On the Capacity of Vector Gaussian Channels With Bounded Inputs

The capacity of a deterministic multiple-input multiple-output channel under the peak and average power constraints is investigated. For the identity channel matrix, the approach of Shamai et al. is generalized to the higher dimension settings to derive the necessary and sufficient conditions for the optimal input probability density function. This approach prevents the usage of the identity theorem of the holomorphic functions of several complex variables which seems to fail in the multi-dimensional scenarios. It is proved that the support of the capacity-achieving distribution is a finite set of hyper-spheres with mutual independent phases and amplitude in the spherical domain. Subsequently, it is shown that when the average power constraint is relaxed, if the number of antennas is large enough, the capacity has a closed-form solution and constant amplitude signaling at the peak power achieves it. Moreover, it will be observed that in a discrete-time memoryless Gaussian channel, the average power constrained capacity, which results from a Gaussian input distribution, can be closely obtained by an input where the support of its magnitude is a discrete finite set. Finally, we investigate some upper and lower bounds for the capacity of the non-identity channel matrix and evaluate their performance as a function of the condition number of the channel

Information theoretic limits of MIMO wireless networks with bounded input and imperfect CSIT

In this thesis, we investigate some information theoretic limits of two specific types of MIMO wireless networks. In the first one, the effect of channel uncertainty at the transmitter (due to estimation error, feedback latency, and so on) in MIMO broadcast channels is investigated. In this setting, we capture this imperfectness in the bounds for the DoF region of the channel. The second one is the point to point deterministic MIMO channel with input amplitude constraint. For certain settings, the capacity of this channel is derived, while for the general problem, upper and lower bounds for the capacity are obtained.Open Acces

Enhancing LTE with Cloud-RAN and Load-Controlled Parasitic Antenna Arrays

Cloud radio access network systems, consisting of remote radio heads densely distributed in a coverage area and connected by optical fibers to a cloud infrastructure with large computational capabilities, have the potential to meet the ambitious objectives of next generation mobile networks. Actual implementations of C-RANs tackle fundamental technical and economic challenges. In this article, we present an end-to-end solution for practically implementable C-RANs by providing innovative solutions to key issues such as the design of cost-effective hardware and power-effective signals for RRHs, efficient design and distribution of data and control traffic for coordinated communications, and conception of a flexible and elastic architecture supporting dynamic allocation of both the densely distributed RRHs and the centralized processing resources in the cloud to create virtual base stations. More specifically, we propose a novel antenna array architecture called load-controlled parasitic antenna array (LCPAA) where multiple antennas are fed by a single RF chain. Energy- and spectral-efficient modulation as well as signaling schemes that are easy to implement are also provided. Additionally, the design presented for the fronthaul enables flexibility and elasticity in resource allocation to support BS virtualization. A layered design of information control for the proposed end-to-end solution is presented. The feasibility and effectiveness of such an LCPAA-enabled C-RAN system setup has been validated through an over-the-air demonstration

Finite Blocklength Analysis of Gaussian Random Coding in AWGN Channels under Covert Constraint

This paper considers the achievability and converse bounds on the maximal channel coding rate at a given blocklength and error probability over AWGN channels. The problem stems from covert communication with Gaussian codewords. By re-visiting [18], we first present new and more general achievability bounds for random coding schemes under maximal or average probability of error requirements. Such general bounds are then applied to covert communication in AWGN channels where codewords are generated from Gaussian distribution while meeting the maximal power constraint. Further comparison is made between the new achievability bounds and existing one with deterministic codebooks.Comment: 18 page