The integration of communication and sensing functions within mmWave systems has gained attention due to the potential for enhanced passive sensing and improved communication reliability. State-of-the-art techniques separate these two functions in frequency, use of hardware, or time, i.e., sending known preambles for channel sensing or unknown symbols for communications. In this paper, we introduce Panoptic, a novel system architecture for integrated communication and sensing sharing the same hardware, frequency, and time resources. Panoptic jointly detects unknown symbols and channel components from data-modulated signals. The core idea is a new beam manipulation technique, which we call compressive sidelobe forming, that maintains a directional mainlobe toward the intended communication nodes while acquiring unique spatial information through pseudorandom sidelobe perturbations. We implemented Panoptic on 60 GHz mmWave radios and conducted extensive over-the-air experiments. Our results show that Panoptic achieves reflector angular localization error of less than 2°while at the same time supporting mmWave data communication with a negligible BER penalty when compared with conventional communication-only mmWave systems.
Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal's polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean-squared error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target SINR leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for MSE minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.
Quantum optimization is poised to play a transformative role in the design of next-generation wireless communication systems by addressing key computational and technological challenges. This paper provides an overview of the principles of adiabatic quantum computing, the foundation of quantum optimization, and explores its two primary computational models: quantum annealing and the gate-based quantum approximate optimization algorithm. By highlighting their core features, performance benefits, limitations, and distinctions, we position these methods as promising tools for advancing wireless communication system design. As a case study, we examine the design of passive reconfigurable intelligent surface beamforming with binary phase-shift resolution, supported by experimental results obtained from real-world quantum hardware.
Vasileios Kouvakis, Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos
et al.
Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage expansion through the use of intermediate commercial or private wireless nodes. To assess the efficiency and limitations of the architecture, we employ Markov chain theory in order to extract a theoretical model with increased engineering insights. Building upon this model, we quantify the latency as well as the security capabilities in terms of probability of successful attack, for three scenarios, namely fixed topology fronthaul network, advanced coverage expansion and advanced mobile node connectivity, which reveal the scalability of the blockchain-RAN architecture.
Telecommunication, Transportation and communications
Nazmul Arefin Khan, Krishna Murthy Gurumurthy, Amir Davatgari
et al.
In recent years, shared E-Scooters (SES) have emerged as one of the most popular and rapidly growing micromobility modes. To better understand the role of SES in urban mobility, it is critical for policymakers and planners to explore the adoption behavior and usage frequency of Shared E-Scooters. This study jointly estimates the Shared E-Scooters' potential adoption and frequency of usage using a zero-inflated ordered probit (ZIOP) model. This approach can be interpreted as whether an individual considers E-scooters as a travel mode alternative, and if so, how frequently they use E-scooters, which also has a zero occurrence. The study uses a dataset from the City of Chicago. The parameter estimation results suggest that various socio-demographics, built environment, accessibility measures and service characteristics have adequate impacts on E-Scooter adoption and usage frequency. This study also implements the model within the POLARIS agent-based transportation system simulator to examine the potential impact of various E-Scooter deployment scenarios. Results suggest that deploying more Shared E-Scooters in the traffic network not only increases the number of E-Scooter trips, but also helps to decrease the person-miles traveled and person-hour traveled. Insights from this study would be useful for planners and policymakers to develop alternative policy strategies associated with emerging mobility.
Quantum Key Distribution (QKD) provides secure communication by leveraging quantum mechanics, with the BB84 protocol being one of its most widely adopted implementations. However, the classical post-processing steps in BB84, such as sifting, error correction, and key verification, often result in significant communication overhead, limiting its efficiency and scalability. In this work, we propose three key optimizations for BB84: (1) PRNG-based predetermined key bit positioning, which eliminates redundant bit exchanges during sifting, (2) hash-based subsequence comparison, enabling lightweight and efficient key verification, and (3) adaptive basis reconciliation, which minimizes the communication costs associated with basis matching. The proposed optimizations achieve a 50% reduction in communication overhead for large key sizes compared to traditional QKD protocols, as demonstrated through rigorous performance analysis. While the focus of this work is on the BB84 protocol, these optimizations are also directly applicable to a broader class of Discrete-Variable QKD (DV-QKD) protocols, such as six-state, B92, and E91, which share a fundamentally similar post-processing structure. This generality highlights the modularity and adaptability of the proposed methods across diverse QKD implementations. The proposed optimizations enhance post-processing efficiency and scalability, enabling practical deployment in bandwidth-limited environments like IoT networks, secure financial systems, and defense communications, thereby supporting broader adoption of quantum communication systems.
