Results 1  10
of
71
Capacity of Fading Channels with Channel Side Information
, 1997
"... We obtain the Shannon capacity of a fading channel with channel side information at the transmitter and receiver, and at the receiver alone. The optimal power adaptation in the former case is "waterpouring" in time, analogous to waterpouring in frequency for timeinvariant frequencysele ..."
Abstract

Cited by 579 (23 self)
 Add to MetaCart
We obtain the Shannon capacity of a fading channel with channel side information at the transmitter and receiver, and at the receiver alone. The optimal power adaptation in the former case is "waterpouring" in time, analogous to waterpouring in frequency for timeinvariant frequencyselective fading channels. Inverting the channel results in a large capacity penalty in severe fading.
Hidden Markov processes
 IEEE Trans. Inform. Theory
, 2002
"... Abstract—An overview of statistical and informationtheoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discretetime finitestate homogeneous Markov chain observed through a discretetime memoryless invariant channel. In recent years, the work of Baum and Petrie on finite ..."
Abstract

Cited by 259 (5 self)
 Add to MetaCart
(Show Context)
Abstract—An overview of statistical and informationtheoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discretetime finitestate homogeneous Markov chain observed through a discretetime memoryless invariant channel. In recent years, the work of Baum and Petrie on finitestate finitealphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximumlikelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed in this paper. Index Terms—Baum–Petrie algorithm, entropy ergodic theorems, finitestate channels, hidden Markov models, identifiability, Kalman filter, maximumlikelihood (ML) estimation, order estimation, recursive parameter estimation, switching autoregressive processes, Ziv inequality. I.
On coding for reliable communication over packet networks
, 2008
"... We consider the use of random linear network coding in lossy packet networks. In particular, we consider the following simple strategy: nodes store the packets that they receive and, whenever they have a transmission opportunity, they send out coded packets formed from random linear combinations of ..."
Abstract

Cited by 223 (37 self)
 Add to MetaCart
We consider the use of random linear network coding in lossy packet networks. In particular, we consider the following simple strategy: nodes store the packets that they receive and, whenever they have a transmission opportunity, they send out coded packets formed from random linear combinations of stored packets. In such a strategy, intermediate nodes perform additional coding yet do not decode nor wait for a block of packets before sending out coded packets. Moreover, all coding and decoding operations have polynomial complexity. We show that, provided packet headers can be used to carry an amount of sideinformation that grows arbitrarily large (but independently of payload size), random linear network coding achieves packetlevel capacity for both single unicast and single multicast connections and for both wireline and wireless networks. This result holds as long as packets received on links arrive according to processes that have average rates. Thus packet losses on links may exhibit correlations in time or with losses on other links. In the special case of Poisson traffic with i.i.d. losses, we give error exponents that quantify the rate of decay of the probability of error with coding delay. Our analysis of random linear network coding shows not only that it achieves packetlevel capacity, but also that the propagation of packets carrying “innovative ” information follows the propagation of jobs through a queueing network, thus implying that fluid flow models yield good approximations.
Reliable Communication Under Channel Uncertainty
 IEEE TRANS. INFORM. THEORY
, 1998
"... In many communication situations, the transmitter and the receiver must be designed without a complete knowledge of the probability law governing the channel over which transmission takes place. Various models for such channels and their corresponding capacities are surveyed. Special emphasis is pla ..."
Abstract

Cited by 175 (5 self)
 Add to MetaCart
(Show Context)
In many communication situations, the transmitter and the receiver must be designed without a complete knowledge of the probability law governing the channel over which transmission takes place. Various models for such channels and their corresponding capacities are surveyed. Special emphasis is placed on the encoders and decoders which enable reliable communication over these channels.
Simulationbased computation of information rates for channels with memory
 IEEE TRANS. INFORM. THEORY
, 2006
"... The information rate of finitestate source/channel models can be accurately estimated by sampling both a long channel input sequence and the corresponding channel output sequence, followed by a forward sum–product recursion on the joint source/channel trellis. This method is extended to compute up ..."
Abstract

Cited by 106 (11 self)
 Add to MetaCart
(Show Context)
The information rate of finitestate source/channel models can be accurately estimated by sampling both a long channel input sequence and the corresponding channel output sequence, followed by a forward sum–product recursion on the joint source/channel trellis. This method is extended to compute upper and lower bounds on the information rate of very general channels with memory by means of finitestate approximations. Further upper and lower bounds can be computed by reducedstate methods.
Recent and Emerging Topics in Wireless Industrial Communications: A Selection
, 2007
"... In this paper we discuss a selection of promising and interesting research areas in the design of protocols and systemsforwirelessindustrialcommunications.Wehaveselected topicsthathaveeitheremergedashottopicsintheindustrial communicationscommunityinthelastfewyears(likewireless sensornetworks),orwhi ..."
Abstract

Cited by 91 (1 self)
 Add to MetaCart
In this paper we discuss a selection of promising and interesting research areas in the design of protocols and systemsforwirelessindustrialcommunications.Wehaveselected topicsthathaveeitheremergedashottopicsintheindustrial communicationscommunityinthelastfewyears(likewireless sensornetworks),orwhichcouldbeworthwhileresearchtopicsin thenextfewyears(forexamplecooperativediversitytechniques for error control, cognitive radio/opportunistic spectrum access for mitigation of external interferences).
On the Achievable Information Rates of Finite State ISI Channels
, 2001
"... In this paper, we present two simple Monte Carlo methods for estimating the achievable information rates of general finite state channels. Both methods require only the ability to simulate the channel with an a posteriori probability (APP) detector matched to the channel. The first method estimates ..."
Abstract

