Communication under Strong Asynchronism

We consider asynchronous communication over point-to-point discrete memoryless channels. The transmitter starts sending one block codeword at an instant that is uniformly distributed within a certain time period, which represents the level of asynchronism. The receiver, by means of a sequential decoder, must isolate the message without knowing when the codeword transmission starts but being cognizant of the asynchronism level A. We are interested in how quickly can the receiver isolate the sent message, particularly in the regime where A is exponentially larger than the codeword length N, which we refer to as `strong asynchronism.' This model of sparse communication may represent the situation of a sensor that remains idle most of the time and, only occasionally, transmits information to a remote base station which needs to quickly take action. The first result shows that vanishing error probability can be guaranteed as N tends to infinity while A grows as Exp(N*k) if and only if k does not exceed the `synchronization threshold,' a constant that admits a simple closed form expression, and is at least as large as the capacity of the synchronized channel. The second result is the characterization of a set of achievable strictly positive rates in the regime where A is exponential in N, and where the rate is defined with respect to the expected delay between the time information starts being emitted until the time the receiver makes a decision. As an application of the first result we consider antipodal signaling over a Gaussian channel and derive a simple necessary condition between A, N, and SNR for achieving reliable communication.Comment: 26 page

Optimal Sequential Frame Synchronization

We consider the `one-shot frame synchronization problem' where a decoder wants to locate a sync pattern at the output of a channel on the basis of sequential observations. We assume that the sync pattern of length N starts being emitted at a random time within some interval of size A, that characterizes the asynchronism level between the transmitter and the receiver. We show that a sequential decoder can optimally locate the sync pattern, i.e., exactly, without delay, and with probability approaching one as N tends to infinity, if and only if the asynchronism level grows as O(exp(N*k)), with k below the `synchronization threshold,' a constant that admits a simple expression depending on the channel. This constant is the same as the one that characterizes the limit for reliable asynchronous communication, as was recently reported by the authors. If k exceeds the synchronization threshold, any decoder, sequential or non-sequential, locates the sync pattern with an error that tends to one as N tends to infinity. Hence, a sequential decoder can locate a sync pattern as well as the (non-sequential) maximum likelihood decoder that operates on the basis of output sequences of maximum length A+N-1, but with much fewer observations.Comment: 6 page

Energy and Sampling Constrained Asynchronous Communication

The minimum energy, and, more generally, the minimum cost, to transmit one bit of information has been recently derived for bursty communication when information is available infrequently at random times at the transmitter. This result assumes that the receiver is always in the listening mode and samples all channel outputs until it makes a decision. If the receiver is constrained to sample only a fraction f>0 of the channel outputs, what is the cost penalty due to sparse output sampling? Remarkably, there is no penalty: regardless of f>0 the asynchronous capacity per unit cost is the same as under full sampling, ie, when f=1. There is not even a penalty in terms of decoding delay---the elapsed time between when information is available until when it is decoded. This latter result relies on the possibility to sample adaptively; the next sample can be chosen as a function of past samples. Under non-adaptive sampling, it is possible to achieve the full sampling asynchronous capacity per unit cost, but the decoding delay gets multiplied by 1/f. Therefore adaptive sampling strategies are of particular interest in the very sparse sampling regime.Comment: Submitted to the IEEE Transactions on Information Theor

A Simple Message-Passing Algorithm for Compressed Sensing

We consider the recovery of a nonnegative vector x from measurements y = Ax, where A is an m-by-n matrix whos entries are in {0, 1}. We establish that when A corresponds to the adjacency matrix of a bipartite graph with sufficient expansion, a simple message-passing algorithm produces an estimate \hat{x} of x satisfying ||x-\hat{x}||_1 \leq O(n/k) ||x-x(k)||_1, where x(k) is the best k-sparse approximation of x. The algorithm performs O(n (log(n/k))^2 log(k)) computation in total, and the number of measurements required is m = O(k log(n/k)). In the special case when x is k-sparse, the algorithm recovers x exactly in time O(n log(n/k) log(k)). Ultimately, this work is a further step in the direction of more formally developing the broader role of message-passing algorithms in solving compressed sensing problems

Training-Based Schemes are Suboptimal for High Rate Asynchronous Communication

We consider asynchronous point-to-point communication. Building on a recently developed model, we show that training based schemes, i.e., communication strategies that separate synchronization from information transmission, perform suboptimally at high rate.Comment: To appear in the proceedings of the 2009 IEEE Information Theory Workshop (Taormina

Update-Efficiency and Local Repairability Limits for Capacity Approaching Codes

Motivated by distributed storage applications, we investigate the degree to which capacity achieving encodings can be efficiently updated when a single information bit changes, and the degree to which such encodings can be efficiently (i.e., locally) repaired when single encoded bit is lost. Specifically, we first develop conditions under which optimum error-correction and update-efficiency are possible, and establish that the number of encoded bits that must change in response to a change in a single information bit must scale logarithmically in the block-length of the code if we are to achieve any nontrivial rate with vanishing probability of error over the binary erasure or binary symmetric channels. Moreover, we show there exist capacity-achieving codes with this scaling. With respect to local repairability, we develop tight upper and lower bounds on the number of remaining encoded bits that are needed to recover a single lost bit of the encoding. In particular, we show that if the code-rate is $\epsilon$ less than the capacity, then for optimal codes, the maximum number of codeword symbols required to recover one lost symbol must scale as $\log1/\epsilon$. Several variations on---and extensions of---these results are also developed.Comment: Accepted to appear in JSA

Iterative algorithms for lossy source coding

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 65-68).This thesis explores the problems of lossy source coding and information embedding. For lossy source coding, we analyze low density parity check (LDPC) codes and low density generator matrix (LDGM) codes for quantization under a Hamming distortion. We prove that LDPC codes can achieve the rate-distortion function. We also show that the variable node degree of any LDGM code must become unbounded for these codes to come arbitrarily close to the rate-distortion bound. For information embedding, we introduce the double-erasure information embedding channel model. We develop capacity-achieving codes for the double-erasure channel model. Furthermore, we show that our codes can be efficiently encoded and decoded using belief propagation techniques. We also discuss a generalization of the double-erasure model which shows that the double-erasure model is closely related to other models considered in the literature.by Venkat Chandar.M.Eng.and S.B