Interference alignment is a radical idea that has recently emerged out of the capacity analysis of interference networks. In a relatively short time, this concept has challenged much of the conventional wisdom about the throughput limits of both wired and wireless networks. The technique is based on the idea that if interference or undesired signals are collectively engineered to occupy as low a dimensional space as possible, the desired signals can be more efficiently conveyed and recovered. Interference Alignment: A New Look at Signal Dimensions in a Communication Network introduces the concept via a series of simple linear algebraic examples that very effectively convey the central idea of interference alignment. The ideas behind the various communication theoretic instantiations and applications of the technique are illustrated through simple examples that come across as neat little puzzles which enables the reader to quickly understand the topic. Interference Alignment: A New Look at Signal Dimensions in a Communication Network provides both a tutorial and a survey of the state-of-art on the topic. The numerous concretely stated open problems sprinkled throughout tee up issues for further research for the reader. The presentation style of this tutorial is informal, favoring broad intuition over mathematical rigor and is written to be accessible to graduate students, researchers and professionals working in communication, signal processing, networking or information theory.

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Interference Alignment: A New Look at Signal Dimensions in a Communication Network

This work studies the joint design of precoding and backhaul compression strategies for the downlink of cloud radio access networks. In these systems, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art approaches, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals of different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate with respect to both the precoding matrix and the joint correlation matrix of the quantization noises is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. Moreover, in order to enable the practical implementation of multivariate compression across BSs, a novel architecture is proposed based on successive steps of minimum mean-squared error (MMSE) estimation and per-BS compression. Robust design with respect to imperfect channel state information is also discussed. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on the separate design of precoding and compression or independent compression across the BSs.

Joint Precoding and Multivariate Backhaul Compression for the Downlink of Cloud Radio Access Networks

Aspects of the subject disclosure may include, for example, a system for receiving decoded satellite signals, obtaining media channels from the decoded satellite signals, selecting a portion of the media channels for distribution to a plurality of media processors, encoding the portion of the media channels selected according to a satellite distribution protocol, such as a protocol that facilitates satellite switching, to generate satellite encoded content, formatting the satellite encoded content according to a transport protocol to generate formatted content, and providing the formatted content for distribution to the plurality of media processors via a communication network, such as a single wire communication network or other network. Other embodiments are disclosed.

Method and apparatus for distributing content in a communication network

This paper studies distributed compression for the uplink of a cloud radio access network where multiple multiantenna base stations (BSs) are connected to a central unit, which is also referred to as a cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed source coding strategies are potentially beneficial. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and they are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst case approach, and an algorithm is proposed that achieves a stationary point for the problem. Then, BS selection is addressed with the aim of reducing the number of active BSs, thus enhancing the energy efficiency of the network. An optimization problem is formulated in which compression and BS selection are performed jointly by introducing a sparsity-inducing term into the objective function. An iterative algorithm is proposed that is shown to converge to a locally optimal point. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information. Moreover, the proposed BS selection algorithm is seen to perform close to the more complex exhaustive search solution.

Robust and Efficient Distributed Compression for Cloud Radio Access Networks

The capacity of interference network is a fundamental issue that eludes the researchers for decades. Interference alignment (IA) is an emerging interference management technique that is degrees of freedom (DoFs) optimal, which means it can approach the capacity of interference network at very high signal-to-noise ratio (SNR). In IA networks, the signals are constrained into the same subspaces at the unintended receivers through cooperative precoding, and the desired signal can be recovered at each receiver by eliminating the aligned interferences using decoding matrix. Due to its promising performance in interference management, IA has successfully been applied to many kinds of multiuser wireless networks with excellent performance. Nevertheless, there are still some challenges for the practical utilization of IA, e.g., the overhead of channel state information (CSI) feedback, performance degradation at low and moderate SNRs, etc. In this review, we provide a survey on IA and its applications and discuss some research issues and challenges. The dimensions, networks topologies, and applications of IA are first introduced. Then some fundamental aspects of IA are discussed, including feasibility condition, performance metrics, iterative algorithms, and CSI. We also present some recent research issues of opportunistic IA, spectrum sharing, green IA, topology management, and physical layer security. Finally, some research challenges of IA are identified.