Cited by 63 (13 self)
 Add to MetaCart
In this paper, we present two simple Monte Carlo methods for estimating the achievable information rates of general finite state channels. Both methods require only the ability to simulate the channel with an a posteriori probability (APP) detector matched to the channel. The first method estimates the mutual information rate between the input random process and the output random process, provided that both processes are stationary and ergodic. When the inputs are i.i.d. equiprobable, this rate is known as the Symmetric Information Rate (SIR). The second method estimates the achievable information rate of an explicit coding system which interleaves m independent codes onto the channel and employs multistage decoding. For practical values of m, numerical results show that this system nearly achieves the SIR. Both methods are applied to the class of partial response channels commonly used in magnetic recording.
Joint Iterative Channel Estimation and Decoding in Flat Correlated Rayleigh Fading
 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 2001
"... This paper addresses the design and performance evaluation with respect to capacity of MPSK turbocoded systems operating in frequencyflat timeselective Rayleigh fading. The receiver jointly performs channel estimation and turbo decoding, allowing the two processes to benefit from each other. To ..."
Abstract

Cited by 50 (1 self)
 Add to MetaCart
This paper addresses the design and performance evaluation with respect to capacity of MPSK turbocoded systems operating in frequencyflat timeselective Rayleigh fading. The receiver jointly performs channel estimation and turbo decoding, allowing the two processes to benefit from each other. To this end, we introduce a suitable Markov model with a finite number of states, designed to approximate both the values and the statistical properties of the correlated flat fading channel phase, which poses a more severe challenge to PSK transmission than amplitude fading. Then, the ForwardBackward algorithm determines both the maximum a posteriori probability (MAP) value for each symbol in the data sequence and the MAP channel phase in each iteration. Simulations show good performance in standard correlated Rayleigh fading channels. A sequence of progressively tighter upper bounds to the capacity of a simplified Markovphase channel is derived, and performance of a turbo code with joint iterative channel estimation and decoding is demonstrated to approach these capacity bounds.
FiniteState Markov Modeling of Fading Channels—A Survey of Principles and Applications
 IEEE Sign. Process. Mag
"... [A survey of principles and applications] © MASTER SERIES In late 1950s and early 1960s, Gilbert and Elliott at Bell Labs were modeling burstnoise telephone circuits with a very simple twostate channel model with memory. This simple model allowed them to evaluate channel capacity and error rate pe ..."
Abstract

Cited by 50 (5 self)
 Add to MetaCart
(Show Context)
[A survey of principles and applications] © MASTER SERIES In late 1950s and early 1960s, Gilbert and Elliott at Bell Labs were modeling burstnoise telephone circuits with a very simple twostate channel model with memory. This simple model allowed them to evaluate channel capacity and error rate performance through bursty wireline telephone circuits. However, it took another 30 years for the socalled GilbertElliott channel (GEC) and its generalized finitestate Markov channel (FSMC) to be applied in the design of secondgeneration (2G) wireless communication systems. Since the mid 1990s, the GEC and FSMC models have been widely used for modeling wireless flatfading channels in a variety of applications, ranging from modeling channel error bursts to decoding at the receiver. FSMC models are versatile, and with suitable choices of model parameters, can capture the essence of timevarying fading channels. This article’s goal is to provide an indepth understanding of the principles of FSMC modeling of fading channels with its applications in wireless communication systems. Digital Object Identifier 10.1109/MSP.2008.926683 10535888/08/$25.00©2008IEEE IEEE SIGNAL PROCESSING MAGAZINE [57] SEPTEMBER 2008While the emphasis is on frequency nonselective or flatfading channels, this understanding will be useful for future generalizations of FSMC models for frequencyselective fading channels. The target audience of this article include both theory and practiceoriented researchers who would like to design accurate channel models for evaluating the performance of wireless communication systems in the physical or media access control layers, or those who would like to develop more efficient and reliable transceivers that take advantage of the inherent memory in fading channels. Both FSMC models and flatfading channels will be formally introduced. However, a background in timevarying fading communication channels is beneficial.
Analysis of lowdensity paritycheck codes for the GilbertElliott channel
 IEEE TRANS. INF. THEORY
, 2005
"... Density evolution analysis of lowdensity paritycheck (LDPC) codes in memoryless channels is extended to the Gilbert–Elliott (GE) channel, which is a special case of a large class of channels with hidden Markov memory. In a procedure referred to as estimation decoding, the sum–product algorithm (S ..."
Abstract

Cited by 34 (8 self)
 Add to MetaCart
Density evolution analysis of lowdensity paritycheck (LDPC) codes in memoryless channels is extended to the Gilbert–Elliott (GE) channel, which is a special case of a large class of channels with hidden Markov memory. In a procedure referred to as estimation decoding, the sum–product algorithm (SPA) is used to perform LDPC decoding jointly with channelstate detection. Density evolution results show (and simulation results confirm) that such decoders provide a significantly enlarged region of successful decoding within the GE parameter space, compared with decoders that do not exploit the channel memory. By considering a variety of ways in which a GE channel may be degraded, it is shown how knowledge of the decoding behavior at a single point of the GE parameter space may be extended to a larger region within the space, thereby mitigating the large complexity needed in using density evolution to explore the parameter space pointbypoint. Using the GE channel as a straightforward example, we conclude that analysis of estimation decoding for LDPC codes is feasible in channels with memory, and that such analysis shows large potential gains.