Search

The max-log-MAP (MLM) receiver is an approximated version of the well-known Bahl–Cocke–Jelinek-Raviv algorithm. The MLM algorithm is attractive due to its implementation simplicity. In practice, sliding-window implementations are preferred, whereby truncated signaling neighborhoods (around each transmission time instant) are considered. In this paper, we consider binary signaling sliding-window MLM receivers, where the MLM detector is truncated to a length-m signaling neighborhood. Here, truncation is used to ease the burden of analysis. For any number n of chosen times instants, we derive exact expressions for both 1) the joint distribution of the MLM symbol reliabilities, and 2) the joint probability of the erroneous MLM symbol detections. We show that the obtained expressions can be efficiently evaluated using Monte–Carlo techniques. The most computationally expensive operation (in each Monte–Carlo trial) is an eigenvalue decomposition of a size 2mn \times 2mn matrix. The proposed method handles various scenarios such as correlated noise distributions, modulation coding, etc. [ABSTRACT FROM PUBLISHER]

Noncontrollable finite-state channels (FSCs) are FSCs in which the channel inputs have no influence on the channel states, i.e., the channel states evolve freely. Since single-letter formulas for the channel capacities are rarely available for general noncontrollable FSCs, computable bounds are usually utilized to numerically bound the capacities. In this paper, we take the delayed channel state as part of the channel input and then define the directed information rate from the new channel input (including the source and the delayed channel state) sequence to the channel output sequence. With this technique, we derive a series of upper bounds on the capacities of noncontrollable FSCs with/without feedback. These upper bounds can be achieved by conditional Markov sources and computed by solving an average reward per stage stochastic control problem (ARSCP) with a compact state space and a compact action space. By showing that the ARSCP has a uniformly continuous reward function, we transform the original ARSCP into a finite-state and finite-action ARSCP that can be solved by a value iteration method. Under a mild assumption, the value iteration algorithm is convergent and delivers a near-optimal stationary policy and a numerical upper bound. [ABSTRACT FROM PUBLISHER]

We establish area theorems for iterative detection and decoding (or simply, iterative detection) over coded linear systems, including multiple-input multiple-output channels, intersymbol interference channels, and orthogonal frequency-division multiplexing systems. We propose a linear precoding technique that asymptotically ensures the Gaussianness of the messages passed in iterative detection, as the transmission block length tends to infinity. Area theorems are established to characterize the behavior of the iterative receiver. We show that, for unconstrained signaling, the proposed single-code scheme with linear precoding and iterative linear minimum mean-square error (LMMSE) detection is potentially information lossless, under various assumptions on the availability of the channel state information at the transmitter. We further show that, for constrained signaling, our proposed single-code scheme considerably outperforms the conventional multicode parallel transmission scheme based on singular value decomposition and water-filling power allocation. Numerical results are provided to verify our analysis. [ABSTRACT FROM PUBLISHER]