Limited Feedback Design for Interference Alignment on MIMO Interference Networks with Heterogeneous Path Loss and Spatial Correlations

Interference alignment is degree of freedom optimal in K -user MIMO interference channels and many previous works have studied the transceiver designs. However, these works predominantly focus on networks with perfect channel state information at the transmitters and symmetrical interference topology. In this paper, we consider a limited feedback system with heterogeneous path loss and spatial correlations, and investigate how the dynamics of the interference topology can be exploited to improve the feedback efficiency. We propose a novel spatial codebook design, and perform dynamic quantization via bit allocations to adapt to the asymmetry of the interference topology. We bound the system throughput under the proposed dynamic scheme in terms of the transmit SNR, feedback bits and the interference topology parameters. It is shown that when the number of feedback bits scales with SNR as C_{s}\cdot\log\textrm{SNR}, the sum degrees of freedom of the network are preserved. Moreover, the value of scaling coefficient C_{s} can be significantly reduced in networks with asymmetric interference topology.Comment: 30 pages, 6 figures, accepted by IEEE transactions on signal processing in Feb. 201

CSI Feedback Reduction for MIMO Interference Alignment

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder / decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.Comment: 30 pages, 7 figures, accepted for publication by IEEE transactions on signal processing in June, 201

Interference Alignment — Practical Challenges and Test-bed Implementation

Data traffic over wireless communication networks has experienced a tremendous growth in the last decade, and it is predicted to exponentially increase in the next decades. Enabling future wireless networks to fulfill this expectation is a challenging task both due to the scarcity of radio resources (e.g. spectrum and energy), and also the inherent characteristics of the wireless transmission medium. Wireless transmission is in general subject to two phenomena: fading and interference. The elegant interference alignment concept reveals that with proper transmission signalling design, different interference signals can in fact be aligned together, such that more radio resources can be assigned to the desired transmission. Although interference alignment can achieve a larger data rate compared to orthogonal transmission strategies, several challenges should be addressed to enable the deployment of this technique in future wireless networks For instance, to perform interference alignment, normally, global channel state information (CSI) is required to be perfectly known at all terminals. Clearly, acquiring such channel knowledge is a challenging problem in practice and proper channel training and channel state feedback techniques need to be deployed. In addition, since the channels are time-varying proper adaptive transmission is needed. This chapter review recent advances in practical aspects of interference alignment. It also presents recent test-bed implementations of signal processing algorithms for the realization of interference alignment.Comment: Book Chapter accepted for publication in the book entitled: Contemporary Issues in Wireless Communications, ISBN: 978-953-51-4101-3, Khatib, M. (Ed.), to be published by INTECH Publishers. Expected month of publication: November 201

Interference Alignment and Cancellation in Wireless Communication Systems

The Shannon capacity of wireless networks has a fundamental importance for network information theory. This area has recently seen remarkable progress on a variety of problems including the capacity of interference networks, X networks, cellular networks, cooperative communication networks and cognitive radio networks. While each communication scenario has its own characteristics, a common reason of these recent developments is the new idea of interference alignment. The idea of interference alignment is to consolidate the interference into smaller dimensions of signal space at each receiver and use the remaining dimensions to transmit the desired signals without any interference. However, perfect alignment of interference requires certain assumptions, such as perfect channel state information at transmitter and receiver, perfect synchronization and feedback. Today’s wireless communication systems, on the other and, do not encounter such ideal conditions. In this thesis, we cover a breadth of topics of interference alignment and cancellation schemes in wireless communication systems such as multihop relay networks, multicell networks as well as cooperation and optimisation in such systems. Our main contributions in this thesis can be summarised as follows: • We derive analytical expressions for an interference alignment scheme in a multihop relay network with imperfect channel state information, and investigate the impact of interference on such systems where interference could accumulate due to the misalignment at each hop. • We also address the dimensionality problem in larger wireless communication systems such as multi-cellular systems. We propose precoding schemes based on maximising signal power over interference and noise. We show that these precoding vectors would dramatically improve the rates for multi-user cellular networks in both uplink and downlink, without requiring an excessive number of dimensions. Furthermore, we investigate how to improve the receivers which can mitigate interference more efficiently. • We also propose partial cooperation in an interference alignment and cancellation scheme. This enables us to assess the merits of varying mixture of cooperative and non-cooperative users and the gains achievable while reducing the overhead of channel estimation. In addition to this, we analytically derive expressions for the additional interference caused by imperfect channel estimation in such cooperative systems. We also show the impact of imperfect channel estimation on cooperation gains. • Furthermore, we propose jointly optimisation of interference alignment and cancellation for multi-user multi-cellular networks in both uplink and downlink. We find the optimum set of transceivers which minimise the mean square error at each base station. We demonstrate that optimised transceivers can outperform existing interference alignment and cancellation schemes. • Finally, we consider power adaptation and user selection schemes. The simulation results indicate that user selection and power adaptation techniques based on estimated rates can improve the overall system performance significantly

