Search

29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536, In this paper, precoding methods used in downlink multi-user, multi-input multi-output systems (downlink MU-MIMO) are investigated. Precoding methods provide inter-user interference management and are designed to maximize the achievable sum rate. Downlink MU-MIMO systems can be underloaded, critically loaded or overloaded depending on the number of antennas at the base station and users and the number of users. This study provides a comparison between the generalized power iteration precoding (GPIP) (which is newly proposed in the literature) with the minimum mean square error precoding (MMSE). It is observed that the GPIP method results in similar or worse sum rate performance than the MMSE method in all load cases. Moreover, the MMSE method has lower complexity. © 2021 IEEE.

29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536, In this paper, precoding methods used in downlink multi-user, multi-input multi-output systems (downlink MU-MIMO) are investigated. Precoding methods provide inter-user interference management and are designed to maximize the achievable sum rate. Downlink MU-MIMO systems can be underloaded, critically loaded or overloaded depending on the number of antennas at the base station and users and the number of users. This study provides a comparison between the generalized power iteration precoding (GPIP) (which is newly proposed in the literature) with the minimum mean square error precoding (MMSE). It is observed that the GPIP method results in similar or worse sum rate performance than the MMSE method in all load cases. Moreover, the MMSE method has lower complexity. © 2021 IEEE.

This work seeks to design a pilot signal, under a power constraint, such that the channel can be estimated with minimum mean square error. The procedure we derive does not assume Kronecker structure on the underlying covariance matrices, and the pilot signal is obtained in three main steps. Firstly, we solve a relaxed convex version of the original minimization problem. Secondly, its solution is projected onto the feasible set. Thirdly we use the projected solution as starting point for an augmented Lagrangian method. Numerical experiments indicate that this procedure may produce pilot signals that are far better than those obtained under the Kronecker structure assumption., QC 20140120

Compressive (2:1) joint source-channel coding using direct mappings from source to channel symbol space is considered. To enable the use of prior information due to e.g. correlated samples at the receiver, or statistical knowledge of the source, minimum mean square error decoding is considered. The prior information is incorporated in the form of the a-priori distribution in the decoding. Four mapping methods are presented and evaluated using the generic Bayesian minimum mean square error estimator. The schemes are evaluated for transmitting a memoryless Gaussian source over additive white Gaussian noise channel with a quadratic distortion measure. The simplicity of implementation and applicability to a wider variety of sources is discussed., QC 20110225