1,419 research outputs found
Random Beamforming with Heterogeneous Users and Selective Feedback: Individual Sum Rate and Individual Scaling Laws
This paper investigates three open problems in random beamforming based
communication systems: the scheduling policy with heterogeneous users, the
closed form sum rate, and the randomness of multiuser diversity with selective
feedback. By employing the cumulative distribution function based scheduling
policy, we guarantee fairness among users as well as obtain multiuser diversity
gain in the heterogeneous scenario. Under this scheduling framework, the
individual sum rate, namely the average rate for a given user multiplied by the
number of users, is of interest and analyzed under different feedback schemes.
Firstly, under the full feedback scheme, we derive the closed form individual
sum rate by employing a decomposition of the probability density function of
the selected user's signal-to-interference-plus-noise ratio. This technique is
employed to further obtain a closed form rate approximation with selective
feedback in the spatial dimension. The analysis is also extended to random
beamforming in a wideband OFDMA system with additional selective feedback in
the spectral dimension wherein only the best beams for the best-L resource
blocks are fed back. We utilize extreme value theory to examine the randomness
of multiuser diversity incurred by selective feedback. Finally, by leveraging
the tail equivalence method, the multiplicative effect of selective feedback
and random observations is observed to establish the individual rate scaling.Comment: Submitted in March 2012. To appear in IEEE Transactions on Wireless
Communications. Part of this paper builds upon the following letter: Y. Huang
and B. D. Rao, "Closed form sum rate of random beamforming", IEEE Commun.
Lett., vol. 16, no. 5, pp. 630-633, May 201
Reinforcement-based data transmission in temporally-correlated fading channels: Partial CSIT scenario
Reinforcement algorithms refer to the schemes where the results of the
previous trials and a reward-punishment rule are used for parameter setting in
the next steps. In this paper, we use the concept of reinforcement algorithms
to develop different data transmission models in wireless networks. Considering
temporally-correlated fading channels, the results are presented for the cases
with partial channel state information at the transmitter (CSIT). As
demonstrated, the implementation of reinforcement algorithms improves the
performance of communication setups remarkably, with the same feedback
load/complexity as in the state-of-the-art schemes.Comment: Accepted for publication in ISWCS 201
Cooperative Feedback for Multi-Antenna Cognitive Radio Networks
Cognitive beamforming (CB) is a multi-antenna technique for efficient
spectrum sharing between primary users (PUs) and secondary users (SUs) in a
cognitive radio network. Specifically, a multi-antenna SU transmitter applies
CB to suppress the interference to the PU receivers as well as enhance the
corresponding SU-link performance. In this paper, for a
multiple-input-single-output (MISO) SU channel coexisting with a
single-input-single-output (SISO) PU channel, we propose a new and practical
paradigm for designing CB based on the finite-rate cooperative feedback from
the PU receiver to the SU transmitter. Specifically, the PU receiver
communicates to the SU transmitter the quantized SU-to-PU channel direction
information (CDI) for computing the SU transmit beamformer, and the
interference power control (IPC) signal that regulates the SU transmission
power according to the tolerable interference margin at the PU receiver. Two CB
algorithms based on cooperative feedback are proposed: one restricts the SU
transmit beamformer to be orthogonal to the quantized SU-to-PU channel
direction and the other relaxes such a constraint. In addition, cooperative
feedforward of the SU CDI from the SU transmitter to the PU receiver is
exploited to allow more efficient cooperative feedback. The outage
probabilities of the SU link for different CB and cooperative
feedback/feedforward algorithms are analyzed, from which the optimal
bit-allocation tradeoff between the CDI and IPC feedback is characterized.Comment: 26 pages; to appear in IEEE Trans. Signal Processin
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