Telecommunication, Transportation and communications
Ammar Mohamed Abouelmaati, Sylvester Aboagye, Hina Tabassum
With spectrum resources becoming congested and the emergence of sensing-enabled wireless applications, conventional resource allocation methods need a revamp to support communications-only, sensing-only, and integrated sensing and communication (ISaC) services together. In this letter, we propose two joint spectrum partitioning (SP) and power allocation (PA) schemes to maximize the aggregate sensing and communication performance as well as corresponding energy efficiency (EE) of a semi-ISaC system that supports all three services in a unified manner. The proposed framework captures the priority of the distinct services, impact of target clutters, power budget and bandwidth constraints, and sensing and communication quality-of-service (QoS) requirements. We reveal that the former problem is jointly convex and the latter is a non-convex problem that can be solved optimally by exploiting fractional and parametric programming techniques. Numerical results verify the effectiveness of proposed schemes and extract novel insights related to the impact of the priority and QoS requirements of distinct services on the performance of semi-ISaC networks.
Eleonora Grassucci, Jinho Choi, Jihong Park
et al.
In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing. Among them, semantic communication obtained promising results especially when combined with state-of-the-art deep generative models, such as large language or diffusion models, able to regenerate content from extremely compressed semantic information. However, most of these approaches focus on single-user scenarios processing the received content at the receiver on top of conventional communication systems. In this paper, we propose to go beyond these methods by developing a novel generative semantic communication framework tailored for multi-user scenarios. This system assigns the channel to users knowing that the lost information can be filled in with a diffusion model at the receivers. Under this innovative perspective, OFDMA systems should not aim to transmit the largest part of information, but solely the bits necessary to the generative model to semantically regenerate the missing ones. The thorough experimental evaluation shows the capabilities of the novel diffusion model and the effectiveness of the proposed framework, leading towards a GenAI-based next generation of communications.
Achintha Wijesinghe, Songyang Zhang, Suchinthaka Wanninayaka
et al.
The latest advances in artificial intelligence (AI) present many unprecedented opportunities to achieve much improved bandwidth saving in communications. Unlike conventional communication systems focusing on packet transport, rich datasets and AI makes it possible to efficiently transfer only the information most critical to the goals of message recipients. One of the most exciting advances in generative AI known as diffusion model presents a unique opportunity for designing ultra-fast communication systems well beyond language-based messages. This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models as a specific example of the general goal-oriented communication framework. To better control the regenerated message at the receiver output, our diffusion system design includes a local regeneration module with finite dimensional noise latent. The critical significance of noise latent control and sharing residing on our Diff-GO is the ability to introduce the concept of "local generative feedback" (Local-GF), which enables the transmitter to monitor the quality and gauge the quality or accuracy of the message recovery at the semantic system receiver. To this end, we propose a new low-dimensional noise space for the training of diffusion models, which significantly reduces the communication overhead and achieves satisfactory message recovery performance. Our experimental results demonstrate that the proposed noise space and the diffusion-based generative model achieve ultra-high spectrum efficiency and accurate recovery of transmitted image signals. By trading off computation for bandwidth efficiency (C4BE), this new framework provides an important avenue to achieve exceptional computation-bandwidth tradeoff.
The purpose of the optimization of holiday traffic emergency traffic organization is to solve the problem of serious traffic jams in holiday scenic spots. Based on the prediction of traffic volume and traffic mode division in the future years of the scenic spot, the traffic accident route is analyzed to provide theoretical support for the emergency traffic organization and planning of the scenic spot. This article takes the Shijiazhuang Jinta Bay scenic area as the research object, based on the traffic volume of the Jinta Bay tourist scenic area from 2009 to 2016, analyzes the traffic environment of the scenic area, predicts the traffic demand, and builds a one‐way traffic organization double‐layer optimization model. The simulated annealing algorithm is used to solve the model, an emergency transportation organization optimization plan is formulated, and the feasibility of the plan is verified through VISSIM simulation. The results of the study show that the one‐way traffic organization method reduces the average vehicle delay by 32.2% and the average queue length by 14.5%. The one‐way traffic organization based on branch diversion can more effectively solve the main road jamming and congestion caused by traffic accidents, prevent the occurrence of secondary accidents, and reduce the economic losses of scenic area managers. At the same time, the purpose of ensuring the tourist quality of tourists and the economic interests of scenic spot management departments is ensured.
