Immersive communications is a key use case for 6G where applications require reliable latency-bound media traffic at a certain data rate to deliver an acceptable User Experience (UX) or Quality-of-Experience (QoE). The Quality-of-Service (QoS) framework of current cellular systems (4G and 5G) and prevalent network congestion control algorithms for latency-bound traffic like L4S typically target network-related Key Performance Indicators (KPIs) such as data rates and latencies. Network capacity is based on the number of users that attain these KPIs. However, the UX of an immersive application for a given data rate and latency is not the same across users, since it depends on other factors such as the complexity of the media being transmitted and the encoder format. This implies that guarantees on network KPIs do not necessarily translate to guarantees on the UX. In this paper, we propose a framework in which the communication network can provide guarantees on the UX. The framework requires application servers to share real-time information on UX dependency on data rate to the network, which in turn, uses this information to maximize a UX-based network utility function. Our framework is motivated by the recent industry trends of increasing application awareness at the network, and pushing application servers towards the edge, allowing for tighter coordination between the servers and the 6G system. Our simulation results show that the proposed framework substantially improves the UX capacity of the network, which is the number of users above a certain UX threshold, compared to conventional rate control algorithms.

As the automotive industry shifts towards enabling self-driving vehicles, real-time situational awareness is becoming a crucial requirement. This paper introduces a novel information-sharing mechanism to opportunistically improve the vehicles’ local environment estimates via infrastructure-assisted collaborative sensing, while still allowing them to operate autonomously when no assistance is available. As vehicles might have different sensing capabilities, combining and sharing information from a judiciously selected subset is often sufficient to considerably improve all the vehicles’ estimation errors. We develop an opportunistic framework for vehicular collaborative sensing determining (1) which nodes require assistance, (2) which ones are best suited to provide it, and (3) the corresponding information-sharing rates, so as to minimize the communication overheads while meeting the vehicles’ target estimation error. We leverage the supermodularity of the problem to devise an efficient vehicle information sharing algorithm with suboptimality guarantees to solve this problem and make it suitable to deploy in dynamic environments where network conditions might fluctuate rapidly. We support our analysis with simulations showing evidence that vehicles can considerably benefit from the proposed opportunistic collaborative sensing framework compared to operating autonomously. Finally, we explore the value of information-sharing in vehicular collaborative sensing networks by evaluating the associated safe driving velocity gains.

This paper presents a theoretical analysis for a self-driving vehicle’s velocity as it navigates through a random environment. We study a stylized environment and vehicle mobility model capturing the essential features of a self-driving vehicle’s behavior, and leverage results from stochastic geometry to characterize the distribution of a typical vehicle’s safe driving velocity, as a function of key network parameters such as the density of objects in the environment and sensing accuracy. We then consider a setting wherein the sensing accuracy is subject to a sensing/communication rate constraint. We propose a procedure that focuses the vehicle’s sensing/communication resources and estimation efforts on the objects that affect its velocity and safety the most so as to optimize its ability to drive faster in uncertain environments. Simulation results show that the proposed methodology achieves considerable gains in the vehicle’s safe driving velocity as compared to uniform rate allocation policies.

In this paper, we analyze the performance of Multiplayer Cloud Gaming (MCG) systems. To that end, we introduce a model and new MCG-Quality of Service (QoS) metric that captures the freshness of the players' updates and fairness in their gaming experience. We introduce an efficient measurement-based Joint Multiplayer Rate Adaptation (JMRA) algorithm that optimizes the MCG-QoS by overcoming large (possibly varying) network transport delays by increasing the associated players' update rates. The resulting MCG-QoS is shown to be Schur-concave in the network delays, leading to natural characterizations and performance comparisons associated with the players' spatial geometry and network congestion. In particular, joint rate adaptation enables service providers to combat variability in network delays and players' geographic spread to achieve high service coverage. This, in turn, allows us to explore the spatial density and capacity of compute resources that need to be provisioned. Finally, we leverage tools from majorization theory, to show how service placement decisions can be made to improve the robustness of the MCG-QoS to stochastic network delays.

Motivated by the potentially high downlink traffic demands of commuters in future autonomous vehicles, we study a network architecture where vehicles use Vehicle-to-Vehicle (V2V) links to form relay network clusters, which in turn use Vehicle-to-Infrastructure (V2I) links to connect to one or more Road Side Units (RSUs). Such cluster-based multihoming offers improved performance, e.g., in coverage and per user shared rate, but depends on the penetration of V2V+V2I capable vehicles and possible blockage, by legacy vehicles, of line of sight based V2V links, such as those based on millimeter-wave and visible light technologies. This paper provides a performance analysis of a typical vehicle's connectivity and throughput on a highway in the free-flow regime, exploring its dependence on vehicle density, sensitivity to blockages, number of lanes and heterogeneity across lanes. The results, backed up by simulations of realistic vehicular traffic, show that even with moderate vehicle densities and penetration of V2V+V2I capable vehicles, such architectures can achieve substantial improvements in connectivity and reduction in per-user rate variability as compared to V2I based networks. The typical vehicle's performance is also shown to improve considerably in the multilane highway setting as compared to a single lane road. This paper also sheds light on how the network performance is affected when vehicles can control their relative positions, by characterizing the connectivity-throughput tradeoff faced by the clusters of vehicles.

The automotive industry is undergoing disruptive changes, e.g., ride sharing and self-driving cars which, in addition to leveraging wireless connectivity, may lead to dramatic changes in the volume of infotainment and work related data consumption of vehicle bound passengers. This paper studies the potential gains of leveraging clusters of V2V interconnected vehicles to enable: (1) improved opportunistic access to the cellular infrastructure; and (2), balancing traffic loads across cells through cluster multihoming. A stochastic geometric model and associated analysis are used to obtain a preliminary understanding of possible gains of cluster-based opportunistic relaying and its sensitivity to the system parameters, e.g., base station density, vehicular cluster size and density etc. An optimal network utility maximization formulation is then developed to serve as a baseline to evaluate a simple distributed cluster management algorithm which for the scenarios considered proves to be near-optimal. Overall the results suggest that 3-10x throughput gains are possible along with significant improvements in user rate fairness depending on the system parameters.

This paper investigates the bit-error probability of several color shift keying (CSK) constellations in a visible light communications (VLC) scenario with a camera-based receiver. A novel VLC communication constellation set, the extended circular CSK, is presented and analyzed. Its performance is compared to the CSK constellation described in the IEEE 802.15.7 standard, in addition to other constellations that were introduced previously, namely the geometric and circular CSK constellations. The bit-error rate (BER) of the proposed extended circular CSK constellation is studied. Simulation results show that the extended circular CSK significantly outperforms the other constellations and deserves further research attention in the future.

This paper introduces circular color shift keying (CSK) constellations in a visible light communications (VLC) system using a camera-based receiver with applications to vehicular communications, where vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are required to improve the safety of vehicles in high traffic density. The circular CSK constellations are shown to outperform the geometric and IEEE 802.15.7 constellations in terms of bit-error probability. Afterward, the noise at the receiver is investigated and transferred from the spatial domain to an angular noise in the International Commission on Illumination (CIE) color domain, where it is shown to behave similarly to a Gaussian distribution. Based on this noise model, a closed-form expression of the bit-error rate (BER) of circular CSK constellations is derived and shown to be accurate for BER ranges corresponding to VLC applications of practical interest. Diversity techniques are also investigated, and interesting differences are noted during the analysis compared to traditional radio frequency (RF) communication scenarios. Finally, the design of a link adaptation approach maximizing the number of bits per transmitted symbol, while meeting a target BER, is proposed. Hence, this paper presents a detailed design and analysis of a novel VLC modulation technique and demonstrates its efficiency and superiority.

We present in this paper a visible light communication transceiver system design for Vehicle-to-Vehicle (V2V) communication over an outdoor wireless channel using a camera as a receiver. We propose a channel model relevant to the nature of the receiver, according to which different modulation schemes and constellation sets are designed and compared through Monte-Carlo simulations. We evaluate performance for various design alternatives and output metrics, and demonstrate that our proposed color shift keying circular constellation set outperforms schemes included in the IEEE 802.15.7 standard in terms of noise resiliency.

We present a scalable mobile multimedia streaming system with device-to-device cooperation that enables common content distribution in dense wireless networking environments. This is particularly applicable to use cases such as delivering real-time multimedia content to fans watching a soccer game in a stadium or to participants attending a major conference in a large auditorium. The key novel characteristics of our system include seamless neighbor discovery and link quality estimation, intelligent clustering and channel allocation algorithms based on constrained minimum spanning trees, robustness against device mobility, and device centric operation with no changes to existing wireless systems. We demonstrate the functionality of the proposed system on Android devices using heterogeneous networks (cellular/WiFi/WiFi-Direct) and show the formation of multiple clusters to allow for scalable operation. The gained insights will help bridge the gap between theoretical and simulation based research conducted in this area and practical operation taking into account the capabilities and limitations of existing wireless technologies and smartphones/tablets.