Orthogonal Multiple Access with Correlated Sources: Feasible Region and Pragmatic Schemes

In this paper, we consider orthogonal multiple access coding schemes, where correlated sources are encoded in a distributed fashion and transmitted, through additive white Gaussian noise (AWGN) channels, to an access point (AP). At the AP, component decoders, associated with the source encoders, iteratively exchange soft information by taking into account the source correlation. The first goal of this paper is to investigate the ultimate achievable performance limits in terms of a multi-dimensional feasible region in the space of channel parameters, deriving insights on the impact of the number of sources. The second goal is the design of pragmatic schemes, where the sources use "off-the-shelf" channel codes. In order to analyze the performance of given coding schemes, we propose an extrinsic information transfer (EXIT)-based approach, which allows to determine the corresponding multi-dimensional feasible regions. On the basis of the proposed analytical framework, the performance of pragmatic coded schemes, based on serially concatenated convolutional codes (SCCCs), is discussed

Feedback Power Control Strategies in Wireless Sensor Networks with Joint Channel Decoding

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as “balanced SNR” and “unbalanced SNR,” respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm

Iterative joint channel decoding of correlated sources

In this article we exploit the potential correlation existing between multiple information sources to achieve additional coding gains from the channel codes used for data protection. We do not assume the existence of, nor do we use channel sideinformation at the receiver. Instead, empirical estimates of the cross-correlation are used in partial decoding steps in an iterative joint soft decoding paradigm. Experimental results suggest that relatively few iterations (2 to 4) are sufficient to reap significant gains using this approach specially when the sources are highly correlated. Finally, we provide analytical performance bounds of the proposed technique showing a close match with the simulation results at sufficiently high SNR

Iterative Joint Channel Decoding of Correlated Sources Employing Serially Concatenated Convolutional Codes

This correspondence looks at the problem of joint decoding of serially concatenated convolutional codes (SCCCs) used for channel coding of multiple correlated sources. We assume a simple model whereby two correlated sources transmit SCCC encoded data to a single destination receiver. We do not assume the existence of, nor do we use channel side information at the receiver. In particular, we present a novel iterative joint channel decoding algorithm for correlated sources by using the empirical cross-correlation measurements at successive decoding iterations to provide extrinsic information to the outer codes of the SCCC configuration. Two levels of soft metric iterative decoding are used at the receiver: 1) iterative maximum a posteriori probability (MAP) decoding is used for efficient decoding of individual SCCC codes (local iterations) and 2) iterative extrinsic information feedback generated from the estimates of the empirical cross correlation in partial decoding steps is used to pass soft information to the outer decoders of the global joint SCCC decoder (global iterations). We provide analytical results followed by simulation studies confirming the robustness of the cross-correlation estimates to channel-induced errors, justifying the use of such estimates in iterative decoding. Experimental results suggest that relatively few global iterations (two to five) during which multiple local iterations are conducted are sufficient to reap significant gains using this approach specially when the sources are highly correlated

Iterative Joint Channel Decoding of Correlated Sources Employing Serially Concatenated Convolutional Codes

This correspondence looks at the problem of joint decoding of serially concatenated convolutional codes (SCCCs) used for channel coding of multiple correlated sources.W e assume a simple model whereby two correlated sources transmit SCCC encoded data to a single destination receiver. W e do not assume the existence of, nor do we use channel side information at the receiver.In particular, we present a novel iterative joint channel decoding algorithm for correlated sources by using the empirical cross-correlation measurements at successive decoding iterations to provide extrinsic information to the outer codes of the SCCC configuration. Two levels of soft metric iterative decoding are used at the receiver: 1) iterative maximum a posteriori probability (MAP) decoding is used for efficient decoding of individual SCCC codes (local iterations) and 2) iterative extrinsic information feedback generated from the estimates of the empirical cross correlation in partial decoding steps is used to pass soft information to the outer decoders of the global joint SCCC decoder (global iterations).W e provide analytical results followed by simulation studies confirming the robustness of the cross-correlation estimates to channel-induced errors, justifying the use of such estimates in iterative decoding.Experimental results suggest that relatively few global iterations (two to five) during which multiple local iterations are conducted are sufficient to reap significant gains using this approach specially when the sources are highly correlated