This paper investigates the downlink spectral efficiency of low Earth orbit (LEO) satellite constellations, where spectral efficiency refers to the entire network's total data rate per unit spectrum per unit area on the Earth's surface. For practicality, all links employ single-user codebooks and treat interference as noise. A key finding is that, unlike terrestrial networks, the spectral efficiency of LEO constellations does not increase indefinitely with satellite density. Under typical assumptions about antenna array beam widths, this study explores the satellite density that maximizes spectral efficiency. As a special case, a regular deployment of satellites and ground terminals is analyzed across various densities. Simulation results reveal that regular configurations achieve higher spectral efficiency compared to random configurations. Furthermore, while the total downlink capacity of any LEO constellation remains significantly lower than that of terrestrial networks, there is substantial potential for growth-up to a few orders of magnitude-compared to current capacity levels.

We consider a setting where a band of spectrum is shared using time/frequency prioritized sharing, an approach in which one entity has priority access to a portion of the band for a portion of time, while other entities equally share the remaining time/frequency resources. We focus on a case where all of the entities sharing the spectrum are wireless service providers (SPs) that are competing for a common pool of customers. Most prior work on competition between wireless SPs has adopted either a Bertrand competition model, where SPs compete by announcing prices, or a Cournot competition model where SPs compete by announcing quantities. Here, we compare these two frameworks for competition with time/frequency prioritized sharing. We perform both theoretical and numerical analysis on how market parameters affect the two economic models.

Spectrum sharing plays an important role in meeting the demand for the 6 G and advanced wireless communications. This paper considers a framework of time-frequency prioritized spectrum sharing, in which a given band of spectrum is shared both in time and frequency with certain service providers (SPs) having priority access to given time-frequency partitions and other partitions available for use by all SPs. We investigate the competition between the two SPs in such a setting under both Bertrand and Cournot competition models, where SPs compete by either announcing prices or quantities, respectively. We calculate a market equilibrium for both competition models and analyze the impact of the available bandwidth and the spectrum sharing parameters on the revenues of the SPs, the customer surplus, and the social welfare.

While federated learning enables intelligent services and personalized user experiences, it raises privacy concerns due to regulatory requirements and user demands for data protection. Federated unlearning offers a potential solution to these issues. However, despite increasing demand for its practical implementation driven by right-to-be-forgotten regulations, the economic implications of federated unlearning on user behavior and platform profitability remain underexplored, potentially hindering its adoption. In this paper, we formulate a set of contract design problems for both unlearning-disabled and unlearning-enabled scenarios. Challenges arise when the unlearning-enabled platform jointly designs compensation for both learning and unlearning to incentivize users' sequential decisions to balance the expected revenue and unlearning cost. We first conduct a questionnaire survey that reveals that federated unlearning increases users' willingness to participate in federated learning. We then provide a necessary condition for maximizing the surplus of an unlearning-enabled platform, enabling the point-wise decomposition for the optimal contract design problem, based on which we minimize the incentive cost and maximize the surplus for the platform. Our further analysis reveals that i) the incentive effects of unlearning grow quadratically with users' privacy sensitivity, and ii) enabling unlearning may even profit more than disabling it when the training cost increases at a faster rate than the probability of privacy leakage as effort levels rise. Our numerical results show that the platform's profitability is primarily influenced by users' privacy sensitivity. When users have a relatively high privacy sensitivity, enabling unlearning can significantly improve profitability.

To protect users'right to be forgottenin federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrates on developing effective and efficient unlearning techniques. However, the issue of incentivizing valuable users to remain engaged and preventing their data from being unlearned is still under-explored, yet important to the unlearned model performance. This paper focuses on the incentive issue and develops an incentive mechanism for federated learning and unlearning. We first characterize the leaving users' impact on the global model accuracy and the required communication rounds for unlearning. Building on these results, we propose a four-stage game to capture the interaction and information updates during the learning and unlearning process. A key contribution is to summarize users' multi-dimensional private information into one-dimensional metrics to guide the incentive design. Interestingly, we prove that allowing federated unlearning can result in reduced payoffs for both the server and users, compared to a scenario without unlearning. Numerical results demonstrate the necessity of unlearning incentives for retaining valuable leaving users, and also show that our proposed mechanisms decrease the server's cost by up to 53.91% compared to state-of-the-art benchmarks.

Cellular vehicular-to-everything (C-V2X) systems offer the potential for improving road safety, in part through the exchange of periodic basic safety messages (BSMs) between nearby vehicles. The reliability and latency of these messages is a key metric. Hybrid automatic repeat request (HARQ) retransmissions are one technique used to this end. However, HARQ may come at the expense of consuming the limited available wireless resources, especially in highly congested scenarios. This paper studies BSM transmission latency and reliability when HARQ retransmissions are used with the semi-persistent scheduling (SPS) in C-V2X transmission mode 4. We do so through extensive system-level simulations that closely follow the SPS process. Furthermore, we provide an analytical model for the tail behavior of the BSM latency distribution with HARQ retransmissions that is a good approximation to the simulation results. Our study reveals the impact of several deployment settings (e.g., bandwidth configurations and vehicle density).

