Joint spatial division and multiplexing &amp;#x2014; Benefits of antenna correlation in multi-user MIMO

Nam, Jeonghun; Ahn, Jae‐Young

doi:10.1109/isit.2013.6620300

Cited by 28 publications

(21 citation statements)

References 13 publications

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“…The sum-rate scaling in the large K regime where r K , relevant in practice for hot-spot scenarios, was already addressed in [18], but without sufficient exposition. It is well known from Sharif and Hassibi [11] that the sum capacity of the i.i.d.…”

Section: B Large K Analysismentioning

confidence: 99%

“…Notice that the case where all users have the same correlation corresponds to the case of G = 1, i.e., the poorest case of transmit correlation diversity. As a matter of fact, for G > 1 under the unitary structure where different groups of users have orthogonal eigenspaces, it was shown in [18] that, for fixed M and large K, the asymptotic sum capacity of correlated Rayleigh fading MIMO BC is…”

Section: B Large K Analysismentioning

confidence: 99%

“…Proof: For µ < 1 and q * < T c /2, we have M * = M, q * = r, µ p1 = µ. In this case, we use (7) instead of (18). Then, the growth rate at which the high-SNR capacity increases in the large…”

Section: B Pilot-aided System Imentioning

confidence: 99%

“…The resulting joint spatial division and multiplexing (JSDM) precoding strategy was extensively studied to show that the tall unitary condition holds asymptotically at least for the case of uniform linear array (ULA) with large number of antennas, whenever the user groups have scattering with disjoint angular support [17]. Further work along this line of research has considered: 1) the system capacity in the large system regime (both the number of antennas and the number of users grow to infinity at a fixed ratio) [18]; 2) schemes for opportunistic beamforming with probabilistic scheduling [19]; 3) the suitability of the JSDM framework for millimeter wave (mm-Wave) channels [20]; 4) the elimination of pilot contamination in multicell TDD systems where different cells share the same set of uplink pilot signals [21]; 5) how to design coordinated composite beamforming schemes where the knowledge of the transmit correlation can be used to improve the performance of multi-cell MU-MIMO networks [22], [23].…”

Section: Introductionmentioning

confidence: 99%

“…Assuming perfect CSI, we first study the impact of transmit correlation diversity on the power gain (the parallel shift of capacity versus SNR curves, also known as power offset) of MIMO BC. The authors in [18], [19] characterized the asymptotic capacity behavior in the large number of users regime. In sharp contrast to [10], it turned out that transmit correlation diversity can achieve a sum-rate gain of up to M log G (i.e., power gain of 3 log G dB).…”

Section: Introductionmentioning

confidence: 99%

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On the Role of Transmit Correlation Diversity in Multiuser MIMO Systems

Nam¹,

Caire²,

Ko³

et al. 2017

IEEE Trans. Inform. Theory

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Correlation across transmit antennas, in multiple antenna systems (MIMO), has been studied in various scenarios and has been shown to be detrimental or provide benefits depending on the particular system and underlying assumptions. In this paper, we investigate the effect of transmit correlation on the capacity of the Gaussian MIMO broadcast channel (BC), with a particular interest in the large-scale array (or massive MIMO) regime. To this end, we introduce a new type of diversity, referred to as transmit correlation diversity, which captures the fact that the channel vectors of different users may have different, and often nearly mutually orthogonal, large-scale channel eigen-directions. In particular, when taking the cost of downlink training properly into account, transmit correlation diversity can yield significant capacity gains in all regimes of interest. Our analysis shows that the system multiplexing gain can be increased by a factor up to M/r , where M is the number of antennas and r ≤ M is the common rank of the users transmit correlation matrices, with respect to standard schemes that are agnostic of the transmit correlation and treat the channels as if they were isotropically distributed. Thus, this new form of diversity reveals itself as a valuable "new resource" in multiuser communications. Index Terms

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Section: B Large K Analysismentioning

confidence: 99%

Section: B Large K Analysismentioning

confidence: 99%

Section: B Pilot-aided System Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Role of Transmit Correlation Diversity in Multiuser MIMO Systems

Nam¹,

Caire²,

Ko³

et al. 2017

IEEE Trans. Inform. Theory

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Foundations of MIMO Communication

2018

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Theory is the first term in the Taylor series expansion of practice. Thomas Cover 5 1.2 Signal distributions Table 1.1 Constellation minimum distancesConstellation d min M -PSK 2 sin π M Square M -QAM 6 M −1 M -ary phase shift keying (M -PSK), where s m = e j2π m M +φ0 m = 0, . . . , M − 1 (1.1) with φ 0 an arbitrary phase. Because of symmetry, the points are always equiprobable, p m = 1/M for m = 0, . . . , M − 1. Special mention must be made of binary phase-shift keying (BPSK), corresponding to M = 2, and quadrature phase-shift keying (QPSK), which corresponds to M = 4. Square M -ary quadrature amplitude modulation (M -QAM), where the in-phase and quadrature components of s independently take values in the set 3 2(Nonsquare M -QAM constellations also exist, and they are employed regularly in wireline systems, but seldom in wireless.) Although making the points in a M -QAM constellation equiprobable is not in general optimum, it is commonplace. Note that, except for perhaps an innocuous rotation, 4-QAM coincides with QPSK.

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Preface

2018

Foundations of MIMO Communication

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Joint spatial division and multiplexing — Benefits of antenna correlation in multi-user MIMO

Cited by 28 publications

References 13 publications

On the Role of Transmit Correlation Diversity in Multiuser MIMO Systems

On the Role of Transmit Correlation Diversity in Multiuser MIMO Systems

Foundations of MIMO Communication

Preface

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