VBLAST Detection Algorithms Utilizing Soft Symbol Estimate and Noncircular CAI

Xiao, Pei; Yin, Wenjie; Cowan, C.F.N.; Fusco, Vincent

doi:10.1109/tsp.2011.2108292

Cited by 5 publications

(6 citation statements)

References 18 publications

(19 reference statements)

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“…This algorithm continues till the time the last received symbol is detected and its effect is eliminated. VBLAST algorithm [40] is referred as the ZF resultant matrix by zero forcing, whereas Q z (.) represents a slicer.…”

Section: E the Vblast Detectormentioning

confidence: 99%

Optimal Massive MIMO Detection for 5G Communication Systems via Hybrid n-Bit Heuristic Assisted-VBLAST

et al. 2019

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Being increasingly spectral and energy efficient, massive multiple-input multiple-output (MIMO) is envisaged as a potential technology for fifth generation (5G) wireless communication networks. Radio spectrum has become a scarce resource in wireless communications and consequently imposes excessive cost on the high data rate transmission. Several linear and non linear detection techniques such as Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), and Vertical Bell-Labs Layered Space Time (VBLAST) have been introduced. The purpose of such schemes is to mitigate the signal detection problems which are based on trade-offs between the bit error rate (BER) performance and computational complexities. The challenge in the design of massive-MIMO systems is developing less complex and efficient detection algorithms. The problem in building a receiver for massive-MIMO is to de-correlate the spatial signatures on the receiver antenna array. In this paper, we propose a novel algorithm viz: Hybrid n-Bit Heuristic Assisted-VBLAST (HHAV) to perform an optimum decoding. We have simulated this structure in dynamic Rayleigh fading channel. We have also evaluated the AMP algorithm with two threshold functions which include AMP with ternary distribution (AMPT) and AMP with Gaussian distribution (AMPG). Numerical results confirm that HHAV algorithm performs significantly better than the in vogue aforementioned detection systems as introduced in recent years.

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Section: E the Vblast Detectormentioning

confidence: 99%

Optimal Massive MIMO Detection for 5G Communication Systems via Hybrid n-Bit Heuristic Assisted-VBLAST

et al. 2019

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“…The cancelation performed in (21) is referred to as the conventional OSIC [17,19]; detected s`.k/ is regenerated by Q f H x`.k/ h`. The cancelation performed in (22) is referred to as the transformation-based OSIC [18,29]; detected s`.k/ is regenerated by Q f H x`.k/ f T m h`. Thus, the .m,`/-th entry of the unit upper triangular feedback matrix B C-MS is defined by…”

Section: Proofmentioning

confidence: 99%

“…The signals s M .k/, s M 1 .k/, , s mC1 .k/ are already detected. Recall that the filter output for detecting s m .k/ is given in (22). By applying the properties in Lemma 2 to the RHS of (22), we have the filter output…”

Section: Appendix D: Proof Of Lemmamentioning

confidence: 99%

“…We mainly study the MMSE detector, MMSE-SIC detector, and K-best detector with MMSE-SIC preprocessing. These three detectors are representatives of three classes of detectors, including the linear detectors [19], the ordered SIC (OSIC) detectors [17][18][19][20][21][22][23][24][25], and the tree search detectors [7][8][9]26]. The vector and matrix mapping properties [27] originally used in deriving the equivalence between the complex and real optimal detectors and channel capacity [27] play important roles in our derivations.…”

mentioning

confidence: 99%

“…As the conventional OSIC procedure continues, the nulling filter f M 1 is used by the complex MMSE-SIC detector to detect s M 1 .k/. From (22), the filter output for detecting…”

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See 2 more Smart Citations

On fast preprocessing schemes for the real‐valued spatially multiplexed MIMO detectors

Liu¹,

Chiu²

2012

Int J Communication

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SUMMARYMultiple‐input and multiple‐output detectors may rely on the complex and real signal models, yielding complex detectors for quadrature amplitude modulated signals and real detectors for pulse amplitude modulated signals, respectively. It is well‐known that the complex and real maximum likelihood detectors are equivalent. But relying on both the conventional real and pairwise real models, we show in this paper that some of the suboptimal real detectors are equivalent to their counterpart complex detectors, whereas some are not. The equivalence between the complex and pairwise real detectors also leads us to develop fast preprocessing algorithms for the real ordered successive interference cancelation and tree search detectors. Finally, we show that the preprocessing computations required by the equivalent suboptimal complex and pairwise real detectors are of the same complexity. When some practical preprocessing criteria are used, the real detectors may not detect the PAM signals in the pairwise manner. Such non‐pairwise real detectors outperform their counterpart complex detectors at the cost of higher preprocessing complexity. Copyright © 2012 John Wiley & Sons, Ltd.

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