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

Abstract

ElsevierGarcía 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. Abstract 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 (4x4 16-QAM and 4x4 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 4x4 16-QAM; however, in the case 4x4 64-QAM, the new algorithms are far more

Recent Advances in DSP Techniques for Mode Division Multiplexing Optical Networks with MIMO Equalization: A Review

“…A summary of these recent experimental validations using MIMO DSP is presented in Table 4, while an updated graph of mode numbers versus the transmission distance is presented in Figure 38, which summarizes the state-of-the-art SDM-WDM experiments [51][52][53]. Further improvement in the near future will most likely come from the soft-output maximum-likelihood algorithms, including repeated tree search and single tree search, to further improve the efficiency of the MIMO DSP [54]. Last but not least, the set-up of another 6 × 6 MIMO-based SDM/WDM transmission system using a 70-km DGD-compensated three-mode fiber is shown in Figure 36, whereas the received signals are off-line processed by firstly re-sampling the signals to two samples per signal, along with CD and frequency-offset compensation, which is then followed by a 6 × 6 MIMO FDE with 600 symbol-spaced taps using a data-aided LMS algorithm along with carrier-phase recovery and BER counting for Q-factor calculations [49].…”

“…A summary of these recent experimental validations using MIMO DSP is presented in Table 4, while an updated graph of mode numbers versus the transmission distance is presented in Figure 38, which summarizes the state-of-the-art SDM-WDM experiments [51][52][53]. Further improvement in the near future will most likely come from the soft-output maximum-likelihood algorithms, including repeated tree search and single tree search, to further improve the efficiency of the MIMO DSP [54].…”

Soft MIMO detection through sphere decoding and box optimization

“…The work described in this paper focuses on low-cost SO MIMO detection algorithms. We propose three methods: one for NISO case called Box Optimizacion Hard Detector with soft-output (BOHD-SO), which exhibits a non-optimal max-log ML performance; and two SISO methods, SISO Box Optimization Repeated Tree Search (SISO-BORTS) and SISO Double Tree Search (SISO-DTS), with max-log Maximum a Posteriori (MAP) performance based on a previous work reported in [13]. These algorithms reduce the complexity based on the use of continuous constrained optimization methods where the bounds are fixed, hence the name of Box Optimization (BO).…”

“…The work described in [13] has as its main goal the improvement in efficiency of NISO max-log detection algorithms, where the iteration at the receiver was not considered. In the present paper we meaningfully extend the work presented in [13] to the Soft-Input case.…”

“…The work described in [13] has as its main goal the improvement in efficiency of NISO max-log detection algorithms, where the iteration at the receiver was not considered. In the present paper we meaningfully extend the work presented in [13] to the Soft-Input case. Thereby in the present work the a priori information is incorporated by extending BORTS and DTS presented in [13] to the SISO detection.…”