Interference Alignment and Its Applications: A Survey, Research Issues, and Challenges

In this article we present a survey on the technical challenges of future radio access networks beyond LTE-Advanced, which could offer very high average area throughput to support a huge demand for data traffic and high user density with energy-efficient operation. We highlight various potential enabling technologies and architectures to support the aggressive goal of average area throughput 25 Gb/s/km2 in beyond IMT-Advanced systems. Specifically, we discuss the challenges and solutions from the controlling/ processing perspective, the radio resource management perspective, and the physical layer perspective for dense urban cell deployment. Using various advanced technologies such as interference mitigation techniques, MIMO, and cooperative communications as well as cross-layer self-organizing networks, we show that future urban wireless networks could potentially offer high-quality mobile services and offer an experience similar to the wired Internet.

A 25 Gb/s(/km 2 ) urban wireless network beyond IMT-advanced

In this paper, we propose a robust transceiver design for the K-pair quasi-static MIMO interference channel. Each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the kth transmitter sends Lk independent data streams to the desired receiver. In the literature, there exist a variety of theoretically promising transceiver designs for the interference channel such as interference alignment-based schemes, which have feasibility and practical limitations. In order to address practical system issues and requirements, we consider a transceiver design that enforces robustness against imperfect channel state information (CSI) as well as fair performance among the users in the interference channel. Specifically, we formulate the transceiver design as an optimization problem to maximize the worst-case signal-to-interference-plus-noise ratio among all users. We devise a low complexity iterative algorithm based on alternative optimization and semi-definite relaxation techniques. Numerical results verify the advantages of incorporating into transceiver design for the interference channel important practical issues such as CSI uncertainty and fairness performance.

Robust Transceiver Design for K-Pairs Quasi-Static MIMO Interference Channels via Semi-Definite Relaxation

In this paper, we propose a robust transceiver design for the K-pair quasi-static MIMO interference channel. Each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the k-th transmitter sends L_k independent data streams to the desired receiver. In the literature, there exist a variety of theoretically promising transceiver designs for the interference channel such as interference alignment-based schemes, which have feasibility and practical limitations. In order to address practical system issues and requirements, we consider a transceiver design that enforces robustness against imperfect channel state information (CSI) as well as fair performance among the users in the interference channel. Specifically, we formulate the transceiver design as an optimization problem to maximize the worst-case signal-to-interference-plus-noise ratio among all users. We devise a low complexity iterative algorithm based on alternative optimization and semi-definite relaxation techniques. Numerical results verify the advantages of incorporating into transceiver design for the interference channel important practical issues such as CSI uncertainty and fairness performance.

In this paper, we propose a robust transceiver design for the K-pair quasi-static MIMO interference channel Each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the kth transmitter sends L k independent data streams to the desired receiver In the literature, there exist a variety of theoretically promising transceiver designs for the interference channel such as interference alignment-based schemes, which have feasibility and practical limitations In order to address practical system issues and requirements, we consider a transceiver design that enforces robustness against imperfect channel state information (CSI) as well as fair performance among the users in the interference channel Specifically, we formulate the transceiver design as an optimization problem to maximize the worst-case signal-to-interference-plus-noise ratio among all users We devise a low complexity iterative algorithm based on alternative optimization and semi-definite relaxation techniques Numerical results verify the advantages of incorporating into transceiver design for the interference channel important practical issues such as CSI uncertainty and fairness performance

Robust transceiver design for K-pairs quasi-static MIMO interference channels via semi-definite relaxation

In this paper, we consider a robust lattice alignment design for K-user quasi-static multiple-input multiple-output (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 signal-to-noise ratio (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 compared with the time-division multiple-access (TDMA), two-stage maximum-likelihood (ML) decoding, generalized Han-Kobayashi (HK), distributive IA and conventional IA methods in the literature.

Sheng Liu

Papers

A 25 Gb/s(/km 2 ) urban wireless network beyond IMT-advanced

Robust Transceiver Design for K-Pairs Quasi-Static MIMO Interference Channels via Semi-Definite Relaxation

Robust Transceiver Design for K-Pairs Quasi-Static MIMO Interference Channels via Semi-Definite Relaxation

Robust transceiver design for K-pairs quasi-static MIMO interference channels via semi-definite relaxation

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