arXiv subjects

Mojtaba Vaezi

Publications and source records attributed to Mojtaba Vaezi.

47 records · Page 3Linked to original sources

arXiv2015

The Capacity of Mixed and One-Sided Gaussian Interference Channels

This paper adds to the understanding of the capacity region of the Gaussian interference channel. To this end, the capacity region of the one-sided Gaussian interference channel is first fully characterized. This is accomplished by introducing a new representation of the Han-Kobayashi region and providing a new outer bound on the capacity region of this channel which is tight both in the weak and strong interference regimes. In light of this capacity result, the capacity region of the degraded Gaussian interference channel is established immediately. Next, by combining the capacity regions of the one-sided interference channels in the weak and strong interference regimes, new outer bounds on the capacity region of the interference channel are introduced, in various interference regimes. It is then proved that the outer bound corresponding to the mixed interference regime, in which one of the receivers is subject to strong interference while the other one suffers from weak interference, is tight in a broad range of this regime. The new capacity results, on the whole, confirm the optimality of decoding part of the interference and treating the rest as noise. The optimum amount to be decoded varies from 0 to 100% of the interfering signal, depending on the relative importance of the users' rates (i.e., the ratio of weights in the weighted sum-rate), their transmission powers, and the gain of the weak interference link. Optimal values are found explicitly, based on the above parameters.

cs.IT↗

arXiv2015

The Capacity Region of the One-Sided Gaussian Interference Channel

The capacity region of the one-sided Gaussian interference channel is established in the weak interference regime. To characterize this region, a new representation of the Han-Kobayashi inner bound for the one-sided Gaussian interference channel is first given. Next, a new outer bound on the capacity region of this channel is introduced which is tight in the weak interference regime. This is the first capacity region for any variant of the interference channel in the weak interference regime.

cs.IT↗

arXiv2014

The Capacity of More Capable Cognitive Interference Channels

We establish the capacity region for a class of discrete memoryless cognitive interference channel (DM-CIC) called cognitive-more-capable channel, and we show that superposition coding is the optimal encoding technique. This is the largest capacity region for the DM-CIC to date, as the existing capacity results are explicitly shown to be its subsets.

cs.IT↗

arXiv2013

Systematic DFT Frames: Principle, Eigenvalues Structure, and Applications

Motivated by a host of recent applications requiring some amount of redundancy, frames are becoming a standard tool in the signal processing toolbox. In this paper, we study a specific class of frames, known as discrete Fourier transform (DFT) codes, and introduce the notion of systematic frames for this class. This is encouraged by a new application of frames, namely, distributed source coding that uses DFT codes for compression. Studying their extreme eigenvalues, we show that, unlike DFT frames, systematic DFT frames are not necessarily tight. Then, we come up with conditions for which these frames can be tight. In either case, the best and worst systematic frames are established in the minimum mean-squared reconstruction error sense. Eigenvalues of DFT frames and their subframes play a pivotal role in this work. Particularly, we derive some bounds on the extreme eigenvalues DFT subframes which are used to prove most of the results; these bounds are valuable independently.

cs.IT↗

arXiv2013

Extended Subspace Error Localization for Rate-Adaptive Distributed Source Coding

A subspace-based approach for rate-adaptive distributed source coding (DSC) based on discrete Fourier transform (DFT) codes is developed. Punctured DFT codes can be used to implement rate-adaptive source coding, however they perform poorly after even moderate puncturing since the performance of the subspace error localization degrades severely. The proposed subspace-based error localization extends and improves the existing one, based on additional syndrome, and is naturally suitable for rate-adaptive distributed source coding architecture.

cs.IT↗

arXiv2013

Wyner-Ziv Coding in the Real Field Based on BCH-DFT Codes

We show how real-number codes can be used to compress correlated sources and establish a new framework for distributed lossy source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and compensate for the quantization error when the sources are completely correlated. We focus on the asymmetric case, i.e., lossy source coding with side information at the decoder, also known as Wyner-Ziv coding. The encoding and decoding procedures are described in detail for discrete Fourier transform (DFT) codes, both for syndrome- and parity-based approaches. We also extend the parity-based approach to the case where the transmission channel is noisy and perform distributed joint source-channel coding in this context. The proposed system is well suited for low-delay communications. Furthermore, the mean-squared reconstruction error (MSE) is shown to be less than or close to the quantization error level, the ideal case in coding based on binary codes.

