Multiuser multicarrier communication, e.g. orthogonal frequency division multi-access (OFDMA), has been extensively investigated since the 4G era and applied in several mainstream mobile networking standards, because it holds the potential of high-throughput provision, low complexity, and flexible bandwidth allocation. In the upcoming 6G era, mobile networks are newly expected to provide deadline or hard-delay assurance for latency-sensitive traffics generated from factory automation, smart grids, telesurgery, and automatic driving, etc. However, whether the emerging hard-delay constraint can be effectively satisfied in multiuser multicarrier systems, where subcarriers are shared by users, remains open. As a result, a unified framework for realtime multiuser multicarrier communications is presented in this paper, based on the bipartite-graph model of OFDMA. In particular, we conceive a$\mathcal {H}$-matching empowered joint subcarrier allocation and power adaptation strategy, which is shown to meet the deadline requirements deterministically over frequency-selective channels with finite average transmission power. Furthermore, we leverage the theory of random bipartite graph matching to analyze the delay-constrained capacity as a function of the average transmission power, based on the approximate outage probability of the embedded matching diversity. To gain more insights, asymptotic analysis is adopted to obtain the deadline-constrained throughput when the number of independent subcarriers is huge.

Emergency Demand Response Programs (EDRP) have garnered significant attention for its potential to enhance the safety and reliability of smart grids. However, its effectiveness is often compromised by latency in detecting line outages due to noise in observed sources, limited sensor sampling rates, and constrained communication bandwidth. While advancements in quickest line outage detection have been made, the demand reduction actions initiated only after a detected change point may be too late as the EDRP is also time consuming, thereby potentially resulting in severe failures in smart grids. To address these challenges and attain high-assurance power systems, we present a joint framework that integrates quickest line outage detection with emergency demand response, allowing for simultaneous or parallel change point detection and demand reduction. In particular, a Constrained Markov Decision Process (CMDP) is formulated to maximize the average expected reward that characterizes both the revenue and risk of the smart grid, considering operational constraints related to its demand response capabilities. Both theoretical and numerical results demonstrate that our proposed integrated detection and control architecture outperforms the conventional layered successive implementations, achieving superior profit maximization and risk mitigation.

Massive access has attracted considerable recent attention due to its potential in 6G’s usage scenarios including massive communications and ubiquitous connectivity. To assure its quality-of-service (QoS), both channel and queue state information have to be exploited to efficiently schedule multiple devices to attain low latency or strike the optimal delay-power tradeoff. However, such a cross-layer scheduling will suffer from the high complexity when the number of users grows huge, thereby causing the curse of dimensionality. In this paper, we present joint channel- and queue-aware scheduling that minimizes the queueing delay of both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) in low complexity, by leveraging a mean-field approach originating from statistical mechanics. In particular, we conceive a unified mean-field approximation framework that substantially simplifies both the objective function and constraints of the constrained Markov decision process (CMDP) for optimizing the queueing delay in cross-layer scheduling of multiple users. The mean-field approximation allows us to find an asymptoticly optimal solution to the original CMDP or stochastic programming with very high dimension by solving a variational optimization with much lower dimension instead, thereby substantially reducing the computational complexity. Numerical results demonstrate the effectiveness of our mean-field optimization approach for massive access.

The emerging deterministic networking (DetNet) has stimulated an increasing enthusiasm for the investigation of supporting deterministic, ultra-reliable, and low-latency services. However, the time-varying channel characteristics and bursty data traffic bring uncertainties for transmission, thereby making the assurance of deterministic quality-of-service (QoS) a challenging issue in practice. In this paper, we are interested in supporting the deterministic QoS demand in a massive access scenario by reducing the bit dropping rate incurred by delay violation. To minimize the bit dropping rate, a cross-layer scheduling scheme with joint channel and buffer awareness is highly desired to efficiently adjust the resource allocation among users, whose complexity increases exponentially with the number of users. Fortunately, the complexity issue can be relieved by adopting the mean-field approximation approach, which can substantially simplify the design and analysis of the cross-layer scheduling scheme with massive users. Two threshold-based scheduling policies are proposed, which have low computational complexity. Besides, we also derive the deadline-constrained capacity for massive access, which is substantially superior to that of single user transmissions. Numerical results will demonstrate the effectiveness of the mean-field approximation based cross-layer scheduling scheme.

The emerging real-time wireless communication systems are expected to provide deadline assurance for delay-sensitive traffics generated from factory automation, smart grids, and automatic driving, etc. Although there has been cutting edge research on diversity-enabled deadline assurance, real-time communications may suffer from poor energy efficiency. In this paper, we present a paradigm-shift real-time wireless communication method based on matching diversity that is judiciously designed for orthogonal frequency division multi-access (OFDMA) systems. In particular, we adopt bipartite graph to formulate a unified framework for OFDMA, based on which joint subcarrier matching and power adaptation policies are conceived to provide real-time transmissions, also referred to as just-in-time services (JITS). By bridging the average power and the formula of the outage probability, we show that the deadline constraints can be satisfied with a finite average power, when the number of users in the OFDMA system is not greater than that of subcarriers with independent channel gains. Furthermore, we also derive the approximate but yet analytical results on the required average power consumption. Numerical results indicate that a substantial energy efficiency gain can be attained in real-time OFDMA systems if the subcarrier interleaving based frequency diversity is replaced by matching diversity.