Un-coordinated multi-user and inter-cell interference alignment based on partial and outdated information for large cellular networks

The cellular networks have gone through rapid evolution during the past decade. However, their performance is still limited due to the problem of interference. Therefore, interference management in current and future cellular networks is still an ongoing research topic. Interference Alignment is one of the techniques to manage the interference efficiently by using "align" and "suppression" strategy. In the first part of this thesis we focus on Coordinated inter cell interference alignment in a large cellular network. We assess the performance of interference alignment based transmit precoding under specific receiver strategies and coordination scenarios by comparing with different state of the art precoding schemes. We continue our assessment by considering imperfect channel state information at the transmitter. The results show that the gains of coordinated alignment based transmission are very sensitive to the receiver strategies and imperfections as compared to the other precoding schemes. However, in case of the availability of good channel conditions with very slow moving users, coordinated interference alignment outperforms the other baselines even with imperfect channel state information. In addition to that, we propose efficient user selection methods to enhance the performance of coordinated alignment. The results of our assessment draws important conclusions about the application of coordinated interference alignment in practical systems. In the second part of the thesis we consider a cellular system where each cell is serving multiple users simultaneously using the same radio resource. In this scenario, we have to manage not only the inter cell interference but also the multi user interference. For this purpose, we propose a novel Uncoordinated transmit precoding scheme for multi user cellular networks which is based on the alignment of multi user interference with partial and outdated inter cell interference. We show analytically that our scheme approaches the performance optimal transmission scheme. With the help of simulations we show that our proposal outperforms the state of the art non-alignment based multi user transmit precoding schemes We further propose user selection methods which exploit the diversity gains and improve the system spectral efficiency. In order to assess the feasibility of our proposal in a real system, we evaluate our scheme with practical constraints like imperfect information at the transmitter and limited feedback in uplink channel. For the proof of concept we also evaluate the performance of our scheme with measured channels using a software defined measurement platform. Finally, we also assess the application of our proposal in future heterogeneous networks. The outcome of our efforts states that as an interference alignment based transmission scheme, our scheme is a good candidate to manage the two dimensional interference in multi user cellular networks. It outperforms the non-alignment baselines in many scenarios even with practical constraints

Interference Alignment Through User Cooperation for Two-cell MIMO Interfering Broadcast Channels

This paper focuses on two-cell multiple-input multiple-output (MIMO) Gaussian interfering broadcast channels (MIMO-IFBC) with $K$ cooperating users on the cell-boundary of each BS. It corresponds to a downlink scenario for cellular networks with two base stations (BSs), and $K$ users equipped with Wi-Fi interfaces enabling to cooperate among users on a peer-to-peer basis. In this scenario, we propose a novel interference alignment (IA) technique exploiting user cooperation. Our proposed algorithm obtains the achievable degrees of freedom (DoF) of 2K when each BS and user have $M=K+1$ transmit antennas and $N=K$ receive antennas, respectively. Furthermore, the algorithm requires only a small amount of channel feedback information with the aid of the user cooperation channels. The simulations demonstrate that not only are the analytical results valid, but the achievable DoF of our proposed algorithm also outperforms those of conventional techniques.Comment: This paper will appear in IEEE GLOBECOM 201

The Practical Challenges of Interference Alignment

Interference alignment (IA) is a revolutionary wireless transmission strategy that reduces the impact of interference. The idea of interference alignment is to coordinate multiple transmitters so that their mutual interference aligns at the receivers, facilitating simple interference cancellation techniques. Since IA's inception, researchers have investigated its performance and proposed improvements, verifying IA's ability to achieve the maximum degrees of freedom (an approximation of sum capacity) in a variety of settings, developing algorithms for determining alignment solutions, and generalizing transmission strategies that relax the need for perfect alignment but yield better performance. This article provides an overview of the concept of interference alignment as well as an assessment of practical issues including performance in realistic propagation environments, the role of channel state information at the transmitter, and the practicality of interference alignment in large networks.Comment: submitted to IEEE Wireless Communications Magazin