Combined precoding for multiuser Multiple-Input Multiple-Output satellite communications

[EN] Applying Multiple-Input Multiple-Output (MIMO) techniques in satellite communications can increase data rates. However, new signal processing elements have to be taken into account to fully exploit the expected advantages of MIMO communications. In this paper, we evaluate different precoding techniques over the satellite channel. A performance comparison between several precoders in terms of Bit Error Rate (BER) and complexity is given for different channel realizations. Furthermore, a novel hybrid scheme for signal precoding is proposed that optimizes the computation for a required BER. The new scheme is based on the matrix condition number of the satellite MIMO channel.This work has been partially funded by the Spanish MINECO grant RACHEL TEC2013-47141-C4-4-R and through FPU AP-2012/71274.Simarro, MA.; Puig, B.; Martínez Zaldívar, FJ.; Gonzalez, A. (2018). Combined precoding for multiuser Multiple-Input Multiple-Output satellite communications. Computers & Electrical Engineering. 71:704-713. https://doi.org/10.1016/j.compeleceng.2018.08.006S7047137

Parallel SUMIS Soft Detector for Large MIMO Systems on Multicore and GPU

[EN] The number of transmit and receiver antennas is an important factor that affects the performance and complexity of a MIMO system. A MIMO system with very large number of antennas is a promising candidate technology for next generations of wireless systems. However, the vast majority of the methods proposed for conventional MIMO system are not suitable for large dimensions. In this context, the use of high-performance computing systems, such us multicore CPUs and graphics processing units has become attractive for efficient implementation of parallel signal processing algorithms with high computational requirements. In the present work, two practical parallel approaches of the Subspace Marginalization with Interference Suppression detector for large MIMO systems have been proposed. Both approaches have been evaluated and compared in terms of performance and complexity with other detectors for different system parameters.This work has been partially supported by the Spanish MINECO Grant RACHEL TEC2013-47141-C4-4-R, the PROMETEO FASE II 2014/003 Project and FPU AP-2012/71274Ramiro Sánchez, C.; Simarro, MA.; Gonzalez, A.; Vidal Maciá, AM. (2019). Parallel SUMIS Soft Detector for Large MIMO Systems on Multicore and GPU. The Journal of Supercomputing. 75(3):1256-1267. https://doi.org/10.1007/s11227-018-2403-9S12561267753Rusek F, Persson D, Lau BK, Larsson EG, Marzetta TL, Edfors O, Tufvesson F (2013) Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Proc Mag 30(1):40–60Studer C, Burg A, Bölcskei H (2008) Soft-output sphere decoding: algorithms and VLSI implementation. IEEE J Sel Areas Commun 26(2):290–300Wang R, Giannakis GB (2004) Approaching MIMO channel capacity with reduced-complexity soft sphere decoding. In: Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE vol 3, pp 1620–1625Persson D, Larsson EG (2011) Partial marginalization soft MIMO detection with higher order constellations. IEEE Trans Signal Procces 59(1):453–458Cîrkić M, Larsson EG (2014) SUMIS: near-optimal soft-in soft-out MIMO detection with low and fixed complexity. IEEE Trans Signal Process 62(12):3084–3097Alberto Gonzalez C, Ramiro, M, Ángeles Simarro, Antonio M Vidal (2017) Parallel SUMIS soft detector for MIMO systems on multicore. In: Proceedings of the 17th International Conference on Computational and Mathematical Methods in Science and Engineering, pp 1729–1736Hochwald BM, ten Brink S (2003) Achieving near-capacity on a multiple-antenna channel. IEEE Trans Commun 51:389–399Kaipeng L, Bei Y, Michael W, Joseph RC, Christoph S (2015) Accelerating massive MIMO uplink detection on GPU for SDR systems. In: 2015 IEEE dallas circuits and systems conference (DCAS), pp 1–4Di W, Eilert J, Liu D (2011) Implementation of a high-speed MIMO soft-output symbol detector for software defined radio. J Signal Process Syst 63(1):27–37Anderson E, Bai Z, Bischof C, Blackford LS, Demmel J, Dongarra J, Du Croz J, Greenbaum A, Hammarling S, McKenney A, Sorensen D (1999) LAPACK users’ guide. SIAM, LondonIntel MKL Reference Manual (2015) https://software.intel.com/en-us/articles/mkl-reference-manualcuBLAS Documentation (2015) http://docs.nvidia.com/cuda/cublasDagum L, Enon R (1998) OpenMP: an industry standard API for shared-memory programming. IEEE Comput Sci Eng 5(1):46–55CUDA Toolkit Documentation, Version 7.5 (2015) https://developer.nvidia.com/cuda-toolkitRoger S, Ramiro C, Gonzalez A, Almenar V, Vidal AM (2012) Fully parallel GPU implementation of a fixed-complexity soft-output MIMO detector. IEEE Trans Veh Technol 61(8):3796–3800Senst M, Ascheid G, Lüders H (2010) Performance evaluation of the markov chain monte carlo MIMO detector based on mutual information. 2010 IEEE International Conference on Communications (ICC), pp 1–

