This study investigates the integration of intent-sharing within a multi-agent platooning system using a reinforcement learning-based control approach. The method enables vehicles to exchange both their current state information and predictions of future speed and acceleration, thereby enhancing decision-making and coordination. By incorporating intent-sharing, the system effectively reduces spacing errors and maintains optimal inter-vehicle spacing, which results in smoother acceleration and deceleration profiles; critical factors for passenger comfort, safety, and platoon string stability. Experimental data confirm that intent-sharing significantly improves string stability, safety, comfort, and gap-keeping accuracy in connected vehicle platooning.

This paper addresses the optimization of task offloading and scheduling in vehicular edge computing (VEC) by integrating task prioritization incentives across distributed service areas. To maximize task completion ratios within strict deadlines while minimizing latency and energy consumption, we model the problem as a Partially Observable Markov Decision Process (POMDP). Building on this formulation, we propose a multi-agent deterministic policy gradient approach based on MADDPG, where each agent dynamically selects priority classes, assigns tasks to edge servers, and optimizes CPU frequency allocation using only local observations. Simulation results demonstrate that our MADDPG framework not only meets task prioritization objectives but also achieves performance comparable to, and in some cases exceeding, that of centralized value function-based methods. These findings highlight the ad-vantages of distributed decision-making and localized resource management in complex VEC environments.

This paper presents an extension to ETSI redundancy mitigation rules (RMRs) aimed at improving the efficiency of collective perception services in vehicular networks. The proposed mechanisms integrate the ETSI RMRs with distance-based criteria to exclude redundant locally perceived objects from collective perception messages (CPMs). The study evaluates the performance of several strategies: no RMR, self-announcement-based (SAB) RMR, the proposed distance-based SAB (DSAB) RMR, frequency-based (FB) RMR, and the proposed distance-based FB (DFB) RMR, across various market penetration rates. Key metrics analyzed include redundancy level (RL), channel busy ratio (CBR), and environment awareness ratio (EAR). The findings demonstrate that the distance-based extension consistently outperforms traditional methods in balancing redundancy, channel load, and environmental awareness, particularly at higher market penetration rates.

This paper primarily focuses on vehicular computation offloading and scheduling based on prioritized incentives. The objective of this work is to maximize task completion within designated deadlines while concurrently minimizing energy consumption and latency. To achieve this, a prioritized Deep Q Network (DQNP) has been proposed to optimize long-term rewards by dynamically selecting a priority class, server, and CPU frequency at each computational step. Simulation results demonstrate that, compared with baseline algorithms, the proposed DQNP can effectively choose tasks, ensuring minimal energy consumption, reduced latency, and improved completion rates, especially for tasks in the highest priority class. Additionally, the findings shed light on how the model alleviates the resource starvation challenge faced by low-priority tasks.

In this paper, we use the Perceived Safety Analysis Framework (PSAF) to assess the awareness of vehicles performing an unprotected left turn at unsignalized intersections. PSAF is an analytical method developed to quantify the awareness of vehicles to surrounding safety-critical road users in traffic. We derive safety conditions for unprotected left turns using surrogate safety measures and right of way rules, and determine which road users are safety-critical to the left-turning ego vehicle. Then, we evaluate the Perceived Safety Error based on whether the ego vehicle can detect critical road users (CRUs) using sensors (such as camera or radar) and via vehicle-to-everything (V2X) communication. We demonstrate that for intersections with sparse traffic, vehicle-to-vehicle (V2V) communication may be insufficient for left-turning vehicles to get full awareness of CRUs, and vehicle-to-infrastructure (V2I) communication helps to eliminate awareness gaps.

In this paper, we propose an optimization-based strategy that utilizes vehicle-to-everything (V2X) communication in order to resolve conflicts between vehicles of different automation levels. The strategy consists of a decision checking mechanism and a control law to adjust the decision of an ego vehicle in a certain maneuver based on status update messages received from a remote vehicle involved in that maneuver. Using numerical simulations with real highway data, we demonstrate the proposed opportunistic strategy and show how it improves safety and maximizes the time efficiency of the ego vehicle. We also highlight the benefits of the strategy by comparing the results with an existing conservative strategy.

