Luc Deneire

One of the biggest challenges in operating massive multiple-input multiple-output systems is the acquisition of accurate channel state information at the transmitter. To take up this challenge, time division duplex is more favorable thanks to its channel reciprocity between downlink and uplink. However, while the propagation channel over the air is reciprocal, the radio-frequency front-ends in the transceivers are not. Therefore, calibration is required to compensate the RF hardware asymmetry. Although various over-the-air calibration methods exist to address the above problem, this paper offers a unified representation of these algorithms, providing a higher level view on the calibration problem, and introduces innovations on calibration methods. We present a novel family of calibration methods, based on antenna grouping, which improve accuracy and speed up the calibration process compared to existing methods. We then provide the Cramér-Rao bound as the performance evaluation benchmark and compare maximum likelihood and least squares estimators. We also differentiate between coherent and noncoherent accumulation of calibration measurements, and point out that enabling non-coherent accumulation allows the training to be spread in time, minimizing impact to the data service. Overall, these results have special value in allowing to design reciprocity calibration techniques that are both accurate and resource-effective.

In this work, we propose a novel formulation for a precoder design considering practical rate assignment based on the modulation and coding scheme of the long‐term evolution table, beam selection, and power optimization, which exploits the geometric sparsity of the multiuser massive multiple‐input–multiple‐output channel. We consider two different channel models and provide an optimal solution for the joint beam selection and power optimization as well as a heuristic using Lagrangian relaxation. Assuming knowledge of the beamspace channel, the beamspace precoder consists in selecting and optimizing the power of the beams steered to the user equipments (UEs) in order to maximize the signal‐to‐interference‐plus‐noise ratio at the UE. Furthermore, we propose three additional simple heuristics with low complexity. For these three heuristics, we solve the problem in two steps: (1) selection of beams based on the maximal ratio transmission principle and (2) power allocation per UE. Simulation results show that our optimal solution can achieve a better performance than the zero‐forcing beamforming scheme in the high‐sparsity scenarios. Besides, compared to the linear maximal ratio transmission precoder, the proposed low‐complexity heuristics improve the performance under a scenario with channel sparsity.

Cognitive radio, which enables smart use of wireless resources, is a key ingredient to achieve high spectral efficiency. LTE, the latest evolution of cellular standards, is widely adopted and also targets high spectral efficiency. Hence, to enable wide adoption of cognitive radio, using LTE as the physical layer is a natural choice. Targeting a real-time implementation of LTEbased cognitive radio, we focus on spatial interweave cognitive radio, in which a secondary user uses an antenna array to perform null-beamforming in the primary user's direction, hence reusing the spectrum spatially. To allow this, without any help from the primary system, we use the time-division duplex mode and take advantage of the channel reciprocity. However, this reciprocity is jeopardized by the mismatch between the RF front-ends. Hence, we design a calibration protocol to restore it. A key contribution is that this cognitive system calibration does not require cooperation from the primary user. The whole system is implemented and evaluated on EURECOM's experimental OpenAirInterface platform. Performance results are presented, showing the feasibility of spatial interweave cognitive radio on a real-time platform.