Coding for the mimo broadcast channel

In recent years, the remarkable promise of multiple-antenna techniques has motivated an intense research activity devoted to characterizing the theoretical and practical issues associated with multiple-input multiple-output wireless channels. This activity was first focused primarily on single-user communications but more recently there has been extensive work on multi-user settings. The aim of this chapter is to provide an overview of the fundamental information-theoretic results and practical implementation issues of multi-user multiple-antenna networks operating under various conditions of channel state information.The contents of this chapter are as follows. In Section 1 we introduce basic notation and describe the system model of interest. The latter includes both uplink and downlink models for a general mobile communication system operating with multiple antennas at both the base station and mobile terminals. In Section 2 we concentrate on channel capacity as a means of characterizing such systems, and review basic results under various operating conditions, including patterns of knowledge of information about the state of the channel between transmitter(s) and receiver(s). In Section 3 we address the problem of acquisition of channel state information at the transmitter and receiver, and describe the distinctive features of open-loop and closed-loop systems. In Section 4 we provide an overview of system design issues and discuss techniques that require channel state information at the transmitter, while in Section 5 we briefly review some recent work on techniques that allow

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“…Without loss of generality, we assume that all the MTs are equipped with the same number N of antennas 2 .…”

Section: System Modelmentioning

confidence: 99%

“…2 We adopt the following notation: boldface upper and lower-case letters denote matrices and vectors. We use A = diag{a(n) ; n = 1, 2, .…”

Section: System Modelmentioning

confidence: 99%

Fundamentals of Multi-User MIMO Communications

Sanguinetti

Poor

2009

New Directions in Wireless Communications Research

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“…Therefore the users are indistinguishable to the transmitter implying that each user should be able to decode all messages. The capacity region of this channel, in its most generality, has yet to be solved [15], [46].…”

Section: ) Capacity Scaling With No Csimentioning

confidence: 99%

“…However, when the channels are all identically and independently Rayleigh distributed, then the channel is ergodically degraded and hence a time-division transmission is optimal [15]. Furthermore, it can be shown 1) For fixed M and n:…”

Section: ) Capacity Scaling With No Csimentioning

confidence: 99%

Fundamental Limits in MIMO Broadcast Channels

Hassibi

Sharif

2007

IEEE J. Select. Areas Commun.

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Abstract-This paper studies the fundamental limits of MIMO broadcast channels from a high level, determining the sum-rate capacity of the system as a function of system paramaters, such as the number of transmit antennas, the number of users, the number of receive antennas, and the total transmit power. The crucial role of channel state information at the transmitter is emphasized, as well as the emergence of opportunistic transmission schemes. The effects of channel estimation errors, training, and spatial correlation are studied, as well as issues related to fairness, delay and differentiated rate scheduling.Index Terms-MIMO broadcast channels, sum-rate capacity, asymptotics, channel state information.

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“…Indeed, using the fact that in a homogenous MIMO-BC (when the users' channels have the same statistical behavior) with no CSI at the transmitter, the maximum sum-rate is achieved by time-sharing between the users [9], we can write…”

Section: A the Average Number Of Users Send Feedback To The Bsmentioning

confidence: 99%

How much feedback is required in MIMO Broadcast Channels?

Bayesteh

Khandani

2006

2006 IEEE International Symposium on Information Theory

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In this paper, a downlink communication system, in which a Base Station (BS) equipped with M antennas communicates with N users each equipped with K receive antennas (K ≤ M ), is considered.It is assumed that the receivers have perfect Channel State Information (CSI), while the BS only knows the partial CSI, provided by the receivers via feedback. The minimum amount of feedback required at the BS, to achieve the maximum sum-rate capacity in the asymptotic case of N → ∞ is studied. First, the amount of feedback is defined as the average number of users who send information to the BS.For fixed SNR values, it is shown that with finite amount of feedback it is not possible to achieve the maximum sum-rate. Indeed, to reduce the gap between the achieved sum-rate and the optimum value to zero, a minimum feedback of ln ln ln N is asymptotically necessary. Next, the scenario in which the amount of feedback is defined as the average number of bits sent to the BS is considered, assuming different ranges of Signal to Noise Ratio (SNR). In the fixed and low SNR regimes, it is demonstrated that to achieve the maximum sum-rate, an infinite amount of feedback is required. Moreover, in order to reduce the gap to the optimum sum-rate to zero, in the fixed SNR regime, the minimum amount of feedback scales as Θ(ln ln ln N ), which is achievable by the Random Beam-Forming scheme proposed in [14]. In the high SNR regime, two cases are considered; in the case of K < M , it is proved that the minimum amount of feedback bits to reduce the gap between the achievable sum-rate and the maximum sum-rate to zero grows logaritmically with SNR, which is achievable by the "Generalized Random Beam-Forming" scheme, proposed in [18]. In the case of K = M , it is shown that by using the Random Beam-Forming scheme and the total amount of feedback not growing with SNR, the maximum sum-rate capacity is achieved.

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Coding for the mimo broadcast channel

Cited by 14 publications

References 7 publications

Fundamentals of Multi-User MIMO Communications

Fundamentals of Multi-User MIMO Communications

Fundamental Limits in MIMO Broadcast Channels

How much feedback is required in MIMO Broadcast Channels?

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