By allowing users to erase their data’s impact on federated learning models, federated unlearning protects users’ right to be forgotten and data privacy. Despite a burgeoning body of research on federated unlearning’s technical feasibility, there is a paucity of literature investigating the considerations behind users’ requests for data revocation. This paper proposes a non-cooperative game framework to study users’ data revocation strategies in federated unlearning. We prove the existence of a Nash equilibrium. However, users’ best response strategies are coupled via model performance and unlearning costs, which makes the equilibrium computation challenging. We obtain the Nash equilibrium by establishing its equivalence with a much simpler auxiliary optimization problem. We also summarize users’ multi-dimensional attributes into a single-dimensional metric and derive the closed-form characterization of an equilibrium, when users’ unlearning costs are negligible. Moreover, we compare the cases of allowing and forbidding partial data revocation in federated unlearning. Interestingly, the results reveal that allowing partial revocation does not necessarily increase users’ data contributions or payoffs due to the game structure. Additionally, we demonstrate that positive externalities may exist between users’ data revocation decisions when users incur unlearning costs, while this is not the case when their unlearning costs are negligible.

It is common in online markets for agents to learn from others' actions. Such observational learning can lead to herding or information cascades in which agents eventually “follow the crowd”. Models for such cascades have been well studied for Bayes-rational agents faced with deciding between two possible actions - one “good” action and one “bad” action. In this paper, we consider the case when these agents instead have more than two actions, where again only one of these is good. We show that sequential observational learning in such settings has substantially different properties compared to the binary action case and further show than increasing the number of “bad” choices from 1 to 2, can improve the agents' learning.

The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, which may provide a new tool for time-varying databases. In this work, we introduce age-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we assume knowledge of the data process’s statistical information and establish a connection between classical DP and age-dependent DP. We use this connection to characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization reveals that the total variation distance is the sole essential statistical information. Moreover, we demonstrate that aging, which involves utilizing stale data inputs and/or delaying the release of outputs, can serve as a novel strategy for safeguarding data privacy, in addition to the traditional approach of injecting noise in the DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP under any publishing and aging policies. We then characterize the optimal tradeoffs between privacy risk and utility and show how this can be achieved. Finally, case studies show that to achieve an arbitrarily small privacy risk in a single-query case, combing aging and noise injection only leads to a bounded accuracy loss, whereas using noise injection only (as in the benchmark case of DP) will lead to an unbounded accuracy loss.

To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrated on developing effective and efficient unlearning techniques. However, the issue of incentivizing valuable users to remain engaged and preventing their data from being unlearned is still under-explored, yet important to the unlearned model performance. This paper focuses on the incentive issue and develops an incentive mechanism for federated learning and unlearning. We first characterize the leaving users' impact on the global model accuracy and the required communication rounds for unlearning. Building on these results, we propose a four-stage game to capture the interaction and information updates during the learning and unlearning process. A key contribution is to summarize users' multi-dimensional private information into one-dimensional metrics to guide the incentive design. We show that users who incur high costs and experience significant training losses are more likely to discontinue their engagement through federated unlearning. The server tends to retain users who make substantial contributions to the model but has a trade-off on users' training losses, as large training losses of retained users increase privacy costs but decrease unlearning costs. The numerical results demonstrate the necessity of unlearning incentives for retaining valuable leaving users, and also show that our proposed mechanisms decrease the server's cost by up to 53.91% compared to state-of-the-art benchmarks.

Networks that provide agents with access to a common database of the agents' actions enable an agent to easily learn by observing the actions of others, but are also susceptible to manipulation by “fake” agents. Prior work has studied a model for the impact of such fake agents on ordinary (rational) agents in a sequential Bayesian observational learning framework. That model assumes that ordinary agents do not have an ex-ante bias in their actions and that they follow their private information in case of an ex-post tie between actions. This paper builds on that work to study the effect of fake agents on the welfare obtained by ordinary agents under different ex-ante biases and different tie-breaking rules. We show that varying either of these can lead to cases where, unlike in the prior work, the addition of fake agents leads to a gain in welfare. This implies that in such cases, if fake agents are absent or are not adequately present, an altruistic platform could artificially introduce fake actions to effect improved learning.

Security is a critical concern in shared spectrum environments. In additional to degrading service, attacks can influence the market interactions between competing service providers (SPs). This paper investigates these interactions by considering two SPs engaged in Cournot competition while utilizing both proprietary and shared spectrum, with shared spectrum available in either licensed or open-access forms. Additionally, we assume the presence of an attacker whose objective is to deny service to one or more of the shared bands for a fraction of the time, consequently reducing the overall total revenue. We analyze the optimal forms of attacks under different attacker objectives and their repercussions on the resulting market equilibrium. Utilizing these analyses, we compare the impacts of various spectrum sharing approaches (licensed and open access) and differing amounts of spectrum holdings of the two providers.

We study the design of incentive mechanisms for the problem of information elicitation without verification (IEWV). In IEWV, a data requester seeks to design proper incentives to optimize the tradeoff between the quality of information (collected from distributed crowd workers) and the total cost of incentives (provided to crowd workers) without verifiable ground truth. While prior work often relies on sufficient knowledge of worker information, we study a scenario where the data requester cannot access workers’ heterogeneous information quality and costs ex-ante. We propose a continuum-armed bandit-based incentive mechanism that dynamically learns the optimal reward level from workers’ reported information. A key challenge is that the data requester cannot evaluate the workers’ information quality without verification, which motivates the design of an inference algorithm. The inference problem is non-convex, yet we reformulate it as a bi-convex problem and derive an approximate solution with a performance guarantee, which ensures the effectiveness of our online reward design. We further enhance the inference algorithm using part of the workers’ historical reports. We also propose a novel rule for the data requester to aggregate workers’ solutions more effectively. We show that our mechanism achieves a sub-linear regret$\tilde {O}(T^{1/2})$and outperforms several celebrated benchmarks.

