Synchronized and fresh communication of common information is vitally important in numerous multi-user network scenarios, whereby end-users must perform coordinated real-time action with the available information. However, developing efficient policies with performance guarantees is greatly complicated by the abruptly changing nature of related age and synchronization metrics. In particular, powerful approaches that are based on so-called drift-plus-penalty (DPP) methods could not be employed due to the non-traditional multiplicative update dynamics of age and synchronization. In this paper, we overcome these limitations by designing and analyzing a Lyapunov-drift-based algorithm under the non-traditional age and synchronization dynamics that is not only low-complexity and analyzable, but also performs better than all the prior designs in numerical investigations. By comparing our design with two alternatives using the DPP approach, we also shed some light on the key aspect of our design that enables the performance analysis, which may be useful in future studies in multiplicative update dynamics. Furthermore, we implement Feature-based Reinforcement Learning (RL) methods with reduced state spaces and RL with full-state observations. Fresh-Async performs very closely to feature-based RL method using a feature space consisting of average age, age asynchrony, and maximum age, and both algorithms exhibit competitive performance compared to full-state RL while maintaining superior computational efficiency. These investigations clarify the contrast between drift-based and RL-based designs, and also reveal how drift-based design can be beneficial for feature selection for RL operation.

We study the optimal scheduling problem where n source nodes attempt to transmit updates over L shared wireless on/off fading channels to optimize their age performance under energy and age-violation tolerance constraints. Specifically, we provide a generic formulation of age-optimization in the form of a constrained Markov Decision Process (CMDP), and obtain the optimal scheduler as the solution of an associated Linear Programming problem. We investigate the characteristics of the optimal single-user multi-channel scheduler under different age-related objectives where a usual threshold-based policy does not apply. We then investigate the stability region of the optimal scheduler for the multi-user case under age-violation tolerance constraints. Furthermore, we develop two online schedulers that do not require statistics and are amenable to scalable operation: Drift-plus-penalty-based design, and a novel variation of the well-known Q-learning-based reinforcement learning method that combines Q-learning with drift-minimization-methods successfully for the first time, to the best of our knowledge. Our numerical studies compare the performance of our online schedulers to the optimal scheduler to reveal that both algorithms capture the essential behavior of the optimal design under different scenarios with good scalability, with the Q-learning-based design providing even closer performance to the optimal one by utilizing the history of the drift in a novel way.

We consider a scenario whereby the state of a common source is being updated at multiple distributed devices. We are particularly interested in the tradeoff that exists between thefreshnessof the updates at the distributed devices and thesynchronyof the updates across them. In this paper, we explore this tradeoff in a wireless downlink setting whereby the transmitter can choose between unicast transmissions (with given success probabilities) to particular users and broadcast transmissions (with a smaller success probability) to all users. After discussing the Linear Programming (LP)-based optimal design and extreme choices of “always-unicasting” and “always-broadcasting” policies, we note that the optimal design is not scalable and the extreme policies are inefficient. This motivates us to develop two classes of policies, namely a “mixed randomized policy” and a “feature-based learning policy”, which have desirable performance and computational-complexity characteristics. Additionally we manage to provide complete analysis for the mixed randomized policy under the two-user case, which provides interesting insights and can be partially extended to general cases. We perform extensive numerical studies to compare the performance of these designs over the benchmarks to reveal their gains.

We consider a scenario whereby the state of a common source is being updated at multiple distributed devices. We are particularly interested in the tradeoff that exists between the freshness of the updates at the distributed devices and the synchrony of the updates across them. In this paper, we explore this tradeoff in a wireless downlink setting whereby the transmit-ter can choose between unicast transmissions (with given success probabilities) to particular users and broadcast transmissions (with a smaller success probability) to all users. After discussing the Linear Programming (LP)-based optimal design and extreme choices of “always-unicasting” and “always-broadcasting” poli-cies, we note that the optimal design is not scalable and the extreme policies are inefficient. This motivates us to develop two classes of policies, namely a “mixed randomized policy” and a “feature-based learning policy”, which have desirable performance and computational-complexity characteristics. We perform extensive numerical studies to compare the performance of these designs over the benchmarks to reveal their gains.

Future generation wireless technologies are expected to serve an increasingly dense and dynamic population of users that generate short bundles of information to be transferred over the shared spectrum. This calls for new distributed and low-overhead Multiple-Access-Control (MAC) strategies to serve such dynamic demands with spectral efficiency characteristics. In this work, we address this need by identifying and developing two fundamentally different MAC paradigms: (i) congestion-based paradigm that estimates the congestion level in the system and adapts to it; and (ii) age-based paradigm that prioritizes demands based on their ages. Despite their apparent differences, we develop policies under each paradigm in a generic multi-channel access scenario that are provably throughput-optimal when they employ any asymptotically-efficient channel encoding/decoding mechanism. We also characterize the stability regions of the two designs, and investigate the conditions under which one design outperforms the other. We perform extensive simulations to validate the theoretical claims and investigate the non-asymptotic performances of our designs.

