As touch-enabled teleoperation applications envisioned in the Tactile Internet are highly sensitive to latency, minimizing Medium Access Delay is essential. Two commonly employed resource scheduling schemes for this purpose are Semi-Persistent Scheduling (SPS) and Grant-Free Access (GFA) with M-repetition transmission. While SPS is well-suited for periodic traffic and GFA is for sporadic transmissions, the bursty and dynamic nature of haptic traffic in the Tactile Internet necessitates a hybrid scheduling policy that adapts to varying traffic states. To address this challenge, we propose a Diffusion-based Offline Reinforcement Learning framework that efficiently learns effective scheduling policies from static datasets, rather than online interaction. Using synthetic traffic datasets from the human experiment and the 5G emulator, the simulation results show a general improvement over other offline RL benchmarks.

Tactile Internet has emerged as a cutting-edge application in 5G, aligning with requirements of Ultra-Reliable Low-Latency Communications (URLLC), and is expected to be an essential service in 6G. TI service traffic is characterized by a high burstiness, and the design of effective resource reservation strategies remains an open research in this context. To address this challenge, we formulate the resource reservation problem in the Tactile Internet as an online decision-making task and address it by applying Optimistic Constrained Online Convex Optimization, profiting from Tactile Internet traffic prediction capabilities. We validate the proposed optimistic algorithm by utilizing a traffic dataset based on a self-developed haptic-visual testbed, demonstrating the optimistic algorithm’s superior performance compared to the non-optimistic baseline.

The wide appeal of fuel-efficient transport solutions is constantly increasing due to the major impact of the transportation industry on the environment. Platooning systems represent a relatively simple approach in terms of deployment toward fuel-efficient solutions. This paper addresses the reduction of fuel consumption in platooning systems attainable by dynamically switching between two control policies: Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC). The switching rule is dictated by a Deep Reinforcement Learning (DRL) technique to overcome unpredictable platoon disturbances and to learn appropriate transient shift times while maximizing fuel efficiency. However, due to safety and convergence issues of DRL, our algorithm establishes transition times and minimum periods of operation of ACC and CACC controllers instead of directly controlling vehicles. Numerical experiments show that the DRL agent outperforms both static ACC and CACC versions and the threshold logic control in terms of fuel efficiency while also being robust to perturbations and satisfying safety requirements.

We study in this paper the design of the vehicle-to-everything (V2X) network for vehicle platooning scenarios. We focus on the impact of packet relaying using Road Side Units (RSU) on the application-level performance, namely the inter-vehicle distance in the platoon. The RSU extends the coverage range of the platoon leader and allows handling the well-known Predicted Cooperative Adaptive Cruise Control (PCACC) scheme. We develop a Markov model for evaluating the performances of the different communication links (inter-vehicle and vehicle-to-RSU), using licensed or unlicensed spectrum. We then develop a cross-layer approach that adapts the application layer (PCACC control parameters) to the observed Medium Access Layer (MAC) performance. The simulations results prove that relaying allows to greatly reduce both the communication link failures and the inter-vehicular distances while introducing minimal delay in the system. Furthermore, even if using licensed spectrum for the relay link it drastically reduces the Packet Error Rate (PER) compared to the unlicensed spectrum case, the robustness of the application layer scheme makes the latter case viable for the considered scenario.

This article studies cooperative vehicle platooning under different communications approaches and introduces a new dynamic control scheme based on the Predicted Cooperative Adaptive Cruise Control (PCACC). We start by a performance comparison of different communication technologies including VLC (Visible Light Communication) and V2V (Vehicle-to-Vehicle), with or without relaying via RSU (Road Side Unit), and assess their suitability to fully cooperative and semi-autonomous control schemes. Different from the state of the art, our main design goal is the robustness in terms of an extremely low probability of emergency braking under varying communication conditions and bursts of packet losses. In the light of the performance results, we propose an adaptive control mechanism that tunes the control gains as a function of the observed radio link quality and demonstrate, via extensive simulations, a significantly better performance compared to static control strategies.

This article addresses communication and control aspects of platooning systems with the related challenges introduced by the overlap of both areas. The main objective is to provide a dynamic control mechanism where the parameters of the well-known Predicted Cooperative Adaptive Cruise Control (PCACC) are adapted based on the observed quality of the V2V (Vehicle-to-Vehicle) communication links. Different from the state of the art, our main design goal is the minimization of inter-vehicular distances while being robust in terms of an extremely low probability of emergency braking. A new adaptive control scheme based on the offline optimization of the control gains is proposed. We evaluate the new approach in a highway scenario and show the improvements obtained by the dynamic adaptation of the control parameters over static control strategies.

Wireless communications is nowadays an important aspect of robotics. There are many applications in which a robot must move to a certain goal point while transmitting information through a wireless channel which depends on the particular trajectory chosen by the robot to reach the goal point. In this context, we develop a method to generate optimum trajectories which allow the robot to reach the goal point using little mechanical energy while transmitting as much data as possible. This is done by optimizing the trajectory (path and velocity profile) so that the robot consumes less energy while also offering good wireless channel conditions. In this article, we consider a realistic wireless channel model as well as a realistic dynamic model for the mobile robot (considered here to be a drone). Simulations results illustrate the merits of the proposed method.