Mobile cloud computing (MCC) is expected to play a vital role due to its capability of scaling the computational resources of mobile devices by offloading tasks to cloud data centers. However, how to jointly optimize the computation offloading in wireless networks and demand response (DR) in smart grids remains open. In this paper, we present a paradigm-shift architecture that operates data centers as virtual power plants, while assuring the Quality-of-Service (QoS) for mobile application. In particular, less tasks are offloaded to a data center when the power gird is heavily loaded, thereby being capable of leveraging batteries of massive mobile devices to compensate the instantaneous power shortage. We formulate a game-theoretic framework for DR-aware dynamic computation offloading that not only guarantees the device lifetime, but also avoids large latency. Mean-field approximation is also applied to tackle the computational complexity when the data center has to schedule a huge number of devices in a distributed manner. Simulation results demonstrate the potential of operating data centers as virtual power plants, where appropriate demand response, balanced device lifetime, and low service latency can be attained simultaneously.

The emerging next generation Ultra-Reliable and Low-Latency Communications (xURLLC) is expected to play a central role in supporting mission-critical mobile applications because it holds the promise of improving the Quality-of-Service (QoS) substantially. However, it is quite challenging to satisfy the hard delay constraint in harsh wireless environments due to sporadic deep fades, especially when the average power is strictly limited. In this paper, we aim at assuring hard delay constraints with the aid of frequency or spatial diversity techniques. To this end, we focus on both parallel and multiple-input-multiple-output (MIMO) fading channels, in which time domain power adaptation is exploited to provide just-in-time services (JITS). It is shown that the hard delay constraint can be satisfied with a finite average power when the frequency or spatial diversity gains are no less than two. By adopting the implicit function theorem, we reveal the relationship between the required average power, the delay constrained throughput, and the outage probability without power adaptation. Furthermore, by adopting Ferrari’s solution to fourth order algebraic equations, we show that hard delay constrained transmission is feasible even when the sub-channels in the frequency and spatial domains are highly but not fully correlated.

The emerging deterministic networking (DetNet) has attracted considerable recent attention due to its potential in supporting services with ultra-low latency, low delay variation, and extremely low loss. In the development of the DetN et, hard delay constraints and extremely low loss are highly desired to be guaranteed. However, the bursty data traffic demands and random channel qualities bring uncertainties for transmission, incurring bit dropping due to the possible delay violation. How to support reliable transmission services by reducing bit dropping rates becomes a critical issue in the envisioned sixth-generation (6G) network. To efficiently reduce the bit dropping rate, a joint channel and buffer aware scheduling is highly desired, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean-field approximation to simplify the design and analysis of the joint channel and buffer aware scheduling under a huge number of users. A threshold-based scheduling policy is proposed, which has low computational complexity. Numerical results will demonstrate the effectiveness of the mean-field approximation based scheduling.

Hard delay constrained communications have attracted considerable recent attention because of their potential applications in the emerging field of deterministic networking (DetNet). However, developing techniques to satisfy both hard delay constraints and average power constraints simultaneously has long been a challenge. In this paper, we consider hard delay constrained transmissions over frequency selective wireless channels or parallel fading channels, in which the instantaneous transmission power can be adapted. A time domain power allocation scheme, also referred to as the generalized channel inversion policy is proposed. We find that the hard delay constraint can be met when the number of parallel channels with independent channel gains is greater than or equal to two. Furthermore, given a target rate, the required average power can be obtained based on the explicit outage probability as a function of the instantaneous signal-to-noise ratio (SNR). To provide further insight, we present two approximate formulas of the average power based on our derived closed-form approximations for the outage probability in the high SNR regime, and also derive upper and lower bounds on the required average power.

The next generation massive access has attracted considerable recent attention due to its potential in smart meters, industrial internet of things (IIoT), and smart traffics, etc. However, how to achieve the minimum queuing delay in massive access still remains open, thereby making quality-of-service (QoS) assurance a challenging issue in practice. In this paper, we aim at minimizing the average queuing delay by applying cross-layer scheduling with joint channel and buffer awareness, the complexity of which increases exponentially with the number of users. Fortunately, with massive users or devices, mean-field approximation can be adopted to substantially simplify the design and analysis of the delay-optimal scheduling. More specifically, we present a cocktail filling policy and a queue-aware multiuser diversity protocol, in which all backlogged packets of a user will be served by either NOMA or TDMA mode respectively, if the user’s channel gain is beyond a certain threshold. The average queuing delay and queue-length-violation probability are derived based on a Markov model. Numerical results will also demonstrate that the mean-field approximation based joint physical and network layer scheduling is capable of improving the QoS in massive access.