We study a crowdsourcing problem, where a platform aims to incentivize distributed workers to provide high-quality and truthful solutions that are not verifiable. We focus on a largely overlooked yet pratically important asymmetric information scenario, where the platform knows more information regarding workers’ average solution accuracy and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood of obtaining a reward. We first study the case where the platform and workers share the same prior regarding the average worker accuracy (but only the platform observes the realized value). We consider two types of workers: (1)naiveworkers who fully trust the platform's announcement, and (2)strategicworkers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal when facing strategic workers, and the platform may benefit from announcing an average accuracy lower than the actual value. We further study the more challenging non-common prior case, and show the counter-intuitive result that when the platform is uninformed of the workers’ prior, both the platform payoff and the social welfare may decrease as the high accuracy workers’ solutions become more accurate.

When it is difficult to verify contributed solutions in mobile crowdsourcing, the majority voting mechanism is widely utilized to incentivize distributed workers to provide high-quality and truthful solutions. In the majority voting mechanism, a worker is rewarded based on whether his solution is consistent with the majority. However, most prior related work relies on a strong assumption that workers solution accuracy levels are public knowledge, which may not hold in many practical scenarios. We relax such an assumption and propose an online mechanism, which allows the platform to learn the distribution of the workers solution accuracy levels via asking workers to report their private accuracy levels (which do not need to be the true values), in addition to deciding their effort levels and solution reporting strategies. The mechanism design is challenging, as neither the workers task solutions nor their accuracy reports can be verified. We devise a randomized reward mechanism that computes the workers rewards based on their reported accuracy levels, under which the workers obtain rewards if their reported solutions match the majority. Our mechanism induces workers to truthfully report their solution accuracy levels in the long run, and the empirical accuracy distribution converges to the actual accuracy distribution.

Observational learning models seek to understand how distributed agents learn from observing the actions of others. In the basic model, agents seek to choose between two alternatives, where the underlying value of each alternative is the same for each agent. Agents do not know this value but only observe a noisy signal of the value and make their decision based on this signal and observations of other agents’ actions. Here, instead we consider a scenario in which the choices faced by an agent exhibit a negative externality so that value of a choice may decrease depending on the history of other agents selecting that choice. We study the learning behavior of Bayesian agents with such an externality and show that this can lead to very different outcomes compared to models without such an externality.

Autonomous vehicles (AVs) are attractive for ride service providers (RSPs) in part because they eliminate the need to compete for human drivers. We investigate a scenario where two RSPs with AVs compete for customers. We model the problem as a game where the RSPs select prices for each origin-destination pair over multiple time periods in an underlying graph representing the customers’ desired trips. Each RSP also decides the number of AVs to be stationed at each node at each time period to serve the customers’ demands. The number of customers who avail service of a RSP depends on the price selected by the RSP and its competitor. Since the strategy choices available to a RSP depends on its competitor, we seek to compute a Generalized Nash equilibrium (GNE). We show that there may be multiple GNEs. However, when a RSP selects prices in order to deter its competitor when it is not serving a source-destination pair, the game has a potential function and admits a unique GNE. We also compare the competitive prices with a monopoly price where only one RSP is in the market. Numerically, we show that if a network consists of two equal size spatial clusters of demand where the demand between clusters is low, the RSPs may partition the market, i.e, one cluster is served by only one RSP. Hence, the competitive price may become close to the monopoly price.

Mobile crowdsourcing platforms often want to incentivize workers to finish tasks with high quality and truthfully report their solutions by providing proper rewards. Most existing incentive mechanisms reward workers based on the comparison among workers’ reported solutions. However, these mechanisms are vulnerable to worker collusion, i.e., workers coordinate to misreport their solutions. We address such an issue by proposing a novel rewarding mechanism based on a${truth detection}$technology, which relies on the independent verification of the correctness of each worker’s response to some question with animperfectaccuracy. We model the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform optimizes the reward mechanism parameters associated withtruth detectionto maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs (which depend on the output of the truth detector). We analyze the game’s equilibrium and show that our proposed mechanism can effectively mitigate worker collusion. We also propose a novel rule, namedfiltered majority, for the platform to more effectively aggregate the workers’ solutions. Our proposed aggregation rule utilizes truth detection and outperforms the conventional simple majority rule. We further characterize the impact of the truth detection accuracy on the platform’s decisions. Surprisingly, under the simple majority rule, we show that as the truth detection accuracy improves, the platform should always incentivize more workers to exert effort and truthfully report. However, under our proposed filtered majority rule, we show that as the truth detection accuracy improves, in some cases, the platform should incentivize fewer workers and save costs. We further examine the impact of the workers’ imperfect estimation of the truth detection accuracy on the platform’s decisions.

In mobile crowdsourcing, platforms seek to incentivize heterogeneous workers to complete tasks (e.g., road traffic sensing) and truthfully report their solutions. When platforms cannot verify the quality of the workers’ solutions, the crowdsourcing problem is known asinformation elicitation without verification(IEWV). In an IEWV problem, a platform needs to provide incentives to motivate high-quality solutions and truthful reporting of the solutions from the workers. A common approach to solve the IEWV problem is majority voting, where each worker is rewarded according to whether his solution matches the majority’s solution. However, previous work has not considered workers with heterogeneous solution accuracy. This is unrealistic in many domains, where one would expect workers to differ in judgment, expertise, and reliability. Moreover, prior work has not considered how this heterogeneity affects a platform’s tradeoff between the quality of the workers’ solutions and the platform’s cost of achieving this. We address these gaps by studying the interactions between the mobile crowdsourcing platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for majority voting. In Stage II, the workers decide their effort levels and reporting strategies. We show that as a worker’s solution accuracy increases, he is more likely, in equilibrium, to exert effort and truthfully report his solution. However, given a fixed total worker population, surprisingly, the platform’s payoff may decrease in the number of high-accuracy workers. We further characterize the value of knowing the workers’ solution accuracy in terms of improving the platform’s optimal reward design and maximizing its payoff. Knowing such information enables a more effective aggregation of the workers’ solutions. We further design a discriminatory reward policy to incentivize heterogeneous workers. Surprisingly, such a discriminatory policy can improve both the platform’s and the workers’ payoffs, and hence improve the social welfare.

