47,685 research outputs found
Regularized ZF in Cooperative Broadcast Channels under Distributed CSIT: A Large System Analysis
Obtaining accurate Channel State Information (CSI) at the transmitters (TX)
is critical to many cooperation schemes such as Network MIMO, Interference
Alignment etc. Practical CSI feedback and limited backhaul-based sharing
inevitably creates degradations of CSI which are specific to each TX, giving
rise to a distributed form of CSI. In the Distributed CSI (D-CSI) broadcast
channel setting, the various TXs design elements of the precoder based on their
individual estimates of the global multiuser channel matrix, which intuitively
degrades performance when compared with the commonly used centralized CSI
assumption. This paper tackles this challenging scenario and presents a first
analysis of the rate performance for the distributed CSI multi-TX broadcast
channel setting, in the large number of antenna regime. Using Random Matrix
Theory (RMT) tools, we derive deterministic equivalents of the Signal to
Interference plus Noise Ratio (SINR) for the popular regularized Zero-Forcing
(ZF) precoder, allowing to unveil the price of distributedness for such
cooperation methods.Comment: Extended version of an ISIT 2015 submission. Addition of the proofs
omitted due to space constrain
Degrees of Freedom of Certain Interference Alignment Schemes with Distributed CSIT
In this work, we consider the use of interference alignment (IA) in a MIMO
interference channel (IC) under the assumption that each transmitter (TX) has
access to channel state information (CSI) that generally differs from that
available to other TXs. This setting is referred to as distributed CSIT. In a
setting where CSI accuracy is controlled by a set of power exponents, we show
that in the static 3-user MIMO square IC, the number of degrees-of-freedom
(DoF) that can be achieved with distributed CSIT is at least equal to the DoF
achieved with the worst accuracy taken across the TXs and across the
interfering links. We conjecture further that this represents exactly the DoF
achieved. This result is in strong contrast with the centralized CSIT
configuration usually studied (where all the TXs share the same, possibly
imperfect, channel estimate) for which it was shown that the DoF achieved at
receiver (RX) i is solely limited by the quality of its own feedback. This
shows the critical impact of CSI discrepancies between the TXs, and highlights
the price paid by distributed precoding.Comment: This is an extended version of a conference submission which will be
presented at the IEEE conference SPAWC, Darmstadt, June 201
Low-bit rate feedback strategies for iterative IA-precoded MIMO-OFDM-based systems
Interference alignment (IA) is a promising technique that allows high-capacity gains in interference channels, but which requires the knowledge of the channel state information (CSI) for all the system links. We design low-complexity and low-bit rate feedback strategies where a quantized version of some CSI parameters is fed back from the user terminal (UT) to the base station (BS), which shares it with the other BSs through a limited-capacity backhaul network. This information is then used by BSs to perform the overall IA design. With the proposed strategies, we only need to send part of the CSI information, and this can even be sent only once for a set of data blocks transmitted over time-varying channels. These strategies are applied to iterative MMSE-based IA techniques for the downlink of broadband wireless OFDM systems with limited feedback. A new robust iterative IA technique, where channel quantization errors are taken into account in IA design, is also proposed and evaluated. With our proposed strategies, we need a small number of quantization bits to transmit and share the CSI, when comparing with the techniques used in previous works, while allowing performance close to the one obtained with perfect channel knowledge
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
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