Abstract Sixth-generation (6 G) mobile networks is expected to revolutionize wireless connectivity, paving the way for the Internet of Everything (IoE) and enabling seamless communication between devices, people, and organizations. Advanced techniques, such as non-orthogonal multiple access (NOMA), massive multiple input multiple output (MIMO), and millimeter wave (mmWave) are proposed to deliver high data rates and increase the capacity and spectral efficiency of ultra-dense device networks. However, in scenarios like enhanced remote area communications (eRAC), the use of sub-gigahertz frequencies and TV white spaces (TVWS) is essential to provide Internet services in rural and remote areas, making these advanced techniques impractical. In this context, this paper proposes a new multiple access approach based on the faster-than-Nyquist generalized frequency division multiplexing (FTN-GFDM) waveform to improve user throughput and network capacity in remote areas. Resources in the FTN-GFDM time-frequency grid are allocated with the goal of avoiding inter-user interference (IUI) caused by signal overlap in the time or frequency domains, enabling equalization and detection of user signals without loss of bit error rate (BER) performance. This paper exploits resource allocation strategies and uplink and downlink scenarios for faster-than-Nyquist generalized frequency division multiple access (FTN-GFDMA) schemes, evaluating the effects of interference on BER performance and detection complexity.

With the increasing adoption of 5G networks in smart cities and Industry 4.0 applications, energy efficiency (EE) has become a critical concern, particularly for industrial Internet of Things (IIoT) devices that operate continuously in latency-sensitive environments. This paper presents a novel Paging Signal-Aware Ordered Statistical Decoding (PS-Aware OSD) algorithm to optimize energy use during paging operations. By intelligently predicting paging occasions (PO) based on historical patterns, the algorithm reduces unnecessary wake-ups in extended discontinuous reception (eDRX) cycles. Simulations demonstrate that this approach improves energy efficiency by up to 40%, reduces block error rate (BLER), and improves throughput and latency compared to standard third-generation partnership project (3GPP) paging techniques. The paper discusses practical implementation aspects, acknowledges system limitations, and proposes future work, including the integration of machine learning for dynamic paging optimization and the development of security measures against spoofing or unnecessary battery drain.

The reconfigurable intelligent surface (RIS) technology is ushering in a new paradigm for wireless communication systems. It provides an accessible way for controlling the interaction between electromagnetic waves with the propagation medium. As a consequence, there are currently a wealth of works investigating several aspects of the RIS, such as its construction materials, components, and related channel models, to name a few. One particularly important aspect, however, is the configuration of the RIS elements or reflectors. Assuming that each RIS element is represented by a phase-shift, then the objective of the RIS configuration is to determine the optimum phase-shift combination that maximizes the channel capacity. To this end, we propose a data-driven configuration method for the RIS. In light of the remarkable results recently obtained by machine learning algorithms in wireless communications, it leverages neural network (NN) architectures to configure the RIS phase-shifts. In this work, we investigate the RIS configuration under continuous and discrete phase-shifts, also considering the user equipment (UE) mobility. Numerical results demonstrate that the proposed NN promotes far less reconfigurations of the RIS, consequently reducing the configuration overhead. Moreover, the proposed NN also outperforms other state-of-the-art methods in the mobility scenario.

Cellular communication standards have been established to ensure connectivity across most urban environments, complemented by deployment hardware and facilities tailored for city life. At the same time, numerous initiatives seek to broaden connectivity to rural and developing areas. However, with nearly half the global population still offline, there is an urgent need to drive research toward enhancing connectivity in areas and conditions that deviate from the norm. This article delves into innovative communication solutions not only for hard-to-reach and extreme environments but also introduces “hard-to-serve” areas as a crucial, yet underexplored, category within the broader spectrum of connectivity challenges.We explore the latest advancements in communication systems designed for environments subject to extreme temperatures, harsh weather, excessive dust, or even disasters such as fires. Our exploration spans the entire communication stack, covering communications on isolated islands, sparsely populated regions, mountainous terrains, and even underwater and underground settings. We highlight system architectures, hardware, materials, algorithms, and other pivotal technologies that promise to connect these challenging areas. Through case studies, we explore the application of 5G for innovative research, long range (LoRa) for audio messages and emails, LoRa wireless connections, free-space optics, communications in underwater and underground scenarios, delay-tolerant networks, satellite links, and the strategic use of shared spectrum and TV white space (TVWS) to improve mobile connectivity in secluded and remote regions. These studies also touch on prevalent challenges such as power outages, regulatory gaps, technological availability, and human resource constraints, where we introduce the concept of peri-urban hard-to-serve areas where populations might struggle with affordability or lack the skills for traditional connectivity solutions. This article provides an exhaustive summary of our research, showcasing how 6G and future networks will play a crucial role in delivering connectivity to areas that are hard-to-reach, hard-to-serve, or subject to extreme conditions (ECs).

Low‐density parity‐check (LDPC) codes have excellent performance for a wide range of applications at reasonable complexity. LDPC codes with short blocks avoid the high latency of codes with large block lengths, making them potential candidates for ultra reliable low‐latency applications of future wireless standards. In this work, a novel informed dynamic scheduling (IDS) strategy for decoding LDPC codes, denoted reliability‐based residual belief propagation (Rel‐RBP), is developed by exploiting the reliability of the message and the residuals of the possible updates to choose the messages to be used by the decoding algorithm. A different measure for each iteration of the IDS schemes is also presented, which underlies the high cost of those algorithms in terms of computational complexity and motivates the development of the proposed strategy. Simulations show that Rel‐RBP speeds up the decoding at reduced complexity and results in error rate performance gains over prior work.

