On optimum receivers for channels having memory (Corresp.)

IEEE Commun. Surv. Tutorials

2015

675

439

The emerging massive/large-scale MIMO (LS-MIMO) systems relying on very large antenna arrays have become a hot topic of wireless communications. Compared to the LTE based 4G mobile communication system that allows for up to 8 antenna elements at the base station (BS), the LS-MIMO system entails an unprecedented number of antennas, say 100 or more, at the BS. The huge leap in the number of BS antennas opens the door to a new research field in communication theory, propagation and electronics, where random matrix theory begins to play a dominant role. In this paper, we provide a recital on the historic heritages and novel challenges facing LS-MIMOs from a detection perspective. Firstly, we highlight the fundamentals of MIMO detection, including the nature of co-channel interference, the generality of the MIMO detection problem, the received signal models of both linear memoryless MIMO channels and dispersive MIMO channels exhibiting memory, as well as the complex-valued versus real-valued MIMO system models. Then, an extensive review of the representative MIMO detection methods conceived during the past 50 years (1965-2015) is presented, and relevant insights as well as lessons are inferred for designing complexity-scalable MIMO detection algorithms that are potentially applicable to LS-MIMO systems. Furthermore, we divide the LS-MIMO systems into two types, and elaborate on the distinct detection strategies suitable for each of them. The type-I LS-MIMO corresponds to the case where the number of active users is much smaller than the number of BS antennas, which is currently the mainstream definition of LS-MIMO. The type-II LS-MIMO corresponds to the case where the number of active users is comparable to the number of BS antennas. Finally, we discuss the applicability of existing MIMO detection algorithms in LS-MIMO systems, and review some of the recent advances in LS-MIMO detection.Comment: 51 pages, 36 figures, 10 tables, 659 references, accepted to appear on IEEE Communications Surveys & Tutorials, June 201

Section: A Optimum Mimo Detectormentioning

confidence: 99%

Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs

Yang

IEEE Commun. Surv. Tutorials

2015

675

439

“…3. In the next section, we will provide a comparative study of the RBF equalizer and the conventional MAP equalizer of [16].…”

Section: Rbf-assisted Teqmentioning

confidence: 99%

Radial basis function-assisted turbo equalization

Yee

Yeap²,

2003

IEEE Trans. Commun.

Abstract-This paper presents a turbo equalization (TEQ) scheme, which employs a radial basis function (RBF)-based equalizer instead of the conventional trellis-based equalizer of Douillard et al. Structural, computational complexity, and performance comparisons of the RBF-based and trellis-based TEQs are provided. The decision feedback-assisted RBF TEQ is capable of attaining a similar performance to the logarithmic maximum a posteriori scheme in the context of both binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) modulation, while achieving a factor 2.5 and 3 lower computational complexity, respectively. However, there is a 2.5-dB performance loss in the context of 16 quadrature amplitude modulation (QAM), which suffers more dramatically from the phenomenon of erroneous decision-feedback effects. A novel element of our design, in order to further reduce the computational complexity of the RBF TEQ, is that symbol equalizations are invoked at current iterations only if the decoded symbol has a high error probability. This techniques provides 37% and 54% computational complexity reduction compared to the full-complexity RBF TEQ for the BPSK RBF TEQ and 16QAM RBF TEQ, respectively, with little performance degradation, when communicating over dispersive Rayleigh fading channels. [4]. In this paper, we employ a radial basis function (RBF)-assisted equalizer as the soft-in/soft-out (SISO) equalizer in the context of TEQ. In Sections I-A and I-B, we will introduce the RBF equalizer and the TEQ scheme, followed by the implementation details of the RBF TEQ in Section II. Section III is dedicated to the comparison of the RBF and MAP equalizers, while Section IV entails our discussions related to the complexity reduction issues of the RBF TEQ when using the Jacobian logarithm known from the field of turbo-channel coding. In Section V, the proposed scheme's performance is benchmarked against that of the optimal MAP TEQ scheme of [1]. Finally, in Section VI, we proposed a further computational complexity-reduction technique for the RBF TEQ, and in Section VII, we conclude our investigations. Index Terms-Decision-feedback equalizer (DFE), Jacobian logarithm, neural network, radial basis function (RBF), turbo coding, turbo equalization (TEQ). I. BACKGROUNDPaper approved by M. Chiani, the Editor for Wireless Communication of the IEEE Communications Society. Manuscript

“…Without the loss of generality, we will choose , and as this choice of the DFE structure parameters is sufficient to guarantee the linear separability (see Proposition 1 in this section). The observation vector (3) can be arranged as (5) where , with (6) …”

Section: Bayesian Decision Feedback Equalizermentioning

confidence: 99%

Asymptotic Bayesian decision feedback equalizer using a set of hyperplanes

Chen

Mulgrew

IEEE Trans. Signal Process.

2000

Abstract-We present a signal space partitioning technique for realizing the optimal Bayesian decision feedback equalizer (DFE). It is known that when the signal-to-noise ratio (SNR) tends to infinity, the decision boundary of the Bayesian DFE is asymptotically piecewise linear and consists of several hyperplanes. The proposed technique determines these hyperplanes explicitly and uses them to partition the observation signal space. The resulting equalizer is made up of a set of parallel linear discriminant functions and a Boolean mapper. Unlike the existing signal space partitioning technique of Kim and Moon, which involves complex combinatorial search and optimization in design, our design procedure is simple and straightforward, and guarantees to achieve the asymptotic Bayesian DFE.Index Terms-Asymptotic decision boundary, Bayesian decision feedback equalizer, signal space partition.