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.

High performance XR systems can offload intensive computation tasks such as graphic rendering to the edge server - enabling a richer experience while reducing the power consumption on the XR end-user device. This paper provides an overview of the requirements for such split-compute XR systems over 5G and the resulting traffic characteristics. The design of 5G system, from the very first release, provides support for such emerging use cases with low power requirement and traffic requirements of low latency, high bandwidth, and high reliability. As split XR systems scale in terms of the number of users, evolve to support more immersive experience with consumer-friendly device form-factors, and become a key enabler for metaverse experiences, 5G system will continue to evolve to meet the more demanding requirements as part of “5G-Advanced” phase of 5G standards development, starting with 3GPP Release 18. This paper provides an overview of the advances in 5G with 3GPP Release 18 across RAN and SA2 working groups to address the emerging XR requirements.

In this paper, we discuss UE power saving (PS) techniques for eXtended Reality (XR) applications in 5G NR system. XR traffic is characterized by pseudo periodic packet arrival, high bit rate, and low latency requirement. It is challenging to support such traffic in a small device form factor with limited battery size, e.g., head-mounted display (HMD) or XR glasses. Therefore, it is desired for 5G NR to support effective UE PS techniques. We first discuss a few PS techniques defined in 5G NR specifications Release 15 and 16. Then, we introduce new PS techniques that are evaluated via system level simulations with the UE power consumption model defined in a 3GPP. Evaluation results show that UE power consumption can be substantially reduced by the new PS techniques, up to 40%.

We present system-level evaluation results to characterize the performance of eXtended Reality (XR) applications over a 5G NR system. A split-rendering framework is assumed, where the XR device uses 5G to offload computation associated with rendering and encoding of video frames to an edge server. The XR traffic model includes rendered video frames on the downlink and user pose and control updates on the uplink. We present results for both sub-6 GHz (FR1) and millimeter-wave (FR2) bands. Additionally, we also discuss potential enhancements to further improve user experience for XR applications over 5G.

In this paper, we study the revenue sharing and rate allocation for Internet Service Providers (ISPs) that jointly provide network connectivity between content providers and end-users. Without colluding, each ISP may selfishly set a high transit-price to cover its cost and maximize its own profit, which inevitably results in a loss in social profit. We model this noncooperative interaction between an “eyeball” ISP and a “content” ISP as a Stackelberg game and quantify the resulting loss in social profit. To recover the profit loss, we propose a revenue sharing contract between ISPs by modeling them as a supply chain to deliver traffic in a two-sided market. Parameterized by the profit division factor, the sharing contract coordinates ISPs' objectives such that they aim to maximize the social profit self-incentively. We further propose a Nash bargaining process to determine the profit division factor such that all ISPs are simultaneously better off compared to the noncooperative equilibrium.

This paper investigates pricing of Internet connectivity services in the context of a monopoly ISP selling broadband access to consumers. We first study the optimal combination of flat-rate and usage-based access price components for maximization of ISP revenue, subject to a capacity constraint on the data-rate demand. Next, we consider time-varying consumer utilities for broadband data rates that can result in uneven demand for data-rate over time. Practical considerations limit the viability of altering prices over time to smoothen out the demanded data-rate. Despite such constraints on pricing, our analysis reveals that the ISP can retain the revenue by setting a low usage fee and dropping packets of consumer demanded data that exceed capacity. Regulatory attention on ISP congestion management discourages such ``technical" practices and promotes economics based approaches. We characterize the loss in ISP revenue from an economics based approach. Regulatory requirements further impose limitations on price discrimination across consumers, and we derive the revenue loss to the ISP from such restrictions. We then develop partial recovery of revenue loss through non-linear pricing that does not explicitly discriminate across consumers. While determination of the access price is ultimately based on additional considerations beyond the scope of this paper, the analysis here can serve as a benchmark to structure access price in broadband access networks.

Usage-based pricing has been recognized as a network congestion management tool. Internet Service Providers (ISPs), however, have limited ability to set time-adaptive usage-price to manage congestion arising from time-varying consumer utility for data. To achieve the maximum revenue, ISP can set its time-invariant usage-price low enough to aggressively encourage consumer's traffic demand. The downside is that ISP has to drop consumer's excessive traffic demand through congestion management (i.e., packet dropping), which may degrade Quality of Service (QoS) of consumer's traffic. Alternatively, to protect consumer's QoS, ISP can set its time-invariant usage-price high enough to reduce consumer's traffic demand, thus minimizing the need for congestion management through packet dropping. The downside is that ISP suffers a revenue loss due to the inefficient usage of its network. The tradeoff between ISP's revenue maximization and consumer's QoS protection motivates us to study ISP's revenue maximization subject to QoS constraint in terms of the number of packets dropped. We investigate two different QoS measures: short-term per-slot packet dropping constraint and long-term packet dropping constraint. The short-term constraint can be interpreted as a more transparent congestion management practice compared to the long-term constraint. We analyze ISP's optimal time-invariant pricing for both constraints, and develop an upper bound for the optimal revenue by considering the specified packet dropping threshold. We quantify the impact of consumer's price elasticity on ISP's optimal revenue and show that ISP should carry out a differentiated QoS protection strategy based on consumer's price elasticity in order to mitigate the revenue loss1.