The large scale reflector array of programmable metasurfaces is capable of increasing the power efficiency of backscatter communications via passive beamforming and thus has the potential to revolutionize the low-data-rate nature of backscatter communications. In this paper, we propose to design the power-efficient higher-order constellation and reflection pattern under the amplitude constraint brought by backscatter communications. For the constellation design, we adopt the amplitude and phase-shift keying (APSK) constellation and optimize the parameters of APSK such as ring number, ring radius, and inter-ring phase difference. Specifically, we derive closed-form solutions to the optimal ring radius and interring phase difference for an arbitrary modulation order in the decomposed subproblems. For the reflection pattern design, we propose to optimize the passive beamforming vector by solving a multi-objective optimization problem that maximizes reflection power and guarantees beam homogenization within the interested angle range. To solve the problem, we propose a constant-modulus power iteration method, which is proven to be monotonically increasing, to maximize the objective function in each iteration. Numerical results show that the proposed APSK constellation design and reflection pattern design outperform the existing modulation and beam pattern designs in programmable metasurface enabled backscatter communications.
Mahyar Shirvanimoghaddam, Ayoob Salari, Yifeng Gao
et al.
In this paper, we consider the federated learning (FL) problem in the presence of communication errors. We model the link between the devices and the central node (CN) by a packet erasure channel, where the local parameters from devices are either erased or received correctly by CN with probability $ε$ and $1-ε$, respectively. We proved that the FL algorithm in the presence of communication errors, where the CN uses the past local update if the fresh one is not received from a device, converges to the same global parameter as that the FL algorithm converges to without any communication error. We provide several simulation results to validate our theoretical analysis. We also show that when the dataset is uniformly distributed among devices, the FL algorithm that only uses fresh updates and discards missing updates might converge faster than the FL algorithm that uses past local updates.
Automotive-Industry 5.0 will use emerging 6G communications to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other Intelligent Transportation System (ITS) entities. Non-Orthogonal Multiple Access (NOMA) and backscatter communications are two key techniques of 6G communications for enhanced spectrum and energy efficiency. In this paper, we provide an introduction to green transportation and also discuss the advantages of using backscatter communications and NOMA in Automotive Industry 5.0. We also briefly review the recent work in the area of NOMA empowered backscatter communications. We discuss different use cases of backscatter communications in NOMA-enabled 6G vehicular networks. We also propose a multi-cell optimization framework to maximize the energy efficiency of the backscatter-enabled NOMA vehicular network. In particular, we jointly optimize the transmit power of the roadside unit and the reflection coefficient of the backscatter device in each cell, where several practical constraints are also taken into account. The problem of energy efficiency is formulated as nonconvex which is hard to solve directly. Thus, first, we adopt the Dinkelbach method to transform the objective function into a subtractive one, then we decouple the problem into two subproblems. Second, we employ dual theory and KKT conditions to obtain efficient solutions. Finally, we highlight some open issues and future research opportunities related to NOMA-enabled backscatter communications in 6G vehicular networks.
Abstract With the rapid development of communication technologies, the quality of our daily life has been improved with the applications of smart communications and networking, such as intelligent transportation and mobile service computing. However, high user demands for quality of service (QoS) are forcing intelligent transportation to continuously improve immediacy and reduce the tasks offloading delay for the internet of vehicles (IoV). To meet the low latency of vehicle tasks offloading, an offloading scheme combining mobile edge computing (MEC) and deep reinforcement learning (DRL), is proposed in this article. Firstly, a realistic map is simulated, while initializing the tasks queue and building a tasks offloading environment with multiple service nodes. Then, an algorithm that combines deep learning with reinforcement learning, that is, the deep Q‐learning network (DQN) algorithm, is developed to optimize the offloading scheme by reducing the offload latency. Finally, given that the complete information cannot be observed effectively in the environment, a long short‐term memory (LSTM) model is applied within the DQN to train its neural network to improve offloading efficiency. The simulation results show that the MEC‐based vehicle tasks offloading can effectively reduce the latency of vehicle offloading.