Jeremy Nadal

This work proposes a novel pilot structure (PS) to improve channel estimation (CE) in massive multiple-input multiple-output filter bank multi-carrier (mMIMO-FBMC) communications using offset quadrature amplitude modulation (OQAM). Accurate CE is essential to fully benefit from the corresponding spectral efficiency (SE) advantages and robustness against dispersive channels. Unlike orthogonal frequency division multiplexing (OFDM), FBMC/OQAM shows only real-field orthogonality. Therefore, corresponding signals suffer from intrinsic interference, motivating the use of alternative CE techniques. Furthermore, typical CE methods for mMIMO-FBMC systems require large training overheads, particularly for a large number of users or for flat-fading channel conditions over each subcarrier band. To address these issues, this paper proposes a PS that reduces the training overhead by interleaving user pilots in frequency while using conventional OFDM CE techniques, particularly the least squares (LS) method in the context of single-user (SU) and multi-user (MU) scenarios. Analytical expressions for the mean square error (MSE) and the Cramer-Rao lower bound (CRLB) are derived for the estimation technique considering the proposed PSs. Furthermore, performed simulations over the 5G QuaDRiga channel show that the proposed PS improves SE while delivering optimal performance in both preamble-based MU-mMIMO and SU systems.

Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency (EE) of massive multiple-input multiple-output (mMIMO) systems. However, the transmitter architecture may contain several parameters that need to be optimized, such as the power allocated to the antennas and the connections between the antennas and the radio frequency chains. Therefore, finding the optimal transmitter architecture requires solving a non-convex mixed integer problem in a large search space. In this paper, we consider the problem of maximizing the EE of fully digital precoder (FDP) and HBF transmitters. First, we propose an energy model for different beamforming structures. Then, based on the proposed energy model, we develop a self-supervised learning (SSL) method to maximize the EE by designing the transmitter configuration for FDP and HBF. The proposed deep neural networks can provide different trade-offs between spectral efficiency and energy consumption while adapting to different numbers of active users. Finally, towards obtaining a system that can be trained using in-the-field measurements, we investigate the ability of the model to be trained exclusively using imperfect channel state information (CSI), both for the input to the deep learning model and for the calculation of the loss function. Simulation results show that the proposed solutions can outperform conventional methods in terms of EE while being trained with imperfect CSI. Furthermore, we show that the proposed solutions are less complex and more robust to noise than conventional methods.

The applications and services envisioned for the sixth Generation (6G) Internet of Things (IoT) communication systems will place different requirements on the system design. The current 5G waveform, Orthogonal Frequency Division Multiplexing (OFDM), suffers from a list of drawbacks that questions its suitability and ability to fulfill these prerequisites. Filtered multi-carrier waveforms provide viable alternatives for future mobile networks. Based on the best recent advances including contributions by the authors, this work provides a comprehensive assessment of the current status and future challenges related to the design of a next-generation Filter Bank Multi-Carrier/Offset Quadrature Amplitude Modulation (FBMC/OQAM) transceiver. We start by showing how the next-generation FBMC/OQAM waveform can overcome the drawbacks of OFDM. Furthermore, we highlight the advantages and possibilities of extending the next-generation FBMC/OQAM waveform for massive Multiple-Input-Multiple-Output (mMIMO) in well-identified 6G-IoT scenarios. Results show that the next-generation FBMC/OQAM receivers associated with short prototype filters can unleash appealing properties for 6G-IoT applications. Considered a cornerstone for grant-free spectrum and massive access, asynchronous communications are shown to be achievable. Furthermore, a largely improved robustness is observed compared to OFDM under different channel impairments such as carrier frequency offsets, timing offsets, and multipath channels. Compared to the original FBMC/OQAM associated with long filters, this next-generation transceiver achieves a reduction in complexity down to a comparable level to OFDM. Finally, open research opportunities and challenges for the next-generation FBMC/OQAM waveform are discussed.

Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect channel state information (CSI) is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.

Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform decentralized coordinated beamforming with zero or limited communication overhead between APs and NC, for both fully digital and hybrid precoding. The proposed DNNs achieve near-optimal sum-rate while also reducing complexity by $10-24\times $ compared to conventional near-optimal solutions.

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.

The quasi-cyclic (QC) low-density parity-check (LDPC) code is a key error correction code for the fifth generation (5G) of cellular network technology. Designed to support several frame sizes and code rates, the 5G LDPC code structure allows high parallelism to deliver the high demanding data rate of 10 Gb/s. This impressive performance introduces challenging constraints on the hardware design. Particularly, allowing such high flexibility can introduce processing rate penalties on some configurations. In this context, a novel highly parallel and flexible hardware architecture for the 5G LDPC decoder is proposed, targeting field-programmable gate array (FPGA) devices. The architecture supports frame parallelism to maximize the utilization of the processing units, significantly improving the processing rate. The controller unit was carefully designed to support all 5G configurations and to avoid update conflicts. Furthermore, an efficient data scheduling is proposed to increase the processing rate. Compared to the recent related state of the art, the proposed FPGA prototype achieves a higher processing rate per hardware resource for most configurations.

An efficient hardware implementation of correlated Rayleigh fading channel simulator is presented in this paper. It emulates Doppler effects based on the use of Overlap-Save (OLS) method. OLS is used for both, the fading variates generator and the time domain interpolator, leading to a scalable complete solution. Moreover, additional simplifications were introduced to reduce even further algorithmic complexity when compared with the original OLS-based proposal. An efficient hardware implementation is achieved through maximizing the utilization rate of allocated hardware resources. When added to its scalability, this makes the proposal appealing to emulate channels with multipath effects for MIMO systems. Indeed, the proposed parallel architecture enables a throughput of 34 Mega Samples per Second per path at a clock frequency of 275 MHz on Xilinx Virtex-7 FPGA. The achieved throughput allows the support of the most demanding LTE configuration with 20 MHz channel bandwidth. To the best of our knowledge, this is the first ever real-time hardware implementation of OLS-based channel emulator.

Future communication systems are foreseen to support several services with different requirements. Waveform designs based on filter-bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM) can offer interesting advantages in this context, such as low out-of-band power leakage and high spectral efficiency due to the lack of guard intervals. However, downsides of FBMC/OQAM with respect to a typical orthogonal frequency-division multiplexing (OFDM) solution include higher latency, higher complexity and difficulties in adapting some existing OFDM techniques such as MIMO Alamouti. To address these issues, novel FBMC receivers suitable for short prototype filters are proposed. Based on the Overlap- Save algorithm, the proposed receivers improve error-rate performance on multipath channels and support asynchronous communication. We show that complexity can be further reduced by efficiently processing blocks of FBMC symbols jointly, and that user mobility support can be traded off for additional complexity reductions in a flexible way through polynomial decomposition of the equalizer stage. Finally, we show that a block-Alamouti scheme can be applied, and we propose a MIMO equalizer with improved error-rate performance on time-varying channels, compared to the typical FBMC block-Alamouti equalizer.