We study a crowdsourcing problem where the platform aims to incentivize distributed workers to provide high-quality and truthful solutions without the ability to verify the solutions. While most prior work assumes that the platform and workers have symmetric information, we study an asymmetric information scenario where the platform has informational advantages. Specifically, the platform knows more information regarding workers' average solution accuracy, and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood that they obtain a reward if exerting effort on the task. We study two types of workers: (1) naive workers who fully trust the announcement, and (2) strategic workers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal for strategic workers, as it may reduce the credibility of the platform's announcement and hence reduce the platform's payoff. Interestingly, the platform may have an incentive to even announce an average accuracy lower than the actual value when facing strategic workers. Another counter-intuitive result is that the platform's payoff may decrease in the number of high-accuracy workers.

In this paper, we study a fresh data acquisition problem to acquire fresh data and optimize the age-related performance when strategic data sources have private market information. We consider an information update system in which a destination acquires, and pays for, fresh data updates from a source. The destination incurs an age-related cost, modeled as a general increasing function of the age-of-information (AoI). The source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. To this end, we design an optimal (economic) mechanism for timely information acquisition by generalizing Myerson's seminal work. The goal is to minimize the sum of the destination's age-related cost and its payment to the source, while ensuring that the source truthfully reports its private information and will voluntarily participate in the mechanism. Our results show that, under some distributions of the source's cost, our proposed optimal mechanism can lead to an unbounded benefit, compared against a benchmark that naively trusts the source's report and thus incentivizes its maximal over-reporting.

The proliferation of real-time applications has spurred much interest in data freshness, captured by the age-of-information (AoI) metric. When strategic data sources have private market information, a fundamental economic challenge is how to incentivize them to acquire fresh data and optimize the age-related performance. In this work, we consider an information update system in which a destination acquires, and pays for, fresh data updates from multiple sources. The destination incurs an age-related cost, modeled as a general increasing function of the AoI. Each source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. The destination decides on the price of updates, when to get them, and who should generate them, based on the sources' reported sampling costs. We show that a benchmark that naively trusts the sources' reports can lead to an arbitrarily bad outcome compared to the case where sources truthfully report. To tackle this issue, we design an optimal (economic) mechanism for timely information acquisition following Myerson's seminal work. To this end, our proposed optimal mechanism minimizes the sum of the destination's age-related cost and its payment to the sources, while ensuring that the sources truthfully report their private information and will voluntarily participate in the mechanism. However, finding the optimal mechanisms may suffer from prohibitively expensive computational overheads as it involves solving a nonlinear infinite-dimensional optimization problem. We further propose a quantized version of the optimal mechanism that achieves asymptotic optimality, maintains the other economic properties, and enables one to tradeoff between optimality and computational overheads. Our analytical and numerical studies show that (i) both the optimal and quantized mechanisms can lead to an unbounded benefit under some distributions of the source costs compared against a benchmark; (ii) the optimal and quantized mechanisms are most beneficial when there are few sources with heterogeneous sampling costs.

Federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents’ incentives to voluntarily participate and obediently follow traditional federated learning algorithms. To be more specific, our analysis reveals that agents with less typical data distributions and relatively more samples are more likely to opt out of or tamper with federated learning algorithms. To this end, we formulate the first faithful implementation problem of federated learning and design two faithful federated learning mechanisms which satisfy economic properties, scalability, and privacy. First, we design aFaithful Federated Learning (FFL) mechanismwhich approximates the Vickrey–Clarke–Groves (VCG) payments via an incremental computation. We show that it achieves (probably approximate) optimality, faithful implementation, voluntary participation, and some other economic properties (such as budget balance). Further, the time complexity in the number of agents$K$is$\mathcal {O}(\log (K))$. Second, by partitioning agents into several clusters, we present a scalable VCG mechanism approximation. We further design a scalable andDifferentially Private FFL (DP-FFL) mechanism, the first differentially private faithful mechanism, that maintains the economic properties. Our DP-FFL mechanism enables one to make three-way performance tradeoffs among privacy, the iterations needed, and payment accuracy loss.

In this paper, the problem of dynamic resource management in a cognitive radio network (CRN) with multiple primary users (PUs), multiple secondary users (SUs), and multiple channels is investigated. An optimization problem is formulated as a multi-agent partially observable Markov decision process (POMDP) problem in a dynamic and not fully observable environment. We consider using deep reinforcement learning (DRL) to address this problem. Based on the channel occupancy of PUs, a multi-agent deep Q-network (DQN)-based dynamic joint spectrum access and mode selection (SAMS) scheme is proposed for the SUs in the partially observable environment. The current observation of each SU is mapped to a suitable action. Each secondary user (SU) takes its own decision without exchanging information with other SUs. It seeks to maximize the total sum rate. Simulation results verify the effectiveness of our proposed schemes.

