Approximate MIMO Iterative Processing with Adjustable Complexity Requirements

Targeting always the best achievable bit error rate (BER) performance in iterative receivers operating over multiple-input multiple-output (MIMO) channels may result in significant waste of resources, especially when the achievable BER is orders of magnitude better than the target performance (e.g., under good channel conditions and at high signal-to-noise ratio (SNR)). In contrast to the typical iterative schemes, a practical iterative decoding framework that approximates the soft-information exchange is proposed which allows reduced complexity sphere and channel decoding, adjustable to the transmission conditions and the required bit error rate. With the proposed approximate soft information exchange the performance of the exact soft information can still be reached with significant complexity gains.Comment: The final version of this paper appears in IEEE Transactions on Vehicular Technolog

Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats

Space-time coding techniques with bit-interleaved coded modulations for MIMO block-fading channels

The space-time bit-interleaved coded modulation (ST-BICM) is an efficient technique to obtain high diversity and coding gain on a block-fading MIMO channel. Its maximum-likelihood (ML) performance is computed under ideal interleaving conditions, which enables a global optimization taking into account channel coding. Thanks to a diversity upperbound derived from the Singleton bound, an appropriate choice of the time dimension of the space-time coding is possible, which maximizes diversity while minimizing complexity. Based on the analysis, an optimized interleaver and a set of linear precoders, called dispersive nucleo algebraic (DNA) precoders are proposed. The proposed precoders have good performance with respect to the state of the art and exist for any number of transmit antennas and any time dimension. With turbo codes, they exhibit a frame error rate which does not increase with frame length.Comment: Submitted to IEEE Trans. on Information Theory, Submission: January 2006 - First review: June 200

A Fast Sphere Decoding Algorithm for Rank Deficient MIMO Systems

The problem of rank deficient multiple input multiple out (MIMO) systems arises when the number of transmit antennas M is greater than number of receive antennas N or when the channel gains are strongly correlated. Most of the optimal algorithms that deal with uncoded rank-deficient (under-determined) V-BLAST MIMO systems (e.g. Damen ,Meraim and Belfiore) suffer from high complexity and large processing time. Recently, some new optimal algorithms were introduced with low complexity for small constellations like 4-QAM yet they still suffer from very high complexity and processing time with large constellations like the 16 QAM. In order to reduce the complexity and the processing time of the decoding algorithms, some suboptimal algorithms were introduced. One of the most efficient suboptimal solutions for this problem is based on the Minimum mean square error decision-feedback equalizer (MMSE-DFE) followed by either sphere decoder or fano decoder. The performance of these algorithms is shown to be a fraction of dB from the maximum likelihood decoders while offering outstanding reduction in complexity compared to the most efficient ML algorithms (e.g. Cui and Tellambura algorithm). These suboptimal algorithms employ a two stage approach. In the first stage, the channel is pre-processed to transform the original decoding problem into a simpler form which facilitates the search decoding step. The second stage is basically the application of the sphere decoding search algorithm in the case of MMSE-DFE sphere decoding step or Fano decoder in the case of MMSE-DFE Fano decoder. In this study, various algorithms which deal with rank deficient MIMO systems such as Damen,Meraim and Belfiore algorithm ,Dayal and Varansi algorithm, and Cui and Tellambura algorithm are discussed and compared. Moreover, the MMSE-DFE sphere decoding algorithm and MMSE-DFE fano decoding algorithm are applied on uncoded V-BLAST rank deficient MIMO systems. The optimality of MMSE-DFE sphere decoding algorithm is analyzed in the case of V-BLAST 4-QAM. Furthermore, Simulation results show that when these algorithms are extended to cover large constellations, their performance falls within a fraction of dB behind the ML while achieving a significant decrease in the processing time by more than an order of magnitude when compared to the leas