This paper proposes a practical successive decoding scheme with finite levels for the finite-state Markov channels (FSMCs) where there is no a priori state information at the transmitter or the receiver. The design employs either a random interleaver or a deterministic interleaver with an irregular pattern and an optional iterative estimation and decoding procedure within each level. The interleaver design criteria may be the achievable rate or the extrinsic information transfer (EXIT) chart, depending on the receiver type. For random interleavers, the optimization problem is solved efficiently using a pilot-utility function, while for deterministic interleavers, a good construction is given using empirical rules. Simulation results demonstrate that the new successive decoding scheme combined with irregular low-density parity-check (LDPC) codes can approach the identically and uniformly distributed (i.u.d.) input capacity on the Markov-fading channel using only a few levels.

This paper proposes a multilevel coding scheme with linear mapping for intersymbol interference (ISI) channels and derives a low-complexity receiver structure that can achieve the ISI channel capacity. The transmitter superimposes many layers of independent binary antipodal streams to generate a quadrature amplitude modulation (QAM) or Gaussian-like channel input. The receiver performs multistage decoding with decision feedback and interference cancellation. Within each stage is a linear minimum mean-square-error (MMSE) equalizer followed by an error-correcting decoder. The complexity scales linearly with the channel length and the number of layers, and the process is shown to be asymptotically information lossless if a fixed input power is properly distributed over a sufficiently large number of layers. This framework is then extended to achieve the capacity of the ISI channel using a transmitter-side spectral shaping filter that converts a Gaussian input sequence with a white spectrum to one with a water-filling spectrum.

This paper presents a new technique for communication over channels with memory where the channel state is unknown at the transmitter and receiver. A deep interleaver combined with successive decoding decomposes a channel with memory into an array of parallel memoryless channels on which a conventional coding system can operate individually. The problems of joint channel estimation and decoding thus are separated without loss of capacity. This technique achieves channel capacity and so may be used to evaluate the capacities of different channels. A general information-theoretic framework is developed and applied to intersymbol interference (ISI), finite-state Markov, and Rayleigh-fading channels. A full system implementation, which performs within 1.1 dB of the channel capacity upper bound, is presented for the Rayleigh-fading channel

A receiver structure that uses decision feedback and successive decoding is proposed for a noncoherent channel with independent block fading. The transceiver employs a block interleaver to decompose the channel into a set of independent fading sub-channels. LDPC codes are used on each sub-channel, and the decoded data are fed back to help in channel estimation. Simulation results indicate that the performance of the proposed system is within 0.7 dB of the Shannon capacity of the binary-input block fading channel.

This paper proposes novel successive decoding paradigm for fading channels, K independent codewords of different rates and lengths are interleaved at the transmitter according to a pattern, and successively decoded at the receiver. This enables progressively better channel estimates for the receiver, at a minimal cost of pilot symbols. At each decoding stage, channel estimation and decoding are performed separately. or iteratively if the residual channel correlation within a codeword can be exploited. The flexible interleaver pattern, after some level of optimization, achieves near-capacity system performance with only a small number of codewords K, as demonstrated numerically by a finite state Markov modelled flat fading channel

This paper proves that low density parity check (LDPC) codes are universal codes on correlated fading channels if a successive decoding receiver is used. A universal LDPC code is defined as a code with the same performance over a class of channels, in which the performance is measured by the threshold of the code in terms of mutual information (in bits/sec/Hz). The receiver proposed in this paper decomposes the fading channel into a bank of memoryless sub-channels. Each sub-channel is encoded with a LDPC code. An MMSE estimator followed by a LDPC decoder is used to successively decodes the channel code. With this scheme, we show that LDPC codes have the universal performance on fading channels with variant fading rates. We also prove that the LDPC code design is unified. Hence, the optimal LDPC codes for the perfect CSI receiver is also optimal for correlated fading channels without CSI.

This paper presents both an information lossless coding scheme and a method to evaluate constrained capacity for channels with memory and unknown state. The fundamental idea is to decompose the original channel into a bank of memoryless sub-channels with partially known states, then successively decode these sub-channels. The receiver of each sub-channel consists of an optimal estimator followed by a memoryless channel decoder. The coding scheme translates the codes and decoders designed for memoryless channels with near capacity performance to channels with memory. The results are applied to both finite state Markov channels and correlated flat fading channels

This paper presents a new coding scheme with capacity approaching performance for correlated fading channels with unknown state. The transceiver employs a deep interleaver to decompose the original channel into a bank of independent fading sub-channels. Sub-channels are successively decoded and decisions are fed back as new training symbols. The unknown state of each sub-channel can be estimated from past channel observations and future channel outputs. We show that the sub-channel has the same capacity as the original channel, given a sufficiently large estimation window. Therefore, the correlated fading channel capacity can be approached if each sub-channel uses an optimized capacity achieving code. Simulations show a universal performance within 1 dB of capacity upper bound regardless of fading rate. The scheme is also robust against decision feedback errors when the sub-channels use a long LDPC code

This paper presents a method for calculating arbitrarily tight upper and lower bounds on the capacity of a flat fading channel with M-ary inputs, where the fading process is modelled as a Gaussian random process with any spectral density. Neither the transmitter nor the receiver knows the channel state. The paper also presents a practicable method for adapting any family of codes designed for i.i.d. channels to work on correlated fading channels. Thus, the near-capacity performance of existing codes with iterative decoders can be translated directly to fading channels.

This paper describes a location system using an asynchronous distributed sensor network. The sensors' clocks operate independently and are not explicitly synchronized. The motion of the source, or the object, between two emitted pulses will cause the inter-arrival time of these two pulses at each sensor to vary. This inter-arrival time is a function of the source location and motion and is independent of the clock offsets among sensors. Thus, the independently calculated time-difference-of-arrival (TDOA) of successive pulses at each sensor provides a reliable source of information to estimate the source position as well as its motion. The Cramer-Rao lower bound (CRLB) is derived to assess the performance of the proposed method and to show that its performance can approach that of a synchronous network given a sufficient number of sensors and moderate source movement. Some sub-optimal but effective source trajectory tracking methods are proposed which further relax the assumption of source motion between consecutive pulses. The simulation results show that the proposed estimation and tracking methods are quite good.

An opportunistic transmission scheduling scheme is proposed to make better use of the multiuser diversity gain in a MIMO system. The performance of a MIMO link is quantified by its information-theoretic capacity. Even though the base station has no knowledge of the transmission channel, the proposed algorithm generates a transmission randomizing matrix according to the distribution of the channel, so that it is likely that some user is near the "water-filling" configuration. The mobile users measure and feedback the channel quality. Based on this information, the base station schedules transmission to the user whose instantaneous channel capacity is the largest. Simulation results show that, over slow varying channels, or over Rician channels with a large K factor, opportunistic transmission scheduling can improve system performance in terms of channel capacity.