Survey on an efficient, low-complex tuple search based sphere detector

Abstract

Nowadays, high detection complexity is known to be one of the major challenges in MIMO communications based on spatial multiplexing. Tuple search (TS) sphere detection was recently introduced, demonstrating to represent a promising approach in this context. It provides significant complexity reduction in comparison to conventional algorithms, providing in addition close to full max-log-APP BER performance. Due to the increasing multiplicity of communication standards as well as variety of mobile applications demanded by users, tackling the lack of flexibility of common receiver realizations has become an additional key challenge in MIMO detection. Aim of this work is to demonstrate that the benefit provided by the tuple search strategy is still present in a wide range of possible transmission schemes. For this purpose, a novel efficiency indicator is introduced, based on which an exhaustive analysis is performed. The existing tuple search detector has been adapted to deal with different constellation orders and transmit/receive antenna configurations. In addition, the applied MMSE strategy has been modified to support undetermined systems. The obtained results show the superiority of the proposed sphere detector under different transmission conditions, thus demonstrating its efficiency and flexibility.Keywords-MIMO detection, sphere decoder, tuple search, efficiency, overhead, asymmetric MIMO. I. INTRODUCTIONFuture mobile communication systems will make use of multiple-input multiple-output (MIMO) techniques in combination with high constellation orders to enhance spectral efficiency. Transmission of spatially multiplexed data streams allows increasing data rates as well as diversity. However, the inherent high detection complexity and lack of flexibility of common detector realizations still represent limiting factors towards efficient implementations. Tree search strategies have been shown to represent a promising approach to overcome the complexity problem. High order systems allow high data rates, but also imply great detection search spaces. As a consequence, detection algorithms exploring all or most of the possible sent symbols become impractical due to the resulting high computational complexity. This is the case of e.g. full max-log-APP detection. To solve this problem, several complexity-reduced detection algorithms presenting close to full max-log-APP detection accuracy have been widely studied. Sphere detector [1], M-algorithm [2], LISS-algorithm [3], or variations of them [4]-[7] are some examples, still subjected to numerous BER performance-complexity trade-offs.

VLSI architecture for soft-output tuple search sphere decoding

“…Strategies like sorted QR decomposition, MMSE preprocessing as well as sphere and L-values clipping are widely applied approaches towards n reduction [1]. Novel approaches like search sequence determination (SSD) [11] and metric estimation (ME) [12] are additionally utilized in this work to further reduce n as well as the detection computational complexity, as detailed in [2], [13].…”

“…Operations performed within each loop are partitioned into the task blocks illustrated in Fig. 2, each mapped to a FU ( [2], [13]). As mentioned in section III-B, a so called one-node-per-cycle(-loop) realization is desired.…”

“…Likewise, modules separatelly processing candidate leaf nodes are included. 2 Values stored in data memories are represented using fixed point with a maximum word width of 8 bits. Fixed point representation is detailed in [13].…”

“…Single Tree Search -STS detector). In this regard, TSD [1] has demonstrated to achieve significant complexity reduction while providing close to full max-log-APP BER performance, outperforming state-of-the-art detection strategies [2] like STS [3] or List Sphere Detection (LSD) [4]. Irregular and data-dependent control flow of so-called depth-first detection algorithms (like STS and LSD) frustrate efficient algorithm parallelization, thus limiting the achievable throughput.…”

“…This, together with high detector complexity, typically leads to inefficient realizations. Moreover, complexity of these conventional detectors grows exponentially with the size of the constellation Q and with the number of transmit antennas N T [2]. Consequently, application of most state-of-the-art detection strategies is restricted to low-order transmission systems (N T ≤ 4, Q ≤ 16).…”