and useful signal are aligned to their respective subspaces. Numerical results show that the proposed algorithm can significantly improve the sum rate of interference channel. Moreover, by properly choosing the attenuation factor of step size, the proposed algorithm can achieve an effective tradeoff between sum rate performance and convergence speed, which gives the IA scheme more design flexibility.

On the region of feasibility of interference alignment for underwater acoustic communication

To enable underwater applications such as coastal and tactical surveillance, undersea explorations, and picture/video acquisition, there is a need to achieve high data-rate underwater acoustic communications, which translates into attaining high acoustic channel spectral efficiencies. Interference Alignment (IA) technique, which has recently been proposed for radio-frequency 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 lies in designing transmit precoding matrices for each transmitter such that all the interfering signals align at the receiver along a direction different from the desired signal. To decode, the receiver projects the received signal onto a decoding vector that is orthogonal to the data vector of interfering signal. While promising, there are challenges to be solved for the use of IA underwater, i.e., 1) imperfect acoustic channel knowledge, 2) high computational complexity, and 3) high communication delay. We study the feasibility of IA in underwater environment under these challenges; we also propose a distributed computational framework to parallelize the iterative IA algorithm and determine to what extent we can parallelize it among neighboring nodes under different channel coherence times.

Interference Alignment for Partially Connected MIMO Cellular Networks

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels.

Robust Lattice Alignment for $K$-User MIMO Interference Channels With Imperfect Channel Knowledge

In this paper, we consider a robust lattice alignment design for K-user quasi-static MIMO interference channels with imperfect channel knowledge. With random Gaussian inputs, the conventional interference alignment (IA) method has the feasibility problem when the channel is quasi-static. On the other hand, structured lattices can create structured interference as opposed to the random interference caused by random Gaussian symbols. The structured interference space can be exploited to transmit the desired signals over the gaps. However, the existing alignment methods on the lattice codes for quasi-static channels either require infinite SNR or symmetric interference channel coefficients. Furthermore, perfect channel state information (CSI) is required for these alignment methods, which is difficult to achieve in practice. In this paper, we propose a robust lattice alignment method for quasi-static MIMO interference channels with imperfect CSI at all SNR regimes, and a two-stage decoding algorithm to decode the desired signal from the structured interference space. We derive the achievable data rate based on the proposed robust lattice alignment method, where the design of the precoders, decorrelators, scaling coefficients and interference quantization coefficients is jointly formulated as a mixed integer and continuous optimization problem. The effect of imperfect CSI is also accommodated in the optimization formulation, and hence the derived solution is robust to imperfect CSI. We also design a low complex iterative optimization algorithm for our robust lattice alignment method by using the existing iterative IA algorithm that was designed for the conventional IA method. Numerical results verify the advantages of the proposed robust lattice alignment method.