The so-called chief executive officer problem suggests that the source message can be recovered at the destination by merging a set of corrupted replicas forwarded by multiple relays, as long as these replicas are sufficiently correlated with the original message. In this paper, we build on Slepian-Wolf's correlated source coding theorem to design a simple, yet efficient power allocation scheme for a multirelay system, in which the direct link is unavailable to convey information. In such a system, the replicas forwarded by the relays are allowed to contain intra-link errors due to previous unreliable hops, and the destination is supposed to retrieve the source message by jointly decoding all received replicas. Importantly, the proposed power allocation is asymptotically optimal at high signal-to-noise ratio.

This paper studies the link level performance of orthogonal frequency division multiplexing (OFDM) and four other advanced waveforms, namely, filtered OFDM (F-OFDM), universal-filtered OFDM (UF-OFDM), filter bank multicarrier (FBMC) and generalized frequency division multiplexing (GFDM). Compared to OFDM, the two filtered variants achieve lower out-of-band (OOB) emissions and can mostly preserve the conventional OFDM-based transceiver design. For the latter two non-orthogonal waveforms, this paper proposes a low complexity implementation of minimum mean square error equalization to jointly tackle the channel and waveform-induced interference. On this basis, the benefits of FBMC and GFDM can be exploited with complexity comparable to the former (quasi-) orthogonal waveforms. The observed benefits include lower peak-to-average power ratio (PAPR) and smaller frame error rate (FER) under challenging doubly dispersive multiple-input multiple-output (MIMO) fading channels. Additionally, linear filtering of FBMC offers an ultra-low OOB emission, while a good compromise in the usage of time and frequency resources can be achieved by circular filtering of GFDM. In the comparison of offset quadrature amplitude modulation (OQAM) versus QAM for non-orthogonal waveforms, OQAM can offer lower PAPR, while smaller FERs can be achieved by QAM in rich multipath fading channels.

The coded modulation (CM) capacity represents the maximum achievable data rate for a CM scheme assuming optimal decoding at the receiver. It is an important analytical tool, providing theoretic limits for near-optimum transceiver design. Next generation wireless communications systems with the use of new technologies, such as massive antennas and nonorthogonal waveforms, tend to be large scale. However, the conventional ways developed for evaluating the CM capacity of small-scale systems expose exponential complexity with respect to the system's input dimension. Therefore, they become infeasible when the input dimension increases by one or two orders of magnitude in large-scale systems of interest. This letter resorts to a lower and upper bound of the CM capacity, allowing for a computationally efficient evaluation with polynomial complexity. In particular, two message passing algorithms, namely expectation propagation (EP) and variational message passing (VMP), are applied to evaluate the bounds. Two applications are examined in the end. The presented results bring valuable information about the system design, allowing one to evaluate the impact of suboptimal implementation in the overall system performance.

Generalized frequency division multiplexing (GFDM) as a nonorthogonal waveform aims at diverse applications in future mobile networks. To evaluate its performance, its capacity limits are of particular importance. Therefore, this paper analyzes its constellation-constrained capacities for cases where the channel state information (CSI) is unknown at the transmitter and perfectly known at the receiver. In frequency selective channels, GFDM may provide advantage over the conventional orthogonal frequency division multiplexing (OFDM) scheme. In order to achieve near-capacity performance, the interaction of data symbols in time and frequency combined with multiple antennas (MIMO) challenges the design of GFDM receivers. This paper, therefore, applies expectation propagation (EP) for systematic receiver design. It is shown that the resulting iterative MIMO-GFDM receiver with affordable complexity can approach optimum decoding performance and outperform MIMO-OFDM in a rich multipath environment. Simulations are also used to illustrate the impact of channel delay spread on the constellation-constrained capacities and on the performance of the novel receiver algorithm.

Within the framework of Monte Carlo simulation, this paper derives a Markov chain Monte Carlo (MCMC) algorithm for efficient detection in multiple-input multiple-output (MIMO) systems using the non-orthogonal multi-carrier waveform termed generalized frequency division multiplexing (GFDM). The proposed MCMC algorithm performs the detection task in frequency domain. Its adopted proposal distribution and Gibbs sampler are tailored under the consideration of complexity and latency for tackling the three-dimensional interference involved in the received signal, i.e., inter-carrier, inter-symbol and inter-antenna interference. By means of simulation, its decoding performance is compared with that achieved by employing the sphere decoding algorithm in a conventional orthogonal frequency division multiplexing (OFDM) based MIMO system. For multi-path fading channels with strong frequency selectivity, the MCMC algorithm proposed for the MIMO-GFDM system can deliver superior performance.

This paper presents a solution for achieving transmit diversity with generalized frequency division multiplexing (GFDM). Compared to previous works, the proposed solution significantly improves symbol error rate (SER) performance and latency, where both aspects are crucial for future 5G cellular networks. It is shown that widely linear estimation at the receiver side can jointly equalize and demodulate the space-time encoded GFDM signal. Moreover, maximum ratio combining can further increase the SER performance with multiple receive antennas. SER performance is evaluated in Rayleigh fading multipath channels.

Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; and control applications require very short response times. We envision a unified physical layer waveform, referred to as generalized frequency division multiplexing (GFDM), to address these requirements. In this paper, we analyze the main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: 1) the means for engineering the waveform's spectral properties; 2) analytical analysis of symbol error performance over different channel models; 3) concepts for MIMO-GFDM to achieve diversity; 4) preamble-based synchronization that preserves the excellent spectral properties of the waveform; 5) bit error rate performance for channel coded GFDM transmission using iterative receivers; 6) relevant application scenarios and suitable GFDM parameterizations; and 7) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.