In online markets, agents often learn from other's actions in addition to their private information. Such observational learning can lead to herding or information cascades in which agents eventually ignore their private information and "follow the crowd". Models for such cascades have been well studied for Bayes-rational agents that arrive sequentially and choose pay-off optimal actions. This paper additionally considers the presence of fake agents that take a fixed action in order to influence subsequent rational agents towards their preferred action. We characterize how the fraction of such fake agents impacts the behavior of rational agents given a fixed quality of private information. Our model results in a Markov chain with a countably infinite state space, for which we give an iterative method to compute an agent's chances of herding and its welfare (expected pay-off). Our main result shows a counter-intuitive phenomenon: there exist infinitely many scenarios where an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome. Moreover, this increase causes a significant improvement in the welfare of every rational agent. Hence, this increase is not only counter-productive for the fake agents but is also beneficial to the rational agents.

It can be profitable for vehicle service providers to set service prices based on users' travel demand on different origin-destination pairs. Prior studies on the spatial pricing of vehicle service rely on the assumption that providers know users' demand. In this paper, we study a monopolistic provider who initially does not know users' demand and needs to learn it over time by observing the users' responses to the service prices. We design a pricing and vehicle supply policy, considering the tradeoff between exploration (i.e., learning the demand) and exploitation (i.e., maximizing the provider's short-term payoff). Considering that the provider needs to ensure the vehicle flow balance at each location, its pricing and supply decisions for different origin-destination pairs are tightly coupled. This makes it challenging to theoretically analyze the performance of our policy. We analyze the gap between the provider's expected time-average payoffs under our policy and a clairvoyant policy, which makes decisions based on complete information of the demand. We prove that after running our policy for D days, the loss in the expected time-average payoff can be at most O((ln D)1/2D−1/4), which decays to zero as D approaches infinity.

Small-cells in licensed spectrum and unlicensed access via Wi-Fi are two commonly used options to reduce the demand for conventional macro-cellular networks and to provide expanded wireless services to low mobility users. The mix of these technologies depends on both the decisions made by wireless service providers (SPs) that seek to maximize revenue, and the allocation of licensed and unlicensed spectrum by regulators. In this paper, we study these interactions and consider heterogeneous cellular networks together with unlicensed access. Both a single monopoly SP and multiple competing SPs are investigated. The SPs split any available licensed spectrum into two separate bands for macro- and small-cells, which are then used to serve two types of users: mobile and fixed. Mobile users must be served by macro-cells only, whereas fixed users can be served by either macro- or small-cells, or alternatively by unlicensed access service. While the providers charge a (different) price per unit rate for licensed access services (macro- or small-cell), unlicensed access is free. We formulate a sequential game in which the users choose a service that yields the highest payoff, and the providers allocate bandwidth across macro-/small-cells. In general, the competition from unlicensed access results in inefficient (albeit unique) market equilibria, and in many cases all or some SPs allocate no resources to small-cell deployment. We conclude by showing how our framework can also be used to optimize the fraction of unlicensed spectrum when new bandwidth becomes available.

Most mobile network operators generate revenues by directly charging users for data plan subscriptions. Some operators now also offer users data rewards to incentivize them to watch mobile ads, which enables the operators to collect payments from advertisers and create new revenue streams. In this work, we analyze and compare two data rewarding schemes: a Subscription-Aware Rewarding (SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR scheme, only the subscribers of the operators' data plans are eligible for the rewards; under the SUR scheme, all users are eligible for the rewards (e.g., the users who do not subscribe to the data plans can still get SIM cards and receive data rewards by watching ads). We model the interactions among an operator, users, and advertisers by a two-stage Stackelberg game, and characterize their equilibrium strategies under both the SAR and SUR schemes. We show that the SAR scheme can lead to more subscriptions and a higher operator revenue from the data market, while the SUR scheme can lead to better ad viewership and a higher operator revenue from the ad market. We further show that the operator's optimal choice between the two schemes is sensitive to the users' data consumption utility function and the operator's network capacity. We provide some counter-intuitive insights. For example, when each user has a logarithmic utility function, the operator should apply the SUR scheme (i.e., reward both subscribers and non-subscribers) if and only if it has a small network capacity.

Small-cells deployed in licensed spectrum can expand wireless service to low mobility users, which potentially reduces the demand for macro-cellular networks with wide-area coverage. Introducing such heterogeneity also makes network resource allocation more complicated. To understand these challenges and tradeoffs we present a two-tier heterogeneous wireless network model with two types of users: mobile users that can only connect to macro-cells; and fixed users that can associate with either macro-cells or small-cells. We study pricing strategies and bandwidth allocation across macro- and small-cells, assuming both monopoly and competitive Service Providers (SPs). For a monopoly SP, we characterize the revenue-maximizing prices and bandwidth allocations. We then consider a competitive scenario, and we show the existence of a unique Nash equilibrium. The possible Nash equilibria for different system parameters are sorted into four categories corresponding to whether or not different SPs assign bandwidth to the macro- and/or small-cells. We also study the allocations that maximize social welfare. For the competitive scenario, we characterize the conditions under which the optimal social welfare is obtained in equilibria as the number of SPs tends to infinity. Case study examples and numerical results illustrate the corresponding pricing and bandwidth allocations.

