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351
On the capacity of MIMO broadcast channel with partial side information
- IEEE TRANS. INFORM. THEORY
, 2005
"... In multiple-antenna broadcast channels, unlike point-to-point multiple-antenna channels, the multiuser capacity depends heavily on whether the transmitter knows the channel coefficients to each user. For instance, in a Gaussian broadcast channel with transmit antennas and single-antenna users, the ..."
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Cited by 349 (9 self)
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In multiple-antenna broadcast channels, unlike point-to-point multiple-antenna channels, the multiuser capacity depends heavily on whether the transmitter knows the channel coefficients to each user. For instance, in a Gaussian broadcast channel with transmit antennas and single-antenna users, the sum rate capacity scales like log log for large if perfect channel state information (CSI) is available at the transmitter, yet only logarithmically with if it is not. In systems with large, obtaining full CSI from all users may not be feasible. Since lack of CSI does not lead to multiuser gains, it is therefore of interest to investigate transmission schemes that employ only partial CSI. In this paper, we propose a scheme that constructs random beams and that transmits information to the users with the highest signal-to-noise-plus-interference ratios (SINRs), which can be made available to the transmitter with very little feedback. For fixed and increasing, the throughput of our scheme scales as log log, where is the number of receive antennas of each user. This is precisely the same scaling obtained with perfect CSI using dirty paper coding. We furthermore show that a linear increase in throughput with can be obtained provided that does not not grow faster than log. We also study the fairness of our scheduling in a heterogeneous network and show that, when is large enough, the system becomes interference dominated and the probability of transmitting to any user converges to 1, irrespective of its path loss. In fact, using = log transmit antennas emerges as a desirable operating point, both in terms of providing linear scaling of the throughput with as well as in guaranteeing fairness.
Distributed video coding
- PROC. OF THE IEEE 93 (2005) 71–83
, 2005
"... Distributed coding is a new paradigm for video compression, ..."
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Cited by 316 (11 self)
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Distributed coding is a new paradigm for video compression,
Sum Capacity of a Gaussian Vector Broadcast Channel
- IEEE Trans. Inform. Theory
, 2002
"... This paper characterizes the sum capacity of a class of non-degraded Gaussian vectB broadcast channels where a singletransmitter with multiple transmit terminals sends independent information to multiple receivers. Coordinat+[ is allowed among the transmit teminals, but not among the different recei ..."
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Cited by 279 (21 self)
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This paper characterizes the sum capacity of a class of non-degraded Gaussian vectB broadcast channels where a singletransmitter with multiple transmit terminals sends independent information to multiple receivers. Coordinat+[ is allowed among the transmit teminals, but not among the different receivers. The sum capacity is shown t be a saddlepoint of a Gaussian mu al informat]R game, where a signal player chooses a tansmit covariance matrix to maximize the mutual information, and a noise player chooses a fictitious noise correlation to minimize the mutual information. This result holds fort he class of Gaussian channels whose saddle-point satisfies a full rank condition. Furt her,t he sum capacity is achieved using a precoding method for Gaussian channels with additive side information non-causally known at the transmitter. The optimal precoding structure is shown t correspond to a decision-feedback equalizer that decomposes t e broadcast channel into a series of single-user channels with intk ference pre-subtract] at the transmiter.
Distributed source coding for sensor networks
- In IEEE Signal Processing Magazine
, 2004
"... n recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf pre-vious milestones in the information revolution. MIT Technology Review ranked wireless sensor networks that con-sist of many tiny, low- ..."
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Cited by 224 (4 self)
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n recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf pre-vious milestones in the information revolution. MIT Technology Review ranked wireless sensor networks that con-sist of many tiny, low-power and cheap wireless sensors as the number one emerging technology. Unlike PCs or the Internet, which are designed to support all types of applications, sensor networks are usually mission driven and application specific (be it detection of biological agents and toxic chemicals; environmental measure-ment of temperature, pressure and vibration; or real-time area video surveillance). Thus they must operate under a set of unique constraints and requirements. For example, in contrast to many other wireless devices (e.g., cellular phones, PDAs, and laptops), in which energy can be recharged from time to time, the energy provisioned for a wireless sensor node is not expected to be renewed throughout its mission. The limited amount of energy available to wireless sensors has a significant impact on all aspects of a wireless sensor network, from the amount of information that the node can process, to the volume of wireless communication it can carry across large distances. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies; it relies on many com-ponents working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies for sensor networks is distributed source coding (DSC), which refers to the compression of multiple correlated sensor out-puts [1]–[4] that do not communicate with each other (hence distributed coding). These sensors send their compressed outputs to a central point [e.g., the base station (BS)] for joint decoding. I
Compression of binary sources with side information using low-density parity-check codes
- in Proc. Global Telecommunications Conf
, 2002
"... Abstract—We show how low-density parity-check (LDPC) codes can be used to compress close to the Slepian–Wolf limit for cor-related binary sources. Focusing on the asymmetric case of com-pression of an equiprobable memoryless binary source with side information at the decoder, the approach is based o ..."
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Cited by 209 (6 self)
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Abstract—We show how low-density parity-check (LDPC) codes can be used to compress close to the Slepian–Wolf limit for cor-related binary sources. Focusing on the asymmetric case of com-pression of an equiprobable memoryless binary source with side information at the decoder, the approach is based on viewing the correlation as a channel and applying the syndrome concept. The encoding and decoding procedures are explained in detail. The per-formance achieved is seen to be better than recently published re-sults using turbo codes and very close to the Slepian–Wolf limit. Index Terms—Channel coding, distributed source coding, LDPC codes, Slepian–Wolf theorem. I.
MIMO Broadcast Channels With Finite-Rate Feedback
, 2006
"... Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has per ..."
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Cited by 189 (1 self)
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Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR.
Joint Physical Layer Coding and Network Coding for Bi-Directional Relaying
- 45th Annual Allerton Conference on Communication, Control and Computing
, 2007
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Compute-and-forward: Harnessing interference through structured codes
- IEEE TRANS. INF. THEORY
, 2009
"... ..."
MIMO broadcast channels with finite rate feedback
- IEEE Trans. on Inform. Theory
, 2006
"... Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e. multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this paper, a system where each receiver has perfect channe ..."
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Cited by 155 (10 self)
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Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e. multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this paper, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well known zero forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the SNR (in dB) in order to achieve the full multiplexing gain, which is in sharp contrast to point-to-point MIMO systems in which it is not necessary to increase the feedback rate as a function of the SNR. I.