Proactive pushing has been recognized as a promising solution to support the dramatically increasing global data traffic, thereby gaining much attention recently. By proactively pushing popular files to users prior to their requests, the data traffic load over peak hours can be relieved, leading to substantially reduced content access latency. However, extra power consumption is incurred due to pushing. How to efficiently schedule proactive pushing and on-demand transmission becomes an important problem. In this paper, we present a unified framework for joint proactive pushing and on-demand transmission. Based on the joint proactive pushing and on-demand transmission scheme, we minimize the average content queueing latency through a Constrained Markov Decision Process (CMDP) approach while satisfying a power constraint. Through linear programming (LP) formulation, we obtain the optimal delay-power tradeoff of the traditional on-demand transmission only scheme and the joint pushing and on-demand transmission scheme, respectively. Finally, simulation results present that joint scheduling of pushing and on-demand transmission significantly reduces the content queueing delay.

The emerging Industrial Internet of Things (IIoT) is driving an ever increasing demand for providing low latency services to massive devices over wireless channels. As a result, how to assure the quality-of-service (QoS) for a large amount of mobile users is becoming a challenging issue in the envisioned sixth-generation (6G) network. In such networks, the delay-optimal wireless access will require a joint channel and queue aware scheduling, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean field approximation to conceive a buffer-aware multi-user diversity or opportunistic access protocol, which serves all backlogged packets of a user if its channel gain is beyond a threshold. A theoretical analysis and numerical results will demonstrate that not only the cross-layer scheduling policy is of low complexity but is also asymptotically optimal for a huge number of devices.

Proactive caching has emerged as a promising solution to reduce the content access latency in radio access networks (RANs), thereby attracting considerable attention in the era of 6G research. It allows base stations to push popular content items to mobile users’ devices proactively. Therefore, a cached-enabled RAN may serve a user by either on-demand transmission or proactive content placement, which share a common radio spectrum. How to efficiently schedule proactive caching and on-demand transmission then becomes a challenging issue that remains open. In this paper, we present a unified framework for joint scheduling of caching and on-demand transmission. In particular, we formulate a Markovian queueing model to analyze the average delay and power consumption of the proposed scheduling policy, which are then jointly minimized via linear programming (LP). Furthermore, a low-complexity heuristic scheduling policy is conceived to strike a sub-optimal tradeoff between delay and power based on greedy algorithms. Simulation results shall demonstrate that the overall service latency of a RAN can be substantially reduced by judiciously designing joint scheduling of caching and on-demand transmission.

Proactive caching has emerged as a promising means to accommodate increased demands for wireless capacity. However, studies of proactive caching usually focus on minimizing the overall load of cache-aided networks. How to calculate each user's caching gain is still an open problem. In this paper, a two-phase cache-aided network is investigated, in which users with heterogeneous preferences are served by a base station through a shared link. Effective throughput is considered as a performance metric, which describes the reduction in each user's transmission cost. All possible values of effective throughputs achieved by legitimate caching policies form an achievable domain. It is proved that the achievable domain is a convex set and can be characterized by its boundary. A special type of caching policies, termed uncoded placement absolutely-fair (UPAF) caching, is studied. For the two-user case, games are formulated to allocate effective throughput gains for the two users. For the general multiuser case, a UPAF policy is proposed to organize user cooperation. It is shown that users with more concentrated preferences can obtain higher effective throughputs.

Caching holds the promise of scaling the service capability of next-generation radio access networks (RANs), thereby attracting much recent attention in the era of 6G research. To enable caching, popular content items should be proactively pushed to the user ends (UEs) in the placement phase. In practice, most content placement is only allowed to exploit idle spectrum or timeslot that is not occupied by any on-demand transmissions. In this case, however, the performance analysis and optimizations of practical caching gains remain open. In this paper, we are interested in how pushing, as a secondary service, improves the overall performance in terms of energy efficiency and latency reduction. To this end, we formulate a Markovian queueing model, the caching gains of which are optimized via linear programming (LP) with acceptable computational complexity. Our work demonstrates that the peak traffic load due to burst on-demand transmissions, as primary services, can be effectively offloaded by pushing over idle timeslots, leading to substantially reduced power consumption and queueing delay.

Proactively pushing popular content files to users has been recognized as a promising technology to exploit the spectrum underutilized during the off-peak times, thereby attracting much attention recently. However, in a push-based system with the on-demand traffic, the appropriate pushing scheme and the achieved performance gains are unclear yet. In this paper, we aim to investigate the problem of joint scheduling of proactive pushing and on-demand transmission over shared spectrum. In particular, during the idle period of on-demand transmission, a probabilistic pushing scheme is presented based on the content request delay information (RDI). With pushing, the traffic load of on-demand transmission can be offloaded by extending transmission time, leading to the reduced power consumption. By establishing a Markov chain model, the energy efficiency gain of the pushing scheme proposed is derived. Based on this, we formulate an optimization problem to achieve maximum performance gains. The optimization problem is further converted into an equivalent linear programming (LP) problem through variable substitution. Finally, simulation results demonstrate the performance gains that can be achieved by proactively pushing.