The cost of uncorrelation and non-cooperation in MIMO channels

Philosof, Tal; Zamir, Ram

doi:10.1109/isit.2005.1523441

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Remark 1: It is easy to see that this scheme is optimal (i.e., it achieves the upper bound (5)) if C ≤ R N C (and thus in particular if P → ∞). Instead, when C > R N C , the rate achievable by this scheme does not achieve the upper bound (5), suffering from the performance penalty caused by independent encoding as compared to the waterfilling solution (3) [10].…”

Section: A Independent Messagesmentioning

confidence: 99%

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Distributed MIMO systems with oblivious antennas

Simeone

Somekh

Poor

et al. 2008

2008 IEEE International Symposium on Information Theory

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Abstract-A scenario in which a single source communicates with a single destination via a distributed MIMO transceiver is considered. The source operates each of the transmit antennas via finite-capacity links, and likewise the destination is connected to the receiving antennas through capacity-constrained channels. Targeting a nomadic communication scenario, in which the distributed MIMO transceiver is designed to serve different standards or services, transmitters and receivers are assumed to be oblivious to the encoding functions shared by source and destination. Adopting a Gaussian symmetric interference network as the channel model (as for regularly placed transmitters and receivers), achievable rates are investigated and compared with an upper bound. It is concluded that in certain asymptotic and non-asymptotic regimes obliviousness of transmitters and receivers does not cause any loss of optimality.

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Section: A Independent Messagesmentioning

confidence: 99%

“…Instead, when C > R N C , the rate achievable by this scheme does not achieve the upper bound (5), suffering from the performance penalty caused by independent encoding as compared to the waterfilling solution (3) [10].…”

Section: A Independent Messagesmentioning

confidence: 99%

Distributed MIMO systems with oblivious antennas

Simeone

Somekh

Poor

et al. 2008

2008 IEEE International Symposium on Information Theory

View full text Add to dashboard Cite

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The Role of SNR in Achieving MIMO Rates in Cooperative Systems

Laneman²,

Goldsmith³

2006 IEEE Information Theory Workshop

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We compare the rate of a multiple-antenna relay channel, under different channel state information (CSI) and channel to the capacity of multiple-antenna systems to characpower allocation assumptions. It was shown that transmitter terize the cooperative capacity in different SNR regions. While it cooperation is more favorable with CSI at the transmitter is known that in the asymptotic regime, at a high SNR or with a cooperation is mrfvab wi C ate trmit large number of cooperating nodes, cooperative systems lack full (CSIT), while receiver cooperatIon is superor under opima multiplexing gain, in this paper we consider cooperative capacity power allocation without CSIT. gain at moderate SNR with a fixed number of cooperatingThe capacity of cooperative systems has been studied in the antennas. We show that up to a lower bound to an SNR threshold, asymptotic regime. In [16], it was shown that at asymptotically a cooperative system performs at least as well as a MIMO system high signal-to-noise ratio (SNR), the multiplexing gain of an with isotropic inputs; whereas beyond an upper bound to the SNR ' threshold, the cooperative system is limited by its coordination N-source-destnation-pair network is upper bounded by Nj2. costs, and the capacity is strictly less than that of a MIMO Thus cooperative systems do not enjoy full multiplexing gain. orthogonal channel. The SNR threshold depends on the network In fact, the N12 upper bound was believed to be loose and geometry (the power gain g between the source and relay) and the multiplexing gain was conjectured to be one. From another the number of cooperating antennas M; when the relay is close to perspective, the capacity of large Gaussian relay networks was the source (g > 1), the SNR threshold lower and upper bounds considered in [17]. In an N-node network, for asymptotically are approximately equal. As the cooperating nodes are closer, i.e., as g increases, the MIMO-gain region extends to a higher large N, capacity scales as 0(logN). Hence cooperative SNR. Whereas for a populous cluster, i.e., when M is large, the systems fail to achieve the linear capacity scaling order (in the coordination-limited region sets in at a lower SNR. number of antennas) found in multiple-input-multiple-output (MIMO) systems. Under what conditions, then, is cooperation of most value? In this paper, we consider cooperative capacity In wireless networks, node cooperation has been proposed gain at moderate SNR with a fixed number of cooperating as a strategy to improve communication reliability and system antennas. We show that up to an SNR threshold, which capacity. The idea of cooperative diversity was pioneered depends on the network geometry and the number of antennas, in [1], [2], where the transmitters cooperate to achieve a a cooperative system performs at least as well as a MIMO larger rate region by repeating detected symbols from each system with isotropic inputs. other. In [3] the transmitters forward parity bits to achieve The rest of the paper is organized as follows. Section II moc...

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Optimal Chunk Processing for Multi-User MIMO OFDM Wireless Systems

Jorswieck

Chamekh

Weckerle

2006

2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications

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It has been shown that CSI at the transmitter can significantly improve the performance and reliability of multiple antenna multi-user systems. In MIMO OFDM systems the control overhead and signal processing complexity lead to the definition of time-frequency chunks. For all carriers and at all times in the chunk, the same spatial signal processing is applied. In this paper, we study the optimal signal processing using chunks in multi-user MIMO OFDM systems with respect to sum capacity optimization. At first, the optimal single-user chunk capacity optimization is studied and different optimal and suboptimal approaches are proposed. Then, the extension to the spectral power allocation is analyzed. Finally, the multi-user case is addressed. The results are illustrated by numerical simulations based on the IEEE 802.11n channel model.

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The cost of uncorrelation and non-cooperation in MIMO channels

Cited by 5 publications

References 31 publications

Distributed MIMO systems with oblivious antennas

Distributed MIMO systems with oblivious antennas

The Role of SNR in Achieving MIMO Rates in Cooperative Systems

Optimal Chunk Processing for Multi-User MIMO OFDM Wireless Systems

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