Soft MIMO detection through sphere decoding and box optimization

[EN] Achieving optimal detection performance with low complexity is one of the major challenges, mainly in multiple-input multiple-output (MIMO) detection. This paper presents three low-complexity Soft-Output MIMO detection algorithms that are based mainly on Box Optimization (BO) techniques. The proposed methods provide good performance with low computational cost using continuous constrained optimization techniques. The rst proposed algorithm is a non-optimal Soft-Output detector of reduced complexity. This algorithm has been compared with the Soft-Output Fixed Complexity (SFSD) algorithm, obtaining lower complexity and similar performance. The two remaining algorithms are employed in a turbo receiver, achieving the max-log Maximum a Posteriori (MAP) performance. The two Soft-Input Soft-Output (SISO) algorithms were proposed in a previous work for soft-output MIMO detection. This work presents its extension for iterative decoding. The SISO algorithms presented are developed and compared with the SISO Single Tree Search algorithm (STS), in terms of efficiency and computational cost. The results show that the proposed algorithms are more efficient for high order constellation than the STS algorithm.Simarro, MA.; García Mollá, VM.; Vidal Maciá, AM.; Martínez Zaldívar, FJ.; Gonzalez, A. (2018). Soft MIMO detection through sphere decoding and box optimization. Signal Processing. 145:48-58. https://doi.org/10.1016/j.sigpro.2017.11.010S485814

Parallel signal detection for generalized spatial modulation MIMO systems

[EN] Generalized Spatial Modulation is a recently developed technique that is designed to enhance the efficiency of transmissions in MIMO Systems. However, the procedure for correctly retrieving the sent signal at the receiving end is quite demanding. Specifically, the computation of the maximum likelihood solution is computationally very expensive. In this paper, we propose a parallel method for the computation of the maximum likelihood solution using the parallel computing library OpenMP. The proposed parallel algorithm computes the maximum likelihood solution faster than the sequential version, and substantially reduces the worst-case computing times.This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities and by the European Union through grant RTI2018- 098085-BC41 (MCUI/AEI/FEDER), by GVA through PROMETEO/2019/109, and by RED 2018-102668-T. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.García Mollá, VM.; Simarro, MA.; Martínez Zaldívar, FJ.; Boratto, M.; Alonso-Jordá, P.; Gonzalez, A. (2022). Parallel signal detection for generalized spatial modulation MIMO systems. The Journal of Supercomputing. 78(5):7059-7077. https://doi.org/10.1007/s11227-021-04163-y7059707778

Maximum likelihood soft-output detection through Sphere Decoding combined with box optimization