Many online social networking platforms are leveraging crowdsourcing to enhance the user experience. These platforms seek to incentivize heterogeneous workers to exert efforts to complete tasks (e.g., moderation of posts and articles) and truthfully report their solutions. Output agreement mechanism (e.g., majority voting) is a common approach to this end. In an output agreement mechanism, a worker is rewarded according to whether his solution matches those of his peers. However, prior related work has not studied the workers' heterogeneous solution accuracy and how this heterogeneity affects the platform's payoff. We fill this void by modeling and analyzing the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform chooses the reward level for the majority voting to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs. We show that as a worker's solution accuracy increases, he is more likely to exert effort and truthfully report his solution under the equilibrium reward mechanism. However, given a fixed total worker population, it is surprising that the platform's overall payoff does not monotonically increase in the number of high-accuracy workers. This is because a larger number of high-accuracy workers brings marginally decreasing benefit to the platform, but the rewards required to incentivize them may significantly grow. Moreover, we show that as the solutions of the high-accuracy workers become more accurate, the platform needs a smaller number of such workers to achieve the maximum payoff.

LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of LTE-U and WiFi in unlicensed spectrum In this paper, we instead investigate the impact of such a technology from an economic perspective. We consider a model in which an incumbent service provider (SP) deploys a duty cycle-based technology like LTE-U in an unlicensed band along with operating in a licensed band and competes with one or more entrants that only operate in the unlicensed band using a different technology like WiFi. We characterize the impact of a technology like LTE-U on the market outcome and show that the welfare impacts of this technology are subtle, depending in part on the amount of unlicensed spectrum and number of entrants. We also investigate the impact of the duty cycle and the portion of unlicensed spectrum used by the technology.

Conventionally, mobile network operators charge users for data plan subscriptions. To create new revenue streams, some operators now also incentivize users to watch ads with data rewards and collect payments from advertisers. In this work, we study two such rewarding schemes: a Subscription-Aware Rewarding (SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR scheme, only the subscribers of the operators' existing data plans are eligible for the rewards; under the SUR scheme, all users are eligible for the rewards (e.g., the users who do not subscribe to the data plans can still get SIM cards and receive data rewards by watching ads). We model the interactions among a capacity-constrained operator, users, and advertisers by a two-stage Stackelberg game, and characterize their equilibrium strategies under both the SAR and SUR schemes. We show that the SAR scheme can lead to more subscriptions and a higher operator revenue from the data market, while the SUR scheme can lead to better ad viewership and a higher operator revenue from the ad market. We provide some counter-intuitive insights for the design of data rewards. For example, the operator's optimal choice between the two schemes is sensitive to the users' data consumption utility function. When each user has a logarithmic utility function, the operator should apply the SUR scheme (i.e., reward both subscribers and nonsubscribers) if and only if it has a small network capacity.

We analyze the prioritized sharing between an added value Mobile Virtual Network Operator (MVNO) and multiple Mobile Network Operators (MNOs). An added value MVNO is one which earns added revenue from wireless users in addition to the revenue it directly collects for providing them wireless service. To offer service, an MVNO needs to contract with one or more MNOs to utilize their networks. Agreeing on such a contract requires the MNOs to consider the impact on their revenue from allowing the MVNO to enter the market as well as the possibility that other MNOs will cooperate. To further protect their customers, the MNOs may prioritize their direct customers over those of the MVNO. We establish a multi-stage game to analyze the equilibrium decisions of the MVNO, MNOs, and users in such a setting. In particular, we characterize the condition under which the MVNO can collaborate with the MNOs. The results show that the MVNO tends to cooperate with the MNOs when the band resources are limited and the added value is significant. When there is significant difference in band resources among the MNOs, the MVNO first considers cooperating with the MNO with a smaller band. We also consider the case when the users also have access to unlicensed spectrum.

Economic mechanisms have been widely studied for allocating network resources while accounting for the incentives of self-interested users. Indeed, the well known Vickrey-Clarke-Groves (VCG) mechanism provides an elegant solution to such problems with a strong incentive guarantee. However, VCG mechanisms can incur high communication costs. Recent work has shown that via quantization, one can reduce the communication costs of VCG while maintaining its incentive guarantees for allocating a single divisible resource. However, in many information theoretic settings, the underlying resource constraints are more complex. Here, we consider developing similar quantized mechanisms for one such setting: a Gaussian multiple access wiretap channel. Namely, we seek to allocate secure rates to a set of users assuming that all users employ a superposition coding scheme. This results in an achievable rate region that we show is a polymatroid. We utilize this characterization to design a quantized mechanism with strong incentive properties.

Many network resource allocation problems can be viewed as allocating a divisible resource, where the allocations are constrained to lie in a polymatroid. We consider market-based mechanisms for such problems. Though the Vickrey-Clarke-Groves (VCG) mechanism can provide the efficient allocation with strong incentive properties (namely dominant strategy incentive compatibility), its well-known high communication requirements can prevent it from being used. There have been a number of approaches for reducing the communication costs of VCG by weakening its incentive properties. Here, instead we take a different approach of reducing communication costs via quantization while maintaining VCG's dominant strategy incentive properties. The cost for this approach is a loss in efficiency which we characterize. We first consider quantizing the resource allocations so that agents need only submit a finite number of bids instead of full utility function. We subsequently consider quantizing the agent's bids.

The Citizens Broadband Radio Service (CBRS) recently adopted in the U.S. enables two tiers of commercial users to share spectrum with a third tier of incumbent users. This sharing can be further assisted by Environmental Sensing Capability operators (ESCs), that monitor the spectrum occupancy to determine when use of the spectrum will not harm incumbents. Two key aspects of this framework that impact how firms may compete are the differences in information provided by different ESCs and the different tiers in which a firm may access the spectrum. We develop a game theoretic model that captures both of these features and analyze it to gain insight into their impact. Specifically, we consider a priority access (PA) tier firm has access to the both a licensed band and an unlicensed band, and a general authorized access (GAA) tier firm has access only to the unlicensed band. The PA tier and GAA tier firms compete for users. Our analysis reveals that the amount of unlicensed and licensed bandwidth must be chosen judiciously in order to maximize the social welfare. We also show that a limited amount of unlicensed access by the PA tier firm is beneficial to the user's surplus as well as to the social welfare.