Connected and automated vehicles (CAVs) are expected to make use of Vehicle-to-Everything (V2X) communication to exchange sensor and trajectory data. Using this data, CAVs can coordinate their maneuvers for safer and more efficient driving. ETSI and SAE are currently working to define standards for maneuver coordination and cooperative driving. The current approach at ETSI is based on a distributed solution where vehicles use Vehicle-to-Vehicle (V2V) communication to exchange their planned and desired trajectories. This study evaluates the potential benefits of the ETSI Maneuver Coordination Service to improve the traffic speed using a unique simulation tool. To do so, we first evaluate the impact of maneuver coordination on the vehicles involved in a coordination process. We also evaluate the effects of maneuver coordination on the overall traffic compared to scenarios without coordination. Our study shows that maneuver coordination can yield significant benefits to traffic mobility, however these improvements are intimately linked to the surrounding traffic environment and the specifications of the coordinated maneuver. This highlights the need for more detailed studies on the design of maneuver coordination protocols that should consider the vehicular context when executing and configuring the coordination process.

A comprehensive vehicular network analysis requires modeling the street system and vehicle locations. Even when Poisson point processes (PPPs) are used to model the vehicle locations on each street, the analysis is barely tractable. That holds for even a simple average-based performance metric—the success probability, which is a special case of the fine-grained metric, the meta distribution (MD) of the signal-to-interference ratio (SIR). To address this issue, we propose the transdimensional approach as an alternative. Here, the union of 1D PPPs on the streets is simplified to the transdimensional PPP (TPPP), a superposition of 1D and 2D PPPs. The TPPP includes the 1D PPPs on the streets passing through the receiving vehicle and models the remaining vehicles as a 2D PPP ignoring their street geometry. Through the SIR MD analysis, we show that the TPPP provides good approximations to the more cumbrous models with streets characterized by Poisson line/stick processes; and we prove that the accuracy of the TPPP further improves under shadowing. Lastly, we use the MD results to control network congestion by adjusting the transmit rate while maintaining a target fraction of reliable links. A key insight is that the success probability is an inadequate measure of congestion as it does not capture the reliabilities of the individual links.

In this paper we investigate the problem of a vehicle merging to a main road while another vehicle is approaching on that road. We utilize conflict analysis to help the decision making and control for vehicles of different automation levels. We demonstrate that using vehicle-to-everything (V2X) communication, e.g., basic safety message (BSM), we are able to prevent conflict between the two vehicles. We design a longitudinal controller for the merging vehicle and show that V2X communication is also beneficial in improving the time efficiency of the merge. The results are demonstrated by performing simulations based on real highway data.

Continuous and reliable flow of navigation information is a pressing need for safer and smarter transportation systems. Vehicles with various levels of autonomy are now penetrating the markets. However, legacy vehicles (LVs) constitute a large portion of vehicles still running to this date. Hence, schemes for leveraging the coexistence of all vehicle types are required. This work represents an attempt towards achieving this goal. The paper proposes a distributed framework to leverage the computational capacity and advanced sensing capabilities of sensor-rich vehicles (SRVs) to enhance the localization accuracy of all participating vehicles. Neighboring SRVs and LVs form an ad-hoc network to exchange information through V2V communication, and SRVs distributively solve a consensus localization optimization problem. Thus, the system eliminates the need for a centralized entity that aggregates the data from all participating vehicles and solves the localization problem. The numerical results show that the proposed system achieves the same performance levels of the centralized methods. It is also shown that all participating vehicles including SRVs and LVs can benefit from this cooperation. For example, localization errors are reduced by at least 62.8% and 11.9% for LVs and SRVs in one scenario, respectively.

We address the user association problem in multi-tier in-band full-duplex (FD) networks. Specifically, we consider the case of decoupled user association (DUA), in which users (UEs) are not necessarily served by the same base station (BS) for uplink (UL) and downlink (DL) transmissions. Instead, UEs can simultaneously associate to different BSs based on two independent weighted path-loss user association criteria for UL and DL. We use stochastic geometry to develop a comprehensive modeling framework for the proposed system model, where BSs and UEs are spatially distributed according to independent point processes. We derive closed-form expressions for the mean rate utility in FD, half-duplex (HD) DL, and HD UL networks as well as the mean rate utility of legacy nodes with only HD capabilities in a multi-tier FD network. We formulate and solve an optimization problem that aims at maximizing the mean rate utility of the FD network by optimizing the DL and UL user association criteria. We investigate the effects of different network parameters, including the spatial density of BSs and power control parameter. We also investigate the effect of imperfect self-interference cancellation (SIC) and show that it is more severe at UL, where there exist minimum required SIC capabilities for BSs and UEs, for which FD networks are preferable to HD networks; otherwise, HD networks are preferable. In addition, we discuss several special cases and provide guidelines on the possible extensions of the proposed framework. We conclude that DUA outperforms coupled user association, in which UEs associate to the same BS for both UL and DL transmissions.