This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing 125 (2016) 249–260. DOI 10.1016/j.sigpro.2016.02.006.This paper focuses on the improvement of known algorithms for maximum likelihood soft-output detection. These algorithms usually have large computational complexity, that can be reduced by using clipping. Taking two well-known soft-output maximum likelihood algorithms (Repeated Tree Search and Single Tree Search) as a starting point, a number of modifications (based mainly on box optimization techniques) are proposed to improve the efficiency of the search. As a result, two new algorithms are proposed for soft-output maximum likelihood detection. One of them is based on Repeated Tree Search (which can be applied with and without clipping). The other one is based on Single Tree Search, which can only be applied to the case with clipping. The proposed algorithms are compared with the Single Tree Search algorithm, and their efficiency is evaluated in standard detection problems (4 4 16-QAM and 4 4 64-QAM) with and without clipping. The results show that the efficiency of the proposed algorithms is similar to that of the Single Tree Search algorithm in the case 4 4 16-QAM; however, in the case 4 4 64- QAM, the new algorithms are far more efficient than the Single Tree Search algorithm. & 2016 Elsevier B.V. All rights reserved.This work has been partially funded by Generalitat Valenciana through the projects ISIC/2012/006 and PROMETEO II/2014/003, and by Ministerio Espanol de Economia y Competitividad through the project TEC2012-38142-C04 and through the Grant RACHEL TEC2013-47141-C4-4-R.García Mollá, VM.; Simarro Haro, MDLA.; Martínez Zaldívar, FJ.; González Salvador, A.; Vidal Maciá, AM. (2016). Maximum likelihood soft-output detection through Sphere Decoding combined with box optimization. Signal Processing. 125:249-260. https://doi.org/10.1016/j.sigpro.2016.02.006S24926012

Low Complexity Near-ML Sphere Decoding based on a MMSE ordering for Generalized Spatial Modulation

[EN] Generalized Spatial Modulation (GSM) is a trans-mission technique used in wireless communications in which only part of the transmitter antennas are activated during each time signaling period. A low complexity Sphere Decoding (SD) algorithm to achieve maximum likelihood (ML) detection has recently been proposed by using subproblem partitions, sorting preprocessing and radius updating. However, the ordering method has a serious limitation when the number of activated antennas is equal to the number of received antennas. Therefore, alternative sorting methods are studied in the present paper. In addition, the computational cost of the ML algorithm can be high when the system sizes increases. In this paper a suboptimal version is proposed where only the first L SD subproblems are carried out. The results show that the proposed algorithm achieves near optimal performance at lower computational cost than ML algorithms.This work has been partially supported by Spanish Ministry of Science, Innovation and Universities and by European Union through grant RTI2018-098085- BC41 (MCUI/AEI/FEDER), by GVA through PROMETEO/2019/109 and by Catedra Telefonica-UPV through SSENCE project.Simarro, MA.; García Mollá, VM.; Martínez Zaldívar, FJ.; Gonzalez, A. (2020). Low Complexity Near-ML Sphere Decoding based on a MMSE ordering for Generalized Spatial Modulation. IEEE. 1-6. https://doi.org/10.1109/PIMRC48278.2020.9217259S1

Maximum likelihood low-complexity GSM detection for large MIMO systems

This is the author's version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, volume 175, october 2020, 107661; DOI 10.1016/j.sigpro.2020.107661.[EN] Hard-Output Maximum Likelihood (ML) detection for Generalized Spatial Modulation (GSM) systems involves obtaining the ML solution of a number of different MIMO subproblems, with as many possible antenna configurations as subproblems. Obtaining the ML solution of all of the subproblems has a large computational complexity, especially for large GSM MIMO systems. In this paper, we present two techniques for reducing the computational complexity of GSM ML detection. The first technique is based on computing a box optimization bound for each subproblem. This, together with sequential processing of the subproblems, allows fast discarding of many of these subproblems. The second technique is to use a Sphere Detector that is based on box optimization for the solution of the subproblems. This Sphere Detector reduces the number of partial solutions explored in each subproblem. The experiments show that these techniques are very effective in reducing the computational complexity in large MIMO setups.This work has been partially supported by Spanish Ministry of Science, Innovation and Universities and by European Union through grant RTI2018-098085-BC41 (MCUI/AEI/FEDER), by GVA through PROMETEO/2019/109 and by Catedra Telefonica-UPV through SSENCE project.García Mollá, VM.; Martínez Zaldívar, FJ.; Simarro, MA.; Gonzalez, A. (2020). Maximum likelihood low-complexity GSM detection for large MIMO systems. Signal Processing. 175:1-11. https://doi.org/10.1016/j.sigpro.2020.107661S11117