Mobile Virtual Network Operators (MVNOs) are an increasingly growing segment of the market for wireless services. MVNOs do not own their own network infrastructure and so must cooperate with existing Mobile Network Operators (MNOs) to gain access to the network infrastructure needed to enter this market. Cooperating with an MVNO is a non-trivial decision for an MNO in part because the MVNO may then become a potential competitor for customers. One motive for entering into such an arrangement is that the MVNO receives an added value from serving customers beyond what it earns from charging them for wireless service. We study a game theoretic model for the cooperation and competition between an MNO and such an added value MVNO based on models for price competition with congestible resources. Our model captures two different dimensions of how an MNO may cooperate. The first dimension is the payment scheme between the MNO and the MVNO. The second dimension is the access priority that the MNO chooses to offer to the MVNO's customers. We characterize the pros and cons of different cooperation modes and analyze the optimal cooperation mode under different conditions.

The recent framework for tiered spectrum sharing in the 3.5 GHz band establishes rules in which multiple firms called Environment Sensing Capability operators (ESCs) may measure spectrum occupancy and sell these measurements to other firms to help facilitate spectrum access. Motived by this we consider a scenario in which two spectrum access firms (SAs) seeks to access a shared band of spectrum and must in turn purchase spectrum measurements from one of two ESCs. Given the measurements they purchase, the SA firms then compete on price to serve customers in a shared band of spectrum. We study how differences in the quality and price of the spectrum measurements impact the resulting market equilibrium between the SAs and find that having different qualities of measurements available to different SAs can lead to better economic welfare.

A fundamental problem in many network systems is how to allocate limited resources among competing agents, who may have their own incentives. The well-known Vickrey-Clarke-Groves (VCG) mechanism provides an elegant solution to this incentive issue. In particular, VCG implements the socially optimal outcome in dominant strategies. However, it is also well-known that this mechanism can require an excessive amount of communication. Approaches have been studied that relax the communication requirements while also relaxing the incentive guarantees to use Nash equilibria instead of dominant strategies. Here, we take a different approach and study mechanisms with limited information that still have dominant strategy outcomes, but suffer an efficiency loss. We characterize this loss for the case of a single divisible resource. We first consider a mechanism in which information is limited by quantizing the resource into a finite number of units and allocating each of these to one agent via a VCG mechanism. This limits each agent to submitting a finite number of real values. We subsequently consider the case where each value is also quantized before being reported by each agent. Finally, we present numerical examples of the performance of these mechanisms.

Unlicensed spectrum has been viewed as a way to increase competition in wireless access and promote innovation in new technologies and business models. However, several recent papers have shown that the openness of such spectrum can also lead to it becoming over congested when used by competing wireless service providers (SPs). This in turn can result in the SPs making no profit and may deter them from entering the market. However, this prior work assumes that unlicensed access is a separate service from any service offered using licensed spectrum. Here, we instead consider the more common case were service providers bundle both licensed and unlicensed spectrum as a single service and offer this with a single price. We analyze a model for such a market and show that in this case SPs are able to gain higher profit than the case without bundling. It is also possible to get higher social welfare with bundling. Moreover, we explore the case where SPs are allowed to manage the customers' average percentage of time they receive service on unlicensed spectrum and characterize the social welfare gap between the profit maximizing and social welfare maximizing setting.

By not requiring expensive licenses, unlicensed spectrum lowers the barriers for firms to offer wireless services. However, incumbent firms may still try to erect other entry barriers. For example, recent work has highlighted how customer contracts may be used as one such barrier by penalizing customers for switching to a new entrant. However, this work did not account for another potential benefit of unlicensed spectrum, having access to this open resource may incentivize entrants to invest in new and potentially better technology. This paper studies the interaction of contracts and the incentives of firms to invest in developing new technology. We use a game theoretic model to study this and characterize the effect of contracts on economic welfare. The role of subsidies or taxes by a social planner is also considered.

We add to a line of work considering the impact of observation imperfections in models of Bayesian observational learning. In particular, we study a discrete-time model in which in each time-slot, an agent may randomly arrive. Agents who arrive have the opportunity to buy a given item. If an agent chooses to buy, this action is recorded for subsequent agents. However, the decisions of agents that choose not to buy are not recorded. Hence, if no one buys in a given slot, agents are unaware if this was due to no agent arriving or an agent choosing not to buy. We study the impact of this uncertainty on the emergence of information cascades. Using a Markov chain based analysis, we show that the probability of incorrect cascades and the expected time until a cascade happens are not monotonic in the arrival probability of a user. We find that adding a small uncertainty in the arrival information from the perfect information setting will make a buy cascade happen with higher probability than a not-buy cascade. However, if the agents' private signals are weak, then a not-buy cascade is more likely to occur for most arrival rates, resulting in wrong cascades dominating when the item is good and vice-versa when the item is bad.

In this paper, we focus on the performance analysis of a semi-persistent scheduling scheme for vehicular safety communications, motivated by the Mode 4 medium access control protocol in 3GPP Release 14 for Cellular-V2X. An analytical model is built and a fixed point method is used to calculate the collision probability and average delay in both fully connected and partially connected cases under the assumption of perfect PHY performance. We use Monte Carlo simulation to verify the results obtained in the analytical model. The simulation results show that our analytical model can give a good estimation of the collision probability and average delay. We verify that a trade-off between delay and collision probability can be achieved with a flexible resource block selection. Monte Carlo simulation results show that with the flexible resource selection scheme average delay can be shortened significantly with only a small compromise in collision probability.

