43 research outputs found
How much feedback is required in MIMO Broadcast Channels?
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 (), 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 and different ranges of SNR is studied. 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 , 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 , 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 , 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.Comment: Submitted to IEEE Trans. on Inform. Theor
On the Delay-Throughput Tradeoff in Distributed Wireless Networks
This paper deals with the delay-throughput analysis of a single-hop wireless
network with transmitter/receiver pairs. All channels are assumed to be
block Rayleigh fading with shadowing, described by parameters
, where denotes the probability of shadowing and
represents the average cross-link gains. The analysis relies on the
distributed on-off power allocation strategy (i.e., links with a direct channel
gain above a certain threshold transmit at full power and the rest remain
silent) for the deterministic and stochastic packet arrival processes. It is
also assumed that each transmitter has a buffer size of one packet and dropping
occurs once a packet arrives in the buffer while the previous packet has not
been served. In the first part of the paper, we define a new notion of
performance in the network, called effective throughput, which captures the
effect of arrival process in the network throughput, and maximize it for
different cases of packet arrival process. It is proved that the effective
throughput of the network asymptotically scales as , with , regardless of
the packet arrival process. In the second part of the paper, we present the
delay characteristics of the underlying network in terms of the packet dropping
probability. We derive the sufficient conditions in the asymptotic case of such that the packet dropping probability tend to zero, while
achieving the maximum effective throughput of the network. Finally, we study
the trade-off between the effective throughput, delay, and packet dropping
probability of the network for different packet arrival processes.Comment: Submitted to IEEE Transactions on Information Theory (34 pages
Scheduling and Codeword Length Optimization in Time Varying Wireless Networks
In this paper, a downlink scenario in which a single-antenna base station
communicates with K single antenna users, over a time-correlated fading
channel, is considered. It is assumed that channel state information is
perfectly known at each receiver, while the statistical characteristics of the
fading process and the fading gain at the beginning of each frame are known to
the transmitter. By evaluating the random coding error exponent of the
time-correlated fading channel, it is shown that there is an optimal codeword
length which maximizes the throughput. The throughput of the conventional
scheduling that transmits to the user with the maximum signal to noise ratio is
examined using both fixed length codewords and variable length codewords.
Although optimizing the codeword length improves the performance, it is shown
that using the conventional scheduling, the gap between the achievable
throughput and the maximum possible throughput of the system tends to infinity
as K goes to infinity. A simple scheduling that considers both the signal to
noise ratio and the channel time variation is proposed. It is shown that by
using this scheduling, the gap between the achievable throughput and the
maximum throughput of the system approaches zero
Asymptotic Analysis of Amplify and Forward Relaying in a Parallel MIMO Relay Network
This paper considers the setup of a parallel MIMO relay network in which
relays, each equipped with antennas, assist the transmitter and the
receiver, each equipped with antennas, in the half-duplex mode, under the
assumption that . This setup has been studied in the literature like
in \cite{nabar}, \cite{nabar2}, and \cite{qr}. In this paper, a simple scheme,
the so-called Incremental Cooperative Beamforming, is introduced and shown to
achieve the capacity of the network in the asymptotic case of
with a gap no more than . This result is shown to hold,
as long as the power of the relays scales as .
Finally, the asymptotic SNR behavior is studied and it is proved that the
proposed scheme achieves the full multiplexing gain, regardless of the number
of relays
On the Throughput Maximization in Dencentralized Wireless Networks
A distributed single-hop wireless network with links is considered, where
the links are partitioned into a fixed number () of clusters each operating
in a subchannel with bandwidth . The subchannels are assumed to be
orthogonal to each other. A general shadow-fading model, described by
parameters , is considered where denotes the
probability of shadowing and () represents the average
cross-link gains. The main goal of this paper is to find the maximum network
throughput in the asymptotic regime of , which is achieved by: i)
proposing a distributed and non-iterative power allocation strategy, where the
objective of each user is to maximize its best estimate (based on its local
information, i.e., direct channel gain) of the average network throughput, and
ii) choosing the optimum value for . In the first part of the paper, the
network hroughput is defined as the \textit{average sum-rate} of the network,
which is shown to scale as . Moreover, it is proved that in
the strong interference scenario, the optimum power allocation strategy for
each user is a threshold-based on-off scheme. In the second part, the network
throughput is defined as the \textit{guaranteed sum-rate}, when the outage
probability approaches zero. In this scenario, it is demonstrated that the
on-off power allocation scheme maximizes the throughput, which scales as
. Moreover, the optimum spectrum sharing for
maximizing the average sum-rate and the guaranteed sum-rate is achieved at M=1.Comment: Submitted to IEEE Transactions on Information Theor