cs.IT↗

arXiv2012

Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC

Accurate modeling of the correlation between the sources plays a crucial role in the efficiency of distributed source coding (DSC) systems. This correlation is commonly modeled in the binary domain by using a single binary symmetric channel (BSC), both for binary and continuous-valued sources. We show that "one" BSC cannot accurately capture the correlation between continuous-valued sources; a more accurate model requires "multiple" BSCs, as many as the number of bits used to represent each sample. We incorporate this new model into the DSC system that uses low-density parity-check (LDPC) codes for compression. The standard Slepian-Wolf LDPC decoder requires a slight modification so that the parameters of all BSCs are integrated in the log-likelihood ratios (LLRs). Further, using an interleaver the data belonging to different bit-planes are shuffled to introduce randomness in the binary domain. The new system has the same complexity and delay as the standard one. Simulation results prove the effectiveness of the proposed model and system.

cs.IT↗

arXiv2012

The Capacity of Less Noisy Cognitive Interference Channels

Fundamental limits of the cognitive interference channel (CIC) with two pairs of transmitter-receiver has been under exploration for several years. In this paper, we study the discrete memoryless cognitive interference channel (DM-CIC) in which the cognitive transmitter non-causally knows the message of the primary transmitter. The capacity of this channel is not known in general; it is only known in some special cases. Inspired by the concept of less noisy broadcast channel (BC), in this work we introduce the notion of less noisy cognitive interference channel. Unlike BC, due to the inherent asymmetry of the cognitive channel, two different less noisy channels are distinguishable; these are named the primary-less-noisy and cognitive-less-noisy channels. We derive capacity region for the latter case, by introducing inner and outer bounds on the capacity of the DM-CIC and showing that these bounds coincide for the cognitive-less-noisy channel. Having established the capacity region, we prove that superposition coding is the optimal encoding technique.

cs.IT↗

arXiv2012

Distributed Lossy Source Coding Using Real-Number Codes

We show how real-number codes can be used to compress correlated sources, and establish a new framework for lossy distributed source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and correct quantization error when the sources are completely correlated. The encoding and decoding procedures are described in detail, for discrete Fourier transform (DFT) codes. Reconstructed signal, in the mean squared error sense, is seen to be better than that in the conventional approach.

cs.IT↗

arXiv2012

Systematic DFT Frames: Principle and Eigenvalues Structure

Motivated by a host of recent applications requiring some amount of redundancy, frames are becoming a standard tool in the signal processing toolbox. In this paper, we study a specific class of frames, known as discrete Fourier transform (DFT) codes, and introduce the notion of systematic frames for this class. This is encouraged by application of systematic DFT codes in distributed source coding using DFT codes, a new application for frames. Studying their extreme eigenvalues, we show that, unlike DFT frames, systematic DFT frames are not necessarily tight. Then, we come up with conditions for which these frames can be tight. In either case, the best and worst systematic frames are established from reconstruction error point of view. Eigenvalues of DFT frames, and their subframes, play a pivotal role in this work.

cs.IT↗

arXiv2011

Superposition Coding-Based Bounds and Capacity for the Cognitive Z-Interference Channels

This paper considers the cognitive interference channel (CIC) with two transmitters and two receivers, in which the cognitive transmitter non-causally knows the message and codeword of the primary transmitter. We first introduce a discrete memoryless more capable CIC, which is an extension to the more capable broadcast channel (BC). Using superposition coding, we propose an inner bound and an outer bound on its capacity region. The outer bound is also valid when the primary user is under strong interference. For the Gaussian CIC, this outer bound applies for $|a| \geq 1 $, where $a$ is the gain of interference link from secondary user to primary receiver. These capacity inner and outer bounds are then applied to the Gaussian cognitive Z-interference channel (GCZIC) where only the primary receiver suffers interference. Upon showing that jointly Gaussian input maximizes these bounds for the GCZIC, we evaluate the bounds for this channel. The new outer bound is strictly tighter than other outer bounds on the capacity of the GCZIC at strong interference ($a^2 \geq 1 $). Especially, the outer bound coincides with the inner bound for $|a| \geq \sqrt{1 + P_1}$ and thus, establishes the capacity of the GCZIC at this range. For such a large $a$, superposition encoding at the cognitive transmitter and successive decoding at the primary receiver are capacity-achieving.

cs.IT↗