With the adoption of 5G wireless technology and the Internet-of-Things (IoT) networking, there is a growing interest in serving a dense population of low-complexity devices over shared wireless uplink channels. Different from the traditional scenario of persistent users, in these new networks each user is expected to generate only small bundles of information intermittently. The highly dynamic nature of such demand and the typically low-complexity nature of the user devices calls for a new MAC paradigm that is geared for low-overhead and distributed operation of dynamic users.In this work, we address this need by developing a generic MAC mechanism for estimating the number and coordinating the activation of dynamic users for efficient utilization of the time-frequency resources with minimal public feedback from the common receiver. We fully characterize the throughput and delay performance of our design under a basic threshold-based multi-channel capacity condition, which allows for the use of different channel utilization schemes. Moreover, we consider the Successive-Interference-Cancellation (SIC) Multi-Channel MAC scheme as a specific choice in order to demonstrate the performance of our design for a spectrally-efficient (albeit idealized) scheme. Under the SIC encoding/decoding scheme, we prove that our low-overhead distributed MAC can support maximum throughput, which establishes the efficiency of our design. Under SIC, we also demonstrate how the basic threshold-based success model can be relaxed to be adapted to the performance of a non-ideal success model.

Social networking environments provide major platforms for the discussion and formation of opinions in diverse areas, including, but not limited to, political discourse, market trends, news, and social movements. Often, these opinions are of a competing nature, e.g., radical vs. peaceful ideologies, correct information vs. misinformation, and one technology vs. another. We study the battles of such competing opinions over evolving social networks. The novelty of our model is that it captures the exposure and adoption dynamics of opinions that account for the preferential and random nature of exposure as well as the persuasion power and persistence of different opinions. We provide a complete characterization of the mean opinion dynamics over time as a function of the initial adoption, as well as the particular exposure, adoption, and persistence dynamics. Our analysis, supported by case studies, reveals the key metrics that govern the spread of opinions and establishes the means to engineer the desired impact of an opinion in the presence of other competing opinions.

We introduce a new optimization framework, built over a discounted-rate metric, that captures the sensitivities of wireless users to time-variations in their fairness measure of rate allocations. The resulting, so-called, Discounted-Rate Utility Maximization (DRUM) formulation not only accommodates traditional long-term and less-explored instant fairness concepts in its extremes, but also encompasses all intermediate degrees of sensitivity to fluctuations in the users' rate allocations. After introducing the versatile DRUM formulation, we fully characterize its solution in the instantly-fair and long-term-fair extremes for the general class of ω-weighted α-fair utility functions. These solutions reveal the non-trivial impact of fading channel statistics and the utility function parameters on the rate allocations, even under perfectly symmetric network conditions. In particular, we demonstrate that the rate allocations lie between the maximum and the harmonic mean of the fading-channel rates. To achieve rates in-between these extremes, we also address the general solution of DRUM by proposing a novel low-complexity dynamic rate allocation algorithm that does not require the knowledge of the channel statistics. This algorithm is proven to achieve the optimal performance of the instantly-fair and long-term-fair solutions as the discount parameter approaches its lower and upper limits, respectively. We also study the fairness and rate allocation characteristics of our algorithm for intermediate values of the discount parameter in a Rayleigh-Fading environment.

In this paper, we focus on a mobile wireless network comprising a powerful communication center and a multitude of mobile users. We investigate the propagation of latency-constrained content in the wireless network characterized by heterogeneous (time-varying and user-dependent) wireless channel conditions, heterogeneous user mobility, and where communication could occur in a hybrid format (e.g., directly from the central controller or by exchange with other mobiles in a peer-to-peer manner). We show that exploiting double opportunities, i.e., both time-varying channel conditions and mobility, can result in substantial performance gains. We develop a class of double opportunistic multicast schedulers and prove their optimality in terms of both utility and fairness under heterogeneous channel conditions and user mobility. Extensive simulation results are provided to demonstrate that these algorithms can not only substantially boost the throughput of all users (e.g., by 50% to 150%), but also achieve different consideration of fairness among individual users and groups of users.

In this paper, we focus on mobile wireless networks comprising of a powerful communication center and a multitude of mobile users. We investigate the propagation of deadline-based content in the wireless network characterized by heterogeneous (time-varying and user-dependent) wireless channel conditions, heterogeneous user mobility, and where communication could occur in a hybrid format (e.g., directly from the central controller or by exchange with other mobiles in a peer-to-peer manner). We show that exploiting double opportunities, i.e., both time-varying channel conditions and mobility, can result in substantial performance gains. We develop a class of double opportunistic multicast schedulers and prove their optimality in terms of both utility and fairness under heterogeneous channel conditions and user mobility. Extensive simulation results are provided to demonstrate that these algorithms can not only substantially boost the throughput of all users (e.g., by 50% to 150%), but also achieve different consideration of fairness among individual users and groups of users.