Fixed-complexity sphere encoder for multi-user MIMO systems

Mohaisen, Manar; Chang, KyungHi

doi:10.1109/jcn.2011.6157253

Cited by 16 publications

(18 citation statements)

References 12 publications

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“…As for lattice basis reduction precoding, the number of iterations required to orthogonalize the lattice basis can be as high as theoretically infinite, depending on the conditioning of the channel matrix (i.e., lattice basis). We are proposing several low and fixed computational complexity algorithms that aim to find the best t vector such that the total transmit power is reduced [14][15][16][17]. That is:…”

Section: Statement Of Problemmentioning

confidence: 99%

“…The size of the symmetric set Z from which the elements of t are drawn is decided using simulations (see figures in [14][15][16]). To solve (6) successively, let the transpose of H be factorized into the product of the unitary matrix Q and upper triangular matrix R. Then the search problem in (6) is expanded to:…”

Section: Statement Of Problemmentioning

confidence: 99%

“…At the last VP level, the accumulative metrics of the obtained vectors are compared and the one with the smallest accumulative metric is selected, precoded, and transmitted. It has been shown in [14] that the complexity of the FSE is about 15% of that of the conventional QRDME for T = 7, whereas, the encoding throughput is increased seven-fold. Fig.…”

Section: Single Expansionmentioning

confidence: 99%

“…We will first introduce the QR-decomposition with the M-algorithm encoder (QRDM-E), and outline its shortages in terms of complexity and lack of pipelining capabilities [13]. Therefore, the fixed-complexity sphere encoder (FSE) is introduced, where both its low complexity and high parallelization capabilities are outlined [14]. The parallel QRDM-E (PQRDME) is then introduced, for which the goal is to reduce the computational complexity of the conventional QRDM-E [15,16].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

Mohaisen

2015

J Inf Process Syst

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Recently, there has been an increasing demand of high data rates services, where several multiuser multipleinput multiple-output (MU-MIMO) techniques were introduced to meet these demands. Among these techniques, vector perturbation combined with linear precoding techniques, such as zero-forcing and minimum mean-square error, have been proven to be efficient in reducing the transmit power and hence, perform close to the optimum algorithm. In this paper, we review several fixed-complexity vector perturbation techniques and investigate their performance under both perfect and imperfect channel knowledge at the transmitter. Also, we investigate the combination of block diagonalization with vector perturbation outline its merits.

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Section: Statement Of Problemmentioning

confidence: 99%

Section: Statement Of Problemmentioning

confidence: 99%

Section: Single Expansionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

Mohaisen

2015

J Inf Process Syst

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“…In the downlink, sphere decoder-based techniques can be used at the transmitter in lieu of zero-forcing based precoding; this is known as sphere encoder precoding [6,27,38,41]. This precoding, however, requires that APs track the wireless channel as they move, which adds complexity and becomes harder with increasing mobility.…”

Section: Downlink Beamformingmentioning

confidence: 99%

Geosphere

Nikitopoulos

Zhou

Congdon

et al. 2014

SIGCOMM Comput. Commun. Rev.

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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 multiuser MIMO of 2× in 4 × 4 systems and 47% in 2 × 2 MIMO systems.

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Parallel QRD-M encoder for multi-user MIMO systems

Mohaisen

Debbah

2013

Telecommun Syst

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International audienceIn the context of multi-user precoding, the idea behind vector perturbation (VP) lies in adding an integer vector to the data vector such that the overall transmit power is reduced, where the performance at the users end is consequently improved. In the literature, several techniques have been proposed to nd a quasi-optimum perturbing vector, where this process was represented as an integer lattice search problem. In this paper, we propose a parallel QRD-M encoder (PQRDME) that, besides attaining a quasi-optimum diversity order, leads to tremendous reduction in the latency of the vector perturbation stage. Based on the set grouping, the proposed encoder transforms the full tree-search of the conventional QRDME into partial trees that can be pipelined to increase the encoding throughput. We evaluate the proposed algorithm under several scenarios with both perfect channel state information (PCSI) and imperfect CSI (ICSI) at the transmitter side, where simulation results show robust performance when compared to the optimum encoder

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Fixed-complexity sphere encoder for multi-user MIMO systems

Cited by 16 publications

References 12 publications

A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

A Review of Fixed-Complexity Vector Perturbation for MU-MIMO

Geosphere

Parallel QRD-M encoder for multi-user MIMO systems

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