Recent advancements in self-interference (SI) cancellation capability of low-power wireless devices motivate in-band full-duplex (FD) wireless backhauling in small cell networks (SCNs). In-band FD wireless backhauling concurrently allows the use of the same frequency spectrum for the backhaul as well as access links of the small cells. In this paper, using tools from stochastic geometry, we develop a framework to model the downlink rate coverage probability of a user in a given SCN with massive multiple-input-multiple-output (MIMO)-enabled wireless backhauls. The considered SCN is composed of a mixture of small cells that are configured in either in-band or out-of-band backhaul modes with a certain probability. The performance of the user in the considered hierarchical network is limited by several sources of interference, such as the backhaul interference, small cell base station (SBS)-to-SBS interference, and the SI. Moreover, due to the channel hardening effect in massive MIMO, the backhaul links only experience long term channel effects, whereas the access links experience both the long term and the short term channel effects. Consequently, the developed framework is flexible to characterize different sources of interference while capturing the heterogeneity of the access and backhaul channels. In specific scenarios, the framework enables deriving closed-form coverage probability expressions. Under perfect backhaul coverage, the simplified expressions are utilized to optimize the proportion of in-band and out-of-band small cells in the SCN in the closed form. Finally, a few remedial solutions are proposed that can potentially mitigate the backhaul interference and in turn improve the performance of in-band FD wireless backhauling. Numerical results investigate the scenarios in which in-band wireless backhauling is useful and demonstrate that maintaining a correct proportion of in-band and out-of-band FD small cells is crucial in wireless backhauled SCNs.

Recent advancements in the self-interference (SI) cancellation capability of low power wireless devices pave the way of implementing full-duplex (FD) self-backhauling in small-cell networks. FD self-backhauling allows the use of conventional radio access network (RAN) spectrum for backhaul as well as access links concurrently. In this paper, we model and analyze massive MIMO- enabled wireless backhaul networks that are composed of a mixture of small cells, configured either in in-band or out-of-band backhaul mode with a certain probability. We consider a hierarchical network structure to model these networks and characterize the downlink coverage probability of a small cell base station (SBS) for both the in-band and out-of-band backhaul modes. The impact of co-tier and cross-tier backhaul interferences on downlink signal-to-interference ratio (SIR) coverage of small cell users is investigated. Numerical results demonstrate that implementing only either the in-band or out-of-band backhauling solutions may not be useful. Instead, a hybrid system with correct proportion of in-band and out-of-band small cells should be implemented.

We use stochastic geometry to develop a comprehensive modeling framework for K-tier uplink cellular networks with RF energy harvesting from the concurrent cellular transmissions. In the considered system model, channel inversion power control is used and cellular users are equipped with energy storage units. We also use tools from queueing theory, namely, Markov chain analysis, to model the level of stored energy in each user's battery. A successful transmission is assumed only when the amount of energy stored in a user's battery is sufficient to perform channel inversion with a received signal-to-interference ratio (SIR) above a predefined threshold. The performance of the proposed system model is evaluated in terms of the transmission probability, the (SIR) coverage probability, and the overall success probability. Using Poisson point processes (PPPs) enables us to derive simple expressions for these performance metrics in order to obtain insights for network design and optimization. We show the effect of varying the different network parameters such as the spatial density of BSs and the receiver sensitivity. In addition, we discuss several special cases and provide guidelines on the extensions of the proposed framework. We show that the gain of using RF energy harvesting can be highly improved by a proper choice of the network design parameters.

While cognitive radio enables spectrum-efficient wireless communication, radio frequency (RF) energy harvesting from ambient interference is an enabler for energy-efficient wireless communication. In this paper, we model and analyze cognitive and energy harvesting-based device-to-device (D2D) communication in cellular networks. The cognitive D2D transmitters harvest energy from ambient interference and use one of the channels allocated to cellular users (in uplink or downlink), which is referred to as the D2D channel, to communicate with the corresponding receivers. We investigate two spectrum access policies for cellular communication in the uplink or downlink, namely, random spectrum access (RSA) policy and prioritized spectrum access (PSA) policy. In RSA, any of the available channels including the channel used by the D2D transmitters can be selected randomly for cellular communication, while in PSA the D2D channel is used only when all of the other channels are occupied. A D2D transmitter can communicate successfully with its receiver only when it harvests enough energy to perform channel inversion toward the receiver, the D2D channel is free, and the signal-to-interference-plus-noise ratio (SINR) at the receiver is above the required threshold; otherwise, an outage occurs for the D2D communication. We use tools from stochastic geometry to evaluate the performance of the proposed communication system model with general path-loss exponent in terms of outage probability for D2D and cellular users. We show that energy harvesting can be a reliable alternative to power cognitive D2D transmitters while achieving acceptable performance. Under the same SINR outage requirements as for the non-cognitive case, cognitive channel access improves the outage probability for D2D users for both the spectrum access policies. When compared with the RSA policy, the PSA policy provides a better performance to the D2D users. Also, using an uplink channel provides improved performance to the D2D users in dense networks when compared to a downlink channel. For cellular users, the PSA policy provides almost the same outage performance as the RSA policy.

