This paper presents the design and implementation of Geosphere, a physical- and link-layer design for access point-based MIMO wireless networks that consistently improves network throughput. To send multiple streams of data in a MIMO system, prior designs rely on a technique called zero-forcing, a way of "nulling" the interference between data streams by mathematically inverting the wireless channel matrix. In general, zero-forcing is highly effective, significantly improving throughput. But in certain physical situations, the MIMO channel matrix can become "poorly conditioned," harming performance. With these situations in mind, Geosphere uses sphere decoding, a more computationally demanding technique that can achieve higher throughput in such channels. To overcome the sphere decoder's computational complexity when sending dense wireless constellations at a high rate, Geosphere introduces search and pruning techniques that incorporate novel geometric reasoning about the wireless constellation. These techniques reduce computational complexity of 256-QAM systems by almost one order of magnitude, bringing computational demands in line with current 16- and 64-QAM systems already realized in ASIC. Geosphere thus makes the sphere decoder practical for the first time in a 4 × 4 MIMO, 256-QAM system. Results from our WARP testbed show that Geosphere achieves throughput gains over multi-user MIMO of 2× in 4 × 4 systems and 47% in 2 × 2 MIMO systems.

/pdf/geosphere-consistently-turning-mimo-capacity-into-throughput-7mmy1aoy6n.pdf

Geosphere: consistently turning MIMO capacity into throughput

In this letter, we propose a reordering of the channel representation for Sphere Decoding (SD) where the real and imaginary parts of each jointly detected symbol are decoded independently. Making use of the proposed structure along with a scalar quantization technique, we reduce the decoding complexity substantially. We show that this approach achieves 85% reduction in the overall complexity compared to the conventional SD for a 2 times 2 system, and 92% reduction for the 4 times 4 and 6 times 6 cases at low SNR values, and almost 50% at high SNR, thus relaxing the requirements for hardware implementation.

Reduced complexity sphere decoding via a reordered lattice representation

Large MIMO base stations remain among wireless network designers’ best tools for increasing wireless throughput while serving many clients, but current system designs, sacrifice throughput with simple linear MIMO detection algorithms. Higher-performance detection techniques are known, but remain off the table because these systems parallelize their computation at the level of a whole OFDM subcarrier, sufficing only for the lessdemanding linear detection approaches they opt for. This paper presents FlexCore, the first computational architecture capable of parallelizing the detection of large numbers of mutually-interfering information streams at a granularity below individual OFDM subcarriers, in a nearly-embarrassingly parallel manner while utilizing any number of available processing elements. For 12 clients sending 64-QAM symbols to a 12-antenna base station, our WARP testbed evaluation shows similar network throughput to the state-of-the-art while using an order of magnitude fewer processing elements. For the same scenario, our combined WARP-GPU testbed evaluation demonstrates a 19× computational speedup, with 97% increased energy efficiency when compared with the state of the art. Finally, for the same scenario, an FPGAbased comparison between FlexCore and the state of the art shows that FlexCore can achieve up to 96% better energy efficiency, and can offer up to 32× the processing throughput.

/pdf/flexcore-massively-parallel-and-flexible-processing-for-42nfm1iioj.pdf

FlexCore: Massively Parallel and Flexible Processing for Large MIMO Access Points

A technique to prune the paths for K-best sphere decoding algorithm (SDA) based on radius constraint is presented. The proposed scheme preserves breadth-first searching nature, and the distinct radii for each decoding layer are theoretically derived from the system model with the noise statistics. In addition, based on the data range provided by the radius, a low complexity sorting strategy is proposed. The proposed method can apply to SDA with various path cost functions. Euclidean norm and sum of absolute difference are demonstrated in this paper. With SNR degradation less than 0.2 dB, more than 47% and 90% computation complexity can be reduced in 16-QAM and 64-QAM 4times4 MIMO detection, respectively.

https://ir.nctu.edu.tw:443/bitstream/11536/31965/1/000259505003083.pdf

Early-Pruned K-Best Sphere Decoding Algorithm Based on Radius Constraints

In this paper, we propose a trellis-search based soft-input soft-output detection algorithm and its very large scale integration (VLSI) architecture for iterative multiple-input multiple-output (MIMO) receivers. We construct a trellis diagram to represent the search space of a transmitted MIMO signal. With the trellis model, we evenly distribute the workload of candidates searching among multiple trellis nodes for parallel processing. The search complexity is significantly reduced because the number of candidates is greatly limited at each trellis node. By leveraging the trellis structure, we develop an approximate Log-MAP algorithm by using a small list of largest exponential terms to compute the LLR (log-likelihood ratio) values. The trellis-search based detector has a fixed-complexity and is very suitable for parallel VLSI implementation. As a case study, we have designed and synthesized a trellis-search based soft-input soft-output MIMO detector for a 4 × 4 16-QAM system using a 1.08 V TSMC 65 nm technology. The detector can achieve a maximum throughput of 1.7 Gb/s with a core area of 1.58 mm2.

/pdf/trellis-search-based-soft-input-soft-output-mimo-detector-2a7pmlmcju.pdf

Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture

Multiple-input multiple-output (MIMO) technique is a key enabling technology for today's high-rate wireless communications. The sphere decoding algorithm (SDA) has been used for maximum likelihood (ML) detection in MIMO systems. However, it suffers from variable computation complexity and non-fixed throughput. Therefore, the k-best sphere decoding algorithm is proposed for MIMO detections for its less complexity and fixed throughput. The disadvantage for k-best SDA is that it has performance loss due to the reason that ML detection is not guaranteed. In this paper, we propose some improved k-best sphere decoding algorithms which improve the MIMO detection performance. Simulation results show that by applying sorted QR decomposition for the channel matrix, and/or introducing dynamic K values for different layers, our improved algorithms can achieve 1-2 dB detection performance gain for 4 /spl times/ 4 64QAM MIMO systems over the traditional k-best SDA without introducing extra computational complexity.

Qingwei Li

Papers

Improved k-best sphere decoding algorithms for MIMO systems