This work presents a novel approach for estimating the minimum distance of concatenated convolutional codes. Unlike most prior methods, the proposed approach relies solely on encoder-side properties, eliminating the need for a decoder in the estimation process. To this end, we introduce an alternative method for identifying return-to-zero sequences, enabling a lowcomplexity, reliable minimum distance estimation. The proposed method is flexible and applicable to both serial and parallel concatenated structures. The results confirm that this approach produces exact minimum distance values for several sizes of the LTE turbo code family while significantly reducing processing time and computational complexity compared to existing methods. For example, in a challenging case study, applying the method to an LTE turbo code with tailbiting termination of length$K=6144$bits yields an estimated minimum distance of 50 and a multiplicity of 12288 in 100 minutes on a standard personal computer - a reduction by several orders of magnitude in comparison to state of the art methods.

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.

Massive multiple-input multiple-output (mMIMO) systems and filtered multi-carrier waveforms have recently emerged as hot research topics in next-generation wireless networks. This paper extends the use of Overlap-Save Filter Bank Multi-Carrier (FBMC) receivers to mMIMO communication systems and investigates the corresponding FBMC transceiver advantages over OFDM. To this aim, we derived the signal-to-interference ratio (SIR) expressions analytically under several channel impairments such as timing offsets and carrier frequency offsets. Furthermore, we conducted an asymptotic study on the performance of FBMC augmented with our proposed receivers in the context of massive MIMO systems. While still outperforming OFDM, results for FBMC show that increasing the number of base station (BS) antennas does not increase the SINR unboundedly. We have validated the proposed analytical study by comparing its results with those obtained from Monte Carlo simulations and confirmed the superiority of the proposed receivers over those in mMIMO literature. Moreover, analytical and simulation results confirm that the Overlap-Save FBMC receiver can support asynchronous communications in the context of mMIMO, a cornerstone for grant-free communications and massive access.

A crucial element of cellular communication networks is the achievable spectral efficiency (SE). Indeed, motivated by the continuous growth in mobile data traffic in wireless networks, improving SE has represented one of the key goals of the 3rd Generation Partner Project (3GPP) along several generations of communication standards. In this paper, we propose a novel transmission scheme based on intentionally overlapping the subcarriers of adjacent users considering two state-of-the-art waveforms: the orthogonal frequency division multiplexing (OFDM) and the filter bank multi-carrier with offset quadrature amplitude modulation (FBMC/OQAM). We investigate the achievable spectral efficiency improvement of the proposed transmission scheme in the presence and absence of timing offset impairments under the tapped delay line B (TDL-B) multipath 3GPP model of the 5G QuaDRiGa channel. According to the test results, the proposed transmission technique achieves up to 20% SE improvement for FBMC/OQAM when associated to the overlap-save FBMC receiver and 12% for OFDM.

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.

Massive MIMO and filtered multi-carrier waveforms are considered as key enabling technologies for next-generation wireless networks. In this work, the Filter Bank Multi-Carrier (FBMC) waveform solution applying our previously proposed short filter and advanced receivers is extended to massive MIMO systems and evaluated in comparison to OFDM. Simulation results are presented for the non-line of sight (NLOS) 3D Urban-Macrocell (UMa) model of the 5G QuaDRiGa channel. Results show that the solution applying the proposed Overlap-Save (OS) and Overlap-Save-Block FBMC (OSB) receivers out-performs OFDM under timing offsets, carrier frequency offsets and Doppler spreads. Moreover, they confirm that the Overlap-Save FBMC receiver can support asynchronous communications in the context of massive MIMO, a cornerstone for grant-free communications and massive access.

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.

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.

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.

The Quasi-Cyclic (QC) Low-Density ParityCode (LDPC) is the key error correction code for the 5thGeneration (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 efficient and flexible hardware architecture for the 5G LDPC decoder is proposed, targeting Field Programmable Gate Array (FPGA) devices and supporting all 5G configurations. The architecture supports frame parallelism to maximize the utilization of the processing units, significantly improving the processing rate. Compared to a recent commercial 5G LDPC decoder, the proposed FPGA prototype achieves a higher processing rate for most configurations while having similar complexity.

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.

In this paper, a new filter-bank multi-carrier (FBMC) receiver targeted at short prototype filters (PFs) is presented. In addition to the typical advantages of FBMC modulation such as improved frequency containment and support of relaxed synchronization, the proposed receiver enables accurate one-tap equalization of the signal despite the use of a short PF, while significantly reducing the complexity by merging the equalization and the standard FBMC receiver. An adapted frame structure is proposed that occupies the same radio resources as a 4G/LTE orthogonal frequency-division multiplexing (OFDM) frame while providing a similar data rate. Moreover, this frame structure is shown to readily support block-based Alamouticoded multiple-input multiple-output transmissions. Simulation results show that the proposed FBMC receiver can outperform an OFDM system on 4G/LTE channel models in both single antenna and Alamouti configurations, while having a better robustness to synchronization errors than a frequency-spread FBMC receiver.