12 research outputs found
Adaptive Differential Feedback in Time-Varying Multiuser MIMO Channels
In the context of a time-varying multiuser multiple-input-multiple-output
(MIMO) system, we design recursive least squares based adaptive predictors and
differential quantizers to minimize the sum mean squared error of the overall
system. Using the fact that the scalar entries of the left singular matrix of a
Gaussian MIMO channel becomes almost Gaussian distributed even for a small
number of transmit antennas, we perform adaptive differential quantization of
the relevant singular matrix entries. Compared to the algorithms in the
existing differential feedback literature, our proposed quantizer provides
three advantages: first, the controller parameters are flexible enough to adapt
themselves to different vehicle speeds; second, the model is backward adaptive
i.e., the base station and receiver can agree upon the predictor and variance
estimator coefficients without explicit exchange of the parameters; third, it
can accurately model the system even when the correlation between two
successive channel samples becomes as low as 0.05. Our simulation results show
that our proposed method can reduce the required feedback by several kilobits
per second for vehicle speeds up to 20 km/h (channel tracker) and 10 km/h
(singular vector tracker). The proposed system also outperforms a fixed
quantizer, with same feedback overhead, in terms of bit error rate up to 30
km/h.Comment: IEEE 22nd International Conference on Personal, Indoor and Mobile
Radio Communications (2011
A General Rate Duality of the MIMO Multiple Access Channel and the MIMO Broadcast Channel
We present a general rate duality between the multiple access channel (MAC)
and the broadcast channel (BC) which is applicable to systems with and without
nonlinear interference cancellation. Different to the state-of-the-art rate
duality with interference subtraction from Vishwanath et al., the proposed
duality is filter-based instead of covariance-based and exploits the arising
unitary degree of freedom to decorrelate every point-to-point link. Therefore,
it allows for noncooperative stream-wise decoding which reduces complexity and
latency. Moreover, the conversion from one domain to the other does not exhibit
any dependencies during its computation making it accessible to a parallel
implementation instead of a serial one. We additionally derive a rate duality
for systems with multi-antenna terminals when linear filtering without
interference (pre-)subtraction is applied and the different streams of a single
user are not treated as self-interference. Both dualities are based on a
framework already applied to a mean-square-error duality between the MAC and
the BC. Thanks to this novel rate duality, any rate-based optimization with
linear filtering in the BC can now be handled in the dual MAC where the arising
expressions lead to more efficient algorithmic solutions than in the BC due to
the alignment of the channel and precoder indices.Comment: Submitted to IEEE Globecom 2008; Fixed dimensions of channel matrix
H_k and covariance matrix Z_k, slightly modified conclusio
Cognitive Beamforming for Multiple Secondary Data Streams With Individual SNR Constraints
In this paper, we consider cognitive beamforming for multiple secondary data
streams subject to individual signal-to-noise ratio (SNR) requirements for each
secondary data stream. In such a cognitive radio system, the secondary user is
permitted to use the spectrum allocated to the primary user as long as the
caused interference at the primary receiver is tolerable. With both secondary
SNR constraint and primary interference power constraint, we aim to minimize
the secondary transmit power consumption. By exploiting the individual SNR
requirements, we formulate this cognitive beamforming problem as an
optimization problem on the Stiefel manifold. Both zero forcing beamforming
(ZFB) and nonzero forcing beamforming (NFB) are considered. For the ZFB case,
we derive a closed form beamforming solution. For the NFB case, we prove that
the strong duality holds for the nonconvex primal problem and thus the optimal
solution can be easily obtained by solving the dual problem. Finally, numerical
results are presented to illustrate the performance of the proposed cognitive
beamforming solutions.Comment: This is the longer version of a paper to appear in the IEEE
Transactions on Signal Processin