We model and analyze a K-tier uplink cellular network with flexible cell association where all transmissions are powered by energy harvesting from ambient interference. Each cellular user transmits data to the corresponding base station (BS) only when the amount of energy harvested is sufficient to perform channel inversion towards the serving BS. Furthermore, the data transmitted can be successfully decoded only when the signal-to-interference-plus-noise ratio (SINR) at the receiver is above a predefined threshold. With flexible cell association, users are not necessarily associated with their nearest BS where a different bias factor is added to each network tier. We use tools from stochastic geometry to evaluate the performance of the proposed system model in terms of the coverage probability of a generic user associated with the k-th tier. We show that energy harvesting can be a reliable source to power cellular users with short-range communication, e.g., small cell users. In addition, we show that energy harvesting can achieve high coverage performance by optimizing different network parameters such as the BS receiver sensitivity as well as the bias factors.

We propose a novel Location-Aware multicell Cooperation (LAC) scheme for downlink transmission in OFDMA-based networks. Compared to the traditional multicell cooperation, the proposed scheme uses coordinated multipoint (CoMP) transmission to serve only users with poor signal-to-interference-plus-noise ratio (SINR). On the other hand, users with good SINR conditions are served via multiuser MIMO by a single base station (BS). The proposed scheme uses a joint zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) transmission along with optimized power allocation in a semi-distributed manner to maximize the overall system energy efficiency (i.e., the average data rate per unit power [bps/Watt], or equivalently, average number of successfully transmitted bits per energy unit [bit/Joule]). Numerical results show that the proposed scheme outperforms the scheme that uses cooperation to serve all users, in terms of energy efficiency as well as system capacity and fairness.

We propose an energy-efficient resource allocation scheme for downlink transmission in two-tier Network MIMO OFDMA-based macrocell-femtocell networks where the femto-cells form clusters of equal size. The proposed scheme uses a joint zero-forcing beamforming with semi-orthogonal user selection (ZFBF-SUS) transmission at each network tier to perform allocation of subcarrier and precoding coefficients. Then, power allocation is optimized in order to maximize the total system energy efficiency (i.e., average number of successfully transmitted bits per energy unit [bit/Joule], or equivalently, the average data rate per unit power [bps/Watt]). The macro base stations (MBSs) and the femto base stations (FBSs) in a cluster maximize their energy efficiency in a distributed manner while considering the cross-tier interference and the capacity limitations of backhaul links. The problem of maximizing energy efficiency is formulated as a fractional program and solved by using the Dinkelbach iterative algorithm. Numerical results show that the proposed scheme outperforms the scheme that maximizes the system average capacity, in terms of energy efficiency, and also improves the total system performance in terms of energy efficiency and average system capacity when compared to a single-tier system.

Multi-tier cellular networks are considered as an effective solution to enhance the coverage and data rate offered by cellular systems. In a multi-tier network, high power base stations (BSs) such as macro BSs are overlaid by lower power small cells such as femtocells and/or picocells. However, co-channel deployment of multiple tiers of BSs gives rise to the problem of cross-tier interference that significantly impacts the performance of wireless networks. Multicell cooperation techniques, such as coordinated multipoint (CoMP) transmission, have been proposed as a promising solution to mitigate the impact of the cross-tier interference in multi-tier networks. In this paper, we propose a novel scheme for Location-Aware Cross-Tier Cooperation (LA-CTC) between BSs in different tiers for downlink CoMP transmission in two-tier cellular networks. On one hand, the proposed scheme only uses CoMP transmission to enhance the performance of the users who suffer from high cross-tier interference due to the co-channel deployment of small cells such as picocells. On the other hand, users with good signal-to-interference-plus-noise ratio (SINR) conditions are served directly by a single BS from any of the two tiers. Thus, the data exchange between the cooperating BSs over the backhaul network can be reduced when compared to the traditional CoMP transmission scheme. We use tools from stochastic geometry to quantify the performance gains obtained by using the proposed scheme in terms of outage probability, achievable data rate, and load per BS. We compare the performance of the proposed scheme with that of other schemes in the literature such as the schemes which use cooperation to serve all users and schemes that use range expansion to offload users to the small cell tier.