In response to the increasing number of devices expected in next-generation networks, a shift to over-the-air (OTA) computing has been proposed. By leveraging the superposition of multiple access channels, OTA computing enables efficient resource management by supporting simultaneous uncoded transmission in the time and frequency domains. To advance the integration of OTA computing, our study presents a theoretical analysis that addresses practical issues encountered in current digital communication transceivers, such as transmitter synchronization (sync) errors and intersymbol interference (ISI). To this end, we investigate the theoretical mean squared error (MSE) for OTA transmission under sync errors and ISI, while also exploring methods for minimizing the MSE in OTA transmission. Using alternating optimization, we also derive optimal power policies for both the devices and the base station. In addition, we propose a novel deep neural network (DNN)-based approach to design waveforms that improve OTA transmission performance under sync errors and ISI. To ensure a fair comparison with existing waveforms such as raised cosine (RC) and better-than-raised-cosine (BTRC), we incorporate a custom loss function that integrates energy and bandwidth constraints along with practical design considerations such as waveform symmetry. Simulation results validate our theoretical analysis and demonstrate performance gains of the designed pulse over RC and BTRC waveforms. To facilitate testing of our results without the need to rebuild the DNN structure, we also provide curve-fitting parameters for the selected DNN-based waveforms.

One of the main applications expected to be enabled by next-generation networks is computing. The goal-oriented nature of computing allows the use of different implementation techniques, with over-the-air (OTA) computation being one of the main proposed schemes due to its effective resource management and computational efficiency. In this work, we aim at optimizing the waveform of the system in the presence of intersymbol interference (ISI) and sampling error. To this end, we propose a deep neural network (DNN) framework that generates an optimal waveform that minimizes the mean square error (MSE) of the OTA computation system. To ensure that the generated waveform exhibits the same behavior as other common waveforms, weighted energy and spectrum constraints are included in the loss function of the training phase. To better mitigate ISI, the spectrum constraint integrates the roll-off factor of the waveform, allowing for the generation of different waveforms. Simulation results verify that the desired constraints are met and show a significant performance gain over state-of-the-art waveforms.

In this paper, we propose a novel approach to minimize the inference delay in semantic segmentation using split learning (SL), tailored to the needs of real-time computer vision (CV) applications for resource-constrained devices. Semantic segmentation is essential for applications such as autonomous vehicles and smart city infrastructure, but faces significant latency challenges due to high computational and communication loads. Traditional centralized processing methods are inefficient in such scenarios, often resulting in unacceptable inference delays. SL offers a promising alternative by partitioning deep neural networks (DNNs) between edge devices and a central server, enabling localized data processing and reducing the amount of data required for transmission. Our contribution includes the joint optimization of bandwidth allocation, cut layer selection of the edge devices’ DNN, and the central server’s processing resource allocation. We investigate both parallel and serial data processing scenarios and propose low-complexity heuristic solutions that maintain near-optimal performance while reducing computational requirements. Numerical results show that our approach effectively reduces inference delay, demonstrating the potential of SL to improve real-time CV applications in dynamic, resource-constrained environments.

Semantic communications offer a promising approach to decrease network congestion and improve reliability, leading to more sustainable and energy-efficient wireless networks. However, the design of semantic transceivers constrain their effectiveness. This paper introduces a novel multi-carrier system that combines both semantic and Shannon communications, with a focus on text transmission. We formulate an optimization problem that jointly selects the transmission method and allocates power to reduce the transmission delay. Despite the challenges of solving this non-convex problem, we employ alternating optimization techniques to address it and the closed-form solution of the power allocation is extracted. The simulation results verify that jointly selecting semantic and Shannon communications decreases the transmission delay compared to using only one of the schemes.

This article focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless networks. In more detail, we first examine multiaccess edge computing (MEC), which is critical to meeting the growing demand for data processing and computational capacity at the edge of the network, as well as network slicing. We then explore over-the-air (OTA) computing, which is considered to be an approach that provides fast and efficient computation of various functions. We also explore semantic communications, identified as an effective way to improve communication systems by focusing on the exchange of meaningful information, thus minimizing unnecessary data and increasing efficiency. The interrelationship between machine learning (ML) and multiple access technologies is also reviewed, with an emphasis on federated learning (FL), federated distillation (FD), split learning (SL), reinforcement learning (RL), and the development of ML-based multiple access protocols. Finally, the concept of digital twinning and its role in network management is discussed, highlighting how virtual replication of physical networks can lead to improvements in network efficiency and reliability.

Semantic communications are considered a promising beyond-Shannon paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable “similarity” between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and Shannon communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrierHybrid Semantic-Shannoncommunication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-Shannon transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-Shannon communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.

Over-the-air computing (AirComp) has recently attracted considerable attention as an efficient method of data fusion by integrating uncoded communication transmissions with computation thanks to the signal superposition offered by the multiple access channels. However, appropriate processing is required to neutralize the wireless channel effect. As, internet-of-things (IoT) applications through low-cost devices is the main target of AirComp, perfect availability of channel state information (CSI) is not always practical, there is the need to investigate the effect of imperfect CSI on AirComp. Specifically, we present novel closed-form expressions for tight approximations that can be used to design and evaluate AirComp systems. Furthermore, we design a general optimization framework that takes into account both magnitude and phase errors in the CSI. Finally, a pilot retransmission policy is designed, that offers trade-off between resources cost and the gain in the accuracy of the computations. In order to validate its application, a utility function of the cost of retransmission is introduced, namely,Retransmission Policy Cost (RPC), which can incorporate the power or throughput cost opposing to the expected gain of the selected policy. Simulations show the deterioration caused by the imperfect CSI and highlight the added value of the proposed policy under various system conditions.

Over-the-air Computation (AirComp) has attracted significant attention as an efficient way of data fusion by inte-grating uncoded communication transmissions with computation thanks to the superposition offered by the multiple access channels. However, proper pre-processing and post-processing is required to neutralize the wireless channel effect, in order for AirComp to function successfully. Since, internet-of-things (IoT) type of devices with limited capabilities are the target de-mographic of AirComp, having perfect channel state information (CSI) available is not always a practical assumption. In this work, we examine the effect of imperfect CSI on the AirComp system and we design a general optimization framework that takes into account both magnitude and phase errors in CSI. On top of that, a pilot retransmission policy is designed that offers a trade-off between cost of retransmissions and gain in the accuracy of the computations. Simulation results show the deterioration caused by the imperfect CSI and also the value of the proposed policy under various system conditions.