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Register a new userEfficient Soft-Input Soft-Output Tree Detection Via an Improved Path Metric
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Added by
Jun Won Choi
Publication date
2011
fields
Informatics Engineering
and research's language is
English
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Tree detection techniques are often used to reduce the complexity of a posteriori probability (APP) detection in high dimensional multi-antenna wireless communication systems. In this paper, we introduce an efficient soft-input soft-output tree detection algorithm that employs a new type of look-ahead path metric in the computation of its branch pruning (or sorting). While conventional path metrics depend only on symbols on a visited path, the new path metric accounts for unvisited parts of the tree in advance through an unconstrained linear estimator and adds a bias term that reflects the contribution of as-yet undecided symbols. By applying the linear estimate-based look-ahead path metric to an M-algorithm that selects the best M paths for each level of the tree we develop a new soft-input soft-output tree detector, called an improved soft-input soft-output M-algorithm (ISS-MA). Based on an analysis of the probability of correct path loss, we show that the improved path metric offers substantial performance gain over the conventional path metric. We also demonstrate through simulations that the ISS-MA provides a better performance-complexity trade-off than existing soft-input soft-output detection algorithms.
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This paper investigates the soft covering lemma under both the relative entropy and the total variation distance as the measures of deviation. The exact order of the expected deviation of the random i.i.d. code for the soft covering problem problem, is determined. The proof technique used in this paper significantly differs from the previous techniques for deriving exact exponent of the soft covering lemma. The achievability of the exact order follows from applying the change of measure trick (which has been broadly used in the large deviation) to the known one-shot bounds in the literature. For the ensemble converse, some new inequalities of independent interest derived and then the change of measure trick is applied again. The exact order of the total variation distance is similar to the exact order of the error probability, thus it adds another duality between the channel coding and soft covering. Finally, The results of this paper are valid for any memoryless channels, not only channels with finite alphabets.
A simple line network model is proposed to study the downlink cellular network. Without base station cooperation, the system is interference-limited. The interference limitation is overcome when the base stations are allowed to jointly encode the user signals, but the capacity-achieving dirty paper coding scheme can be too complex for practical implementation. A new linear precoding technique called soft interference nulling (SIN) is proposed, which performs at least as well as zero-forcing (ZF) beamforming under full network coordination. Unlike ZF, SIN allows the possibility of but over-penalizes interference. The SIN precoder is computed by solving a convex optimization problem, and the formulation is extended to multiple-antenna channels. SIN can be applied when only a limited number of base stations cooperate; it is shown that SIN under partial network coordination can outperform full network coordination ZF at moderate SNRs.
Reed-Muller (RM) codes are conjectured to achieve the capacity of any binary-input memoryless symmetric (BMS) channel, and are observed to have a comparable performance to that of random codes in terms of scaling laws. On the negative side, RM codes lack efficient decoders with performance close to that of a maximum likelihood decoder for general parameters. Also, they only admit certain discrete sets of rates. In this paper, we focus on subcodes of RM codes with flexible rates that can take any code dimension from 1 to n, where n is the blocklength. We first extend the recursive projection-aggregation (RPA) algorithm proposed recently by Ye and Abbe for decoding RM codes. To lower the complexity of our decoding algorithm, referred to as subRPA in this paper, we investigate different ways for pruning the projections. We then derive the soft-decision based version of our algorithm, called soft-subRPA, that is shown to improve upon the performance of subRPA. Furthermore, it enables training a machine learning (ML) model to search for textit{good} sets of projections in the sense of minimizing the decoding error rate. Training our ML model enables achieving very close to the performance of full-projection decoding with a significantly reduced number of projections. For instance, our simulation results on a (64,14) RM subcode show almost identical performance for full-projection decoding and pruned-projection decoding with 15 projections picked via training our ML model. This is equivalent to lowering the complexity by a factor of more than 4 without sacrificing the decoding performance.
Polar codes are a class of {bf structured} channel codes proposed by Ar{i}kan based on the principle of {bf channel polarization}, and can {bf achieve} the symmetric capacity of any Binary-input Discrete Memoryless Channel (B-DMC). The Soft CANcellation (SCAN) is a {bf low-complexity} {bf iterative} decoding algorithm of polar codes outperforming the widely-used Successive Cancellation (SC). Currently, in most cases, it is assumed that channel state is perfectly {bf known} at the decoder and remains {bf constant} during each codeword, which, however, is usually unrealistic. To decode polar codes for {bf slowly-varying} channel with {bf unknown} state, on the basis of SCAN, we propose the Weighted-Window SCAN (W$^2$SCAN). Initially, the decoder is seeded with a coarse estimate of channel state. Then after {bf each} SCAN iteration, the decoder progressively refines the estimate of channel state with the {bf quadratic programming}. The experimental results prove the significant superiority of W$^2$SCAN to SCAN and SC. In addition, a simple method is proposed to verify the correctness of SCAN decoding which requires neither Cyclic Redundancy Check (CRC) checksum nor Hash digest.
Next generation wireless systems will need higher spectral efficiency as the expected traffic volumes per unit bandwidth and dimension will inevitably grow. As a consequence, it is necessary to design coding schemes with performances close to the theoretical limits, having high flexibility and low complexity requirements at transmitter and receiver. In this paper, we point out some of the limitations of the Bit Interleaved Code Modulation (BICM) technique which is the state of the art adopted in several standards and then propose some new lower complexity alternatives. These low complexity alternatives are obtained by applying the recently introduced Analog Digital Belief Propagation (ADBP) algorithm to a two stage encoding scheme embedding a hard decoding stage. First we show that for PAM$^2$ type constellations over the AWGN channel, the performance loss caused by using a hard decoded stage for all modulation bits except the two least protected is negligible. Next, we consider the application of two stage decoders to more challenging Rician channels, showing that in this case the number of bits needed to be soft decoded depends on the Rician factor and increases to a maximum of three bits per dimension for the Rayleigh channel. Finally, we apply the ADBP algorithm to further reduce the detection and decoding complexity.
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