Downlink weighted sum rate maximizing beamformer design is considered for joint processing (JP) coordinated multi-point (CoMP) transmission. Global channel state information exchange, required by the centralized JP CoMP processing, is in many scenarios impractical due to the backhaul latency and capacity requirements. Low overhead decentralized processing enables JP even with limited backhaul capacity. The proposed best response (BR) design also takes into account the possibly non-orthogonal pilot design and noisy pilot estimation. By allowing spatially overlapping pilot sequences, the transceiver processing requires only the channel state information of locally served users. This enables more flexible pilot design and makes the proposed approach realizable in practical time correlated and noisy channel conditions. Furthermore, the proposed BR algorithm provides low signaling overhead for systems with limited backhaul capacity. Robustness for fading channel conditions and limited pilot resources is shown by numerical examples.

We consider a set of users communicating over a Gaussian multiple access channel in the presence of an eavesdropper. The information theoretic secrecy rates for such settings have been well studied under the assumptions that all users are cooperative. In more recent work, a game theoretic model was studied in which each user selected its own rate. This game was shown to have multiple possible equilibria. Here, we consider a related question in which a mechanism is used to solicit information from the users and then allocate secrecy rates among them. We study three simple mechanisms and analyze the performance of each for a small set of users (N = 2, 3) in terms of their worst-case efficiency. For N = 2, we give a closed form lower bound on the efficiency for each scheme and show one mechanism, which generalizes the well-known Kelly mechanism, has the best efficiency. We then consider N = 3 users and give numerical results for different user utility functions.

We consider adding secrecy constraints to a model of information theoretic games introduced in earlier works. In these games, each user autonomously selects their encoding and decoding strategy with the objective of maximizing their own secure rate in the presence of a single eavesdropper. We study the Nash equilibrium regions for such games when the users are communicating over linear deterministic models of a multiple access channel and an interference channel. In particular, we show that for interference channels, the presence of an eavesdropper results in significantly different equilibrium properties when an eavesdropper is not present.

Gradient based downlink beamforming with low computational complexity and training overhead is proposed for joint processing (JP) coordinated multi-point transmission (CoMP). Pilot contamination and estimation noise are taken into account in the pilot based transceiver training process. The proposed designs enable decentralized JP when the backhaul and computational limitations do not allow centralized processing. The impact of backhaul quantization is also considered. The stochastic gradient based designs are shown to be more robust to feedback quantization when compared to more complex methods. The trade-off between the implementation complexity and performance is established for the proposed algorithms. The results show that low complexity decentralized JP CoMP is feasible even with limited backhaul capacity.

We investigate a set of non-cooperative radio resource management games in a Gaussian interference channel, where the receivers are equipped with two stage Successive Interference Cancellers (SIC). In these games users decide on their transmission power, rate and Interference Canceling (IC) strategy. A one-shot game, as well as two-stage variants, where either rate, or IC and rate, are decided in the second stage, are considered. We characterize the equilibria of the games and establish a relationship between the equilibria of the one shot and two-stage games. Postponing the rate decision to a second stage stabilizes the game in a region where no pure strategy Nash Equilibrium exists for the one-shot game. Further postponing the IC decision to a second stage stabilizes the game completely, an equilibrium exists in all network configurations. We simulate a 2-pair device-to-device network where these games are used for radio resource management. The regions where the one-shot game is unstable have a considerable probability, leading to a considerable outage probability. By staging the game, such outage can be mitigated, or removed altogether.

We consider a secondary spectrum market where primaries set prices for their unused channels. The payoff of a primary then depends on the availability of channels for its competitors, which a primary might not have information about. We study a model where a primary can acquire this competitor's channel state information (C-CSI) at a cost. We formulate a game between two primaries, where each primary decides whether to acquire the C-CSI or not and then selects its price based on that. We first characterize the Nash equilibrium of this game for a symmetric model where the C-CSI is perfect. We show that the payoff of a primary is independent of the C-CSI acquisition cost. We then generalize our analysis to allow for imperfect estimation and cases, where the two primaries have different C-CSI costs or different channel availabilities. Our results show interestingly that the payoff of a primary increases when there is estimation error. We also show that surprisingly the expected payoff of a primary may decrease when the C-CSI acquisition cost decreases or primaries have different availabilities.

Next-generation networks will be characterized by three key features:heterogeneity, in terms of technologies and services,dynamics, in terms of rapidly varying environments and uncertainty, andsize, in terms of the numbers of users, nodes, and services. The emergence of such large-scale and decentralized heterogeneous networks operating under dynamic and uncertain environments imposes new challenges in the design, analysis, and optimization of networks. The past decade has witnessed a confluence among the disciplines of networks, games, and economics, which has necessitated novel mathematical tools and designs that can truly remove the boundaries between these disciplines. In this context, advancing game-theoretic models and tailoring them towards the optimization and operation of future networked systems become pressing needs for our research community. The main goal of this IEEE JSAC Special Issue on “Game Theory for Networks” is to collect cutting-edge contributions that address and show the latest developments in game-theoretic models for emerging networking applications. The response of the community to the call has been overwhelming. We received a total of 120 submissions. We want to thank all the authors who submitted their works to this Special Issue. After a strict and selective review process, we accepted 40 papers and decided to publish two issues. Papers were selected based on their appropriateness for and relevance to the Special Issue as well as their technical merits. Unfortunately, a number of interesting papers