In this work, we consider a wireless communication system consisting of multiple rotary-wing unmanned aerial vehicles (UAVs) used as aerial base stations (ABSs) in order to provide downlink connectivity to the user terminals (UEs) on the ground. Towards investigating power efficient deployment strategies for such a system, the contribution of this article is twofold: we formalize the relevant multi-objective optimization problem, and secondly develop Particle Swarm Optimization (PSO) based techniques for optimization of the individual objectives, which are then exploited in an iterative manner. The relevant optimization objectives for reducing the total power consumed are the number of base stations (BSs) and their transmit powers. The optimization is performed while assuring minimum quality-of-service constraints (QoSs) such as per-user coverage probability and per-user rate. Through system level simulations, we show that the developed approach ensures great reductions for both the number of base stations as well as their individual transmit power, thus saving initial deployment cost as well as reducing operational costs induced due to energy consumption.

This paper proposes and analyzes a stochastic multiagent opinion dynamics model. We are interested in a multi-leveled opinion of each agent which is randomly influenced by the binary actions of its neighbors. It is shown that, as far as the number of agents in the network is finite, the model asymptotically produces consensus. The consensus value corresponds to one of the absorbing states of the associated Markov system. However, when the number of agents is large, we emphasize that partial agreements are reached and these transient states are metastable, i.e., the expected persistence duration is arbitrarily large. These states are characterized using an N-intertwined mean field approximation (NIMFA) for the Markov system. Numerical simulations validate the proposed analysis.

In this paper, we propose simple one-shot power control functions and assess their performance both through analytical and numerical results. The proposed functions only assume individual channel state information (CSI) at each transmitter and are based on channel inversion and more importantly on thresholding; a transmitter uses zero power if the channel gain is below a threshold. Although the idea of thresholding has been used for maximizing spectral efficiency, it has not been used for maximizing (the total network) energy-efficiency (EE), which is measured here in terms of sum-EE. More specifically, we prove the optimality of the proposed policy in asymptotic regimes such as the low and high interference scenarios. We also prove that the expected sum-energy is individually quasi-concave with respect to each of the thresholds; this allows us to provide a low-complexity algorithm which can be run offline to find good thresholds. Through numerical simulations, we show that the simple idea of thresholding provides very appreciable gains.

Ultra-Reliable and Low Latency Communications (URLLC) is a challenging class of services to be supported by the fifth generation of mobile networks (5G). Among the URLLC services, many use cases, especially those related to factory automation, involve communications with relatively static radio conditions and a periodic generation of control or data packets. The transmission of these packets requires extremely low latency and ultra-reliable communication to enable realtime control of automation processes. In this paper, we discuss a mechanism of deterministic resource allocation to meet the URLLC requirement in terms of reliability and latency, including initial transmissions and controlled retransmissions. A joint resource allocation and modulation and coding schemes selection is performed so that the resource consumption is minimized, subject to latency and reliability constraints. We show that when applying the proposed resource allocation technique it is possible to achieve very low error rates.

In many resource allocation problems, optimal allocation strategies must be determined when only a quantized version of the relevant parameters are available, for instance, power allocation in wireless communications. The contribution of this work is threefold. First, the quantization problem is revisited and a framework which encompasses the classical problem of quantization is proposed. Instead of minimizing the distortion, the goal is to minimize the gap between the maximum of a general payoff function (which would be reached by knowing all parameters of the function) and what is effectively reached when only the quantized version of the parameters is available. Then, to determine such a quantizer, the well-known Lloyd-Max algorithm is generalized. At last, we show how this framework can be applied to the problem of power control in wireless communications; the obtained numerical results clearly show the potential of such a framework.

A novel technique is proposed, which enables each transmitter to acquire global channel state information (CSI) from the sole knowledge of individual received signal power measurements, which makes dedicated feedback or inter-transmitter signaling channels unnecessary. To make this possible, we resort to a completely new technique whose key idea is to exploit the transmit power levels as symbols to embed information and the observed interference as a communication channel the transmitters can use to exchange coordination information. Although the used technique allows any kind of low-rate information to be exchanged among the transmitters, the focus here is to exchange local CSI. The proposed procedure also comprises a phase which allows local CSI to be estimated. Once an estimate of global CSI is acquired by the transmitters, it can be used to optimize any utility function which depends on it. While algorithms, which use the same type of measurements, such as the iterative water-filling algorithm, implement the sequential best-response dynamics (BRD) applied to individual utilities, here, thanks to the availability of global CSI, the BRD can be applied to the sum-utility. Extensive numerical results show that significant gains can be obtained and, this, by requiring no additional online signaling.

A new problem of optimizing a wireless mobile terminal trajectory under a given communication constraint is introduced. The mobile or vehicle has to move from a given starting point to a target point while uploading/downloading a given amount of data; this contrasts with the classical mobile communications paradigm where the communication and motion aspects are assumed to be independent. To reach the two aforementioned objectives, the mobile has to move sufficiently close to the wireless base station, while accounting for the energy cost due to motion. This setup is formalized here and leads us to determining non-trivial trajectories for the mobile. Remarkably, a counterpart of the Snell-Descartes law for the light propagation is exhibited (see Prop. 2) for the optimal trajectory of the mobile when the latter crosses zones in which the available data rates are different.

In this paper, we study the inter-linked problems of wireless service provider (WSP) selection of users and bandwidth allocation of WSPs in multi-tier heterogeneous cellular networks employing the approach combining stochastic geometry and game theory. In particular, the expected average user achievable rate is calculated by modeling the distributions of users and base stations (BSs) as independent homogeneous Poisson point processes. Moreover, a hierarchical game framework is presented to model the complicated interactions among users and WSPs. Wherein, the evolutionary game, non-cooperative game, and multi-leader multi-follower Stackelberg game models are, respectively, adopted to formulate the competition among users, competition among WSPs, and cyclic dependence between users and WSPs. According to backward induction, the formulated Stackelberg game would be solved after the formulated evolutionary game and non-cooperative game are sequentially studied. For the evolutionary game, both the closed-form expression and the asymptotically stability of its evolutionary equilibrium (EE) were analyzed. Then, conditioned on the obtained EE, the existence of Nash equilibrium (NE) for the non-cooperative bandwidth allocation game is established; furthermore, a sufficient condition for the uniqueness of the NE is derived. Finally, extensive simulation results verify both the validity of our analysis and the effectiveness of the proposed scheme.

In this work, the inter-linked problems of bandwidth allocation for wireless service providers (WSPs) and WSP selection for users in heterogeneous cellular networks (HCNs) are addressed by employing a multi-hierarchical game framework. Wherein, while the interaction between users are modelled using evolutionary game theory, the interactions between competing WSP's are modelled as a non-cooperative spectrum bandwidth allocation game (N-BAG). Moreover, the interaction between the WSPs and users is modelled as a multi- leader multi-follower Stackelberg game. After that, for the formulated evolutionary game, the existence and uniqueness of the evolutionary equilibrium (EE) was investigated. Conditioned on the obtained EE, the existence of a Nash equilibrium (NE) for the proposed N-BAG has been further proven and an offline algorithm to achieve the equilibrium state was proposed. Finally, simulation results verify the validity of our analysis and demonstrate that a unique NE would be achieved by the HCNs adopting the developed algorithm.

In this work, a heterogeneous cellular network with caching enabled at the small cell aggregators is analyzed. The caching technology is enabled to help offload some of the popular traffic requests onto the cache. In this framework, a network congestion game is formulated to study the evolution of traffic when every user as an independent decision maker decides its choice of communication, i.e, via the macro cell or the caching enabled small cells. To study the effect of the cache on the network delay, a numerical simulation is implemented. The file popularity is modeled using the well known Zipf-distribution and the network delay is studied as a function of the cache buffer size and network traffic. In summary, these results can be utilized by network operators to deploy infrastructure efficiently.

In this study, the authors investigate energy efficiency in antenna selection multi‐input multi‐output automatic repeat request (MIMO ARQ) wireless systems. The authors first derive an approximate expression for the average frame‐error rate (FER) in antenna selection MIMO systems over quasi‐static Nakagami‐ m fading channels. The FER approximation is then used to obtain an analytical expression of an energy‐efficiency metric that is defined as the total energy required to successfully deliver one information bit. The authors prove that this energy‐efficiency metric is a quasi‐convex function with respect to the average signal‐to‐noise ratio value. Based on this analysis, the authors obtain the optimal value of the average energy per transmitted data symbol such that the total energy consumption in the system is minimised. The results show that the energy efficiency in antenna selection MIMO ARQ systems is improved when the number of equipped antennas is increased. Simulation results are provided to validate the analysis.

In this paper, we explore the physical limits of successful information transfer in a point-to-point communication system. In interest of studying the fundamental limits imposed by physics on communication systems in general, we model a simple generic system that enables us to make some basic inquiries about the energy efficiency in information transfer and processing. We use ideas from thermodynamics such as Szilard engine to represent information bits. We further use ideas from electromagnetic theory for transfer of information, and information theory to define the energy efficiency metric. We find the upper limit of this efficiency and conditions at which it can be achieved.

In this work, we propose a new energy efficiency metric which allows one to optimize the performance of a wireless system through a novel power control mechanism. The proposed metric possesses two important features. First, it considers the whole power of the terminal and not just the radiated power. Second, it can account for the limited buffer memory of transmitters which store arriving packets as a queue and transmit them with a success rate that is determined by the transmit power and channel conditions. Remarkably, this metric is shown to have attractive properties such as quasi-concavity with respect to the transmit power and a unique maximum, allowing to derive an optimal power control scheme. Based on analytical and numerical results, the influence of the packet arrival rate, the size of the queue, and the constraints in terms of quality of service are studied. Simulations show that the proposed cross-layer approach of power control may lead to significant gains in terms of transmit power compared to a physical layer approach of green communications.