Pricing content-providers for connectivity to end- users and setting connection parameters based on the price is an evolving model on the Internet. The implications are heavily debated in telecom policy circles, and some advocates of "Network Neutrality" have opposed price based differentiation in connectivity. However, pricing content providers can possibly subsidize the end-user's cost of connectivity, and the consequent increase in end-user demand can benefit ISPs and content providers. This paper provides a framework to quantify the precise trade-off in the distribution of benefits among ISPs, content-providers, and end-users. The framework generalizes the well-known utility maximization based rate allocation model, which has been extensively studied as an interplay between the ISP and the end-users, to incorporate pricing of content-providers. We derive the resulting equilibrium prices and data rates in two different ISP market conditions: competition and monopoly. Network neutrality based restriction on content-provider pricing is then modeled as a constraint on the maximum price that can be charged to content-providers. We demonstrate that, in addition to gains in total and end- user surplus, content-provider experiences a net surplus from participation in rate allocation under low cost of connectivity. The surplus gains are, however, limited under monopoly conditions in comparison to competition in the ISP market.

We consider a cellular network or a wireless local area network (WLAN), deployed with attachment points (APs) capable of transmission and reception in multiple radio- frequency (RF) carriers. A major factor contributing to the efficiency and stability of the network is the mechanism determining the connection of terminals to the appropriate AP and the RF carrier. This paper describes a practical, distributed, network-assisted and terminal-driven mechanism to determine the connections for load-balancing among the available RF carriers. The mechanism is based on a metric that we term as the service level indicating metric (SLIM). We extend the notion of SLIM to cases where Quality-of-Service (QoS) parameters are specified and propose a load-balancing mechanism for such cases. We demonstrate the near-optimality of the proposed mechanisms through 3GPP based simulations.

This paper considers the nonconvex and globally coupled problem of joint antenna beamforming and transmit power control, in order to maximize the network-wide utility as a function of attained SIRs. Using a spillage-load characterization for power control [13], we assign utility as a function of attained SIRs and formulate the joint optimization as a utility maximization problem. Despite the highly coupled structure of the problem, we propose an efficient distributed algorithm that is proved to be convergent in general. Despite nonconvexity in the joint optimization, we prove global optimality in the two user case. We find in simulations the algorithm always converges to the global optimal allocation, and the Pareto-optimal tradeoff between power and antenna beamforming in maximizing network utility is illustrated.

A common assumption behind most of the recent research on network utility maximization is that traffic flows are elastic, which implies that their utility functions are concave and there are no hard limits on the rate allocated to each flow. These critical assumptions lead to tractability of the analytic models of utility maximization, but also limits applicability of the resulting rate allocation protocols. This paper focuses on inelastic flows and removes these restrictive and often invalid assumptions. We present several optimization frameworks, optimality conditions, and optimal algorithms. First, we consider nonconcave utility functions, which turn utility maximization into nonconvex, constrained optimization problems that are well-known to be difficult. We present conditions under which the current standard price-based distributed algorithm can still converge to the globally optimal rate allocation despite nonconcavity of utility functions. In particular, continuity of price-based rate allocation at all the optimal prices is a sufficient condition for global convergence of rate allocation by the standard algorithm, and continuity at at least one optimal price is a necessary condition. In the second part of the paper, we provide a general problem formulation of rate allocation among time-sensitive flows from real-time and streaming applications, as well as a decomposition into subproblems coordinated by pricing. After simplifying the subproblems by leveraging the optimization structures, we highlight the difficult issues of causality and time-scale, and propose an effective price-based heuristics for admission control and an optimal algorithm for a special case formulation.

The estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm has proved to be a robust solution to exploit single invariance structure in array elements. However, ESPRIT is not flexible to incorporate multiple invariances. A closed form solution to the problem of estimating eigenvalues in multiple invariant structures is presented in this paper. It is shown that the algorithm is comparable in complexity to ESPRIT but performs better than ESPRIT, especially when only a limited number of signal samples are available. In addition, the algorithm is shown to be feasible even for single array elements sharing multiple invariance.

The problem of channel estimation for a frequency hopping system is considered. Under a discrete multipath fading model, the path gains and delays are estimated separately. By exploiting fixed (but unknown) patterns in the data packet, the delay estimation problem is formulated in an ESPRIT-like format by identifying multiple rotational invariance structures in the received data. Since ESPRIT is limited to exploitation of single invariance, the previously proposed SPECC algorithm is employed for the multipath delay estimation. Performance results based on simulations are presented.