Optimum Feedback Quantization in an Opportunistic Beamforming Scheme
In recent years, digital beamforming has evolved into an excellent technology to improve wireless communications over fading channels. Especially in slow fading environments with a sufficient number of users in the system, opportunistic beamforming through multiuser diversity  has offered some advantages over true beamforming methods that rely on full channel feedback and/or robust channel estimation methods. Opportunistic beamforming achieves good throughput with only signal-to-noise ratio (SNR) feedback from the users. The quality of the SNR feedback such as the degree of SNR quantization is essential for opportunistic beamforming because the base station selects the best receiving user based on the SNR measurements sent by the users. In this paper, we develop an optimum SNR quantization method performed by the users and analyze its impact on the system throughput. While keeping the fairness among the users, we show that the opportunistic beamforming gain can still be realized with the help of the proposed quantization method. Theoretical analysis and computer simulation results show the feasibility and effectiveness of the method which provides insights for engineers to implement opportunistic beamforming in practice.
Available from: Ayman Massaoudi
- "In practice, the SINRs are quantized before being fed back to the base station in order to make more efficient use of the limited resources (bandwidth and power). The SINR quantization for OB non-cognitive system has been recently studied in the literature   . In these studies, the impact of the feedback quantization on the throughput of OB system is analyzed. "
Available from: export.arxiv.org
- "Most of the performance analysis studies on more practical multiuser BF algorithms are on the other hand based upon asymptotic analysis . An exact statistical analysis is essential to gain a complete insight into the system performance and to analytically determine some useful parameters –. The difficulty with the performance analysis of linear precoding schemes with user scheduling mainly arises from the fact that user ordering changes the statistics of the users making it quite complicated to express mathematically. "
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ABSTRACT: Simultaneous multiuser beamforming in multiantenna downlink channels can
entail dirty paper (DP) precoding (optimal and high complexity) or linear
precoding (suboptimal and low complexity) approaches. The system performance is
typically characterized by the sum capacity with homogenous users with perfect
channel state information at the transmitter. The sum capacity performance
analysis requires the exact probability distributions of the user
signal-to-noise ratios (SNRs) or signal-to-interference plus noise ratios
(SINRs). The standard techniques from order statistics can be sufficient to
obtain the probability distributions of SNRs for DP precoding due to the
removal of known interference at the transmitter. Derivation of such
probability distributions for linear precoding techniques on the other hand is
much more challenging. For example, orthogonal beamforming techniques do not
completely cancel the interference at the user locations, thereby requiring the
analysis with SINRs. In this paper, we derive the joint probability
distributions of the user SINRs for two orthogonal beamforming methods combined
with user scheduling: adaptive orthogonal beamforming and orthogonal linear
beamforming. We obtain compact and unified solutions for the joint probability
distributions of the scheduled users' SINRs. Our analytical results can be
applied for similar algorithms and are verified by computer simulations.
Available from: Fabrizio Granelli
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ABSTRACT: This article explores the effects of quantization of feedback information on energy consumption in multiuser wireless communication systems. In order to optimize the energy consumption of the system, the article concentrates on the amount of transmit energy, the additional energy due to quantization and the probability of power outage. Closed form expressions for such parameters are obtained, where the impact of the number of quantization bits is explicitly outlined. An optimization problem is then formulated to find the optimum number of quantization bits able to minimize the consumption in the energy resources. Simulations demonstrate the good results obtainable with the presented optimization strategy, and provide effective validation of the analytic solution presented in the article.
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