Mutual information bounds for MIMO channels under imperfect receiver CSI

2011 IEEE International Conference on Communications (ICC)

Joham

Fonollosa

2011

Self Cite

For single-user MIMO channels with partial receiver CSI, we study the difference between a lower bound and two alternative upper bounds of the mutual information achieved with Gaussian codebooks. These differences are termed bound gaps Δ and δ, respectively. The latter may serve to derive a capacity bound gap. In contrast to previous studies, we assume that the channel estimation error statistics are not given a priori, but depend on the parameters of a training routine, in which a pilot sequence is transmitted, and where the channel realization is linearly estimated. Under these conditions, we successively determine analytic upper and lower bounds on the mutual information bound gap Δ. We further study the asymptotic behavior of said bound gaps for high SNR and a large number of antennas. This allows us to prove, for example, that for MISO channels and a certain class of semicorrelated MIMO channels, when the training and transmit power levels are equal, the capacity is approached to within min(N T, NR) bits by the capacity bounds, where N T and NR stand for the number of transmit and receive antennas, respectively.

Section: A Lower Boundmentioning

confidence: 99%

Section: Definition Of Bound Gaps δ and δ A Mutual Information mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On a Mutual Information and a Capacity Bound Gap of Pilot-Aided MIMO Channels

2011 IEEE International Conference on Communications (ICC)

Joham

Fonollosa

2011

Self Cite

“…Additionally, the standard expression for the mutual information for the Gaussian vector BC with Gaussian signaling, e.g., E[log 2 (1 + SINR k )] for user k, is not applicable for erroneous receiver CSI. Thus, we have to resort to a lower bound that is valid for Gaussian signaling (e.g., [10], [11], [12])…”

Section: Mutual Information Lower Boundmentioning

confidence: 99%

Rate balancing in the vector BC with erroneous CSI at the receivers

Joham¹,

Grundinger²,

2013 47th Annual Conference on Information Sciences and Systems (CISS)

et al. 2013

Self Cite

Abstract-For the vector broadcast channel (BC), the case of erroneous channel state information (CSI) at the receiver is considered. Employing a well established lower bound for the mutual information with Gaussian signaling, a rate balancing problem is formulated where the rates of the different users are maximized under a transmit power constraint, but the rates of the different users have fixed ratios. A duality w.r.t. the signalto-interference-and-noise ratio (SINR) between the vector BC with erroneous receiver CSI and an appropriately constructed vector multiple access channel (MAC) is established. Based on the observation that an interference function can be defined in the dual vector MAC that is standard, an iterative algorithm can be found for an appropriately formulated quality-of-service (QoS) optimization that is used for solving the balancing problem.

Joint pilot and precoder design for optimal throughput

2011 IEEE International Symposium on Information Theory Proceedings

Joham

Fonollosa

2011

Self Cite

For single-user, multiple-input multiple-output (MIMO) channels with Rayleigh fading correlated at the transmitter side, and where the receiver only has partial channel knowledge in form of an MMSE channel estimate, we study the joint optimization of the linear precoder and the pilot (training) sequence under the constraint of prescribed transmit power and training energy budgets. Although this joint problem is generally not convex itself, we can show that the two marginal problems of optimizing either the pilot sequence or the precoder when the other variable is fixed, are convex. Furthermore, we characterize the jointly optimal transmit and training directions. Finally, we propose a full characterization of the Pareto efficient joint power loading strategies for the case of two transmit antennas, and illustrate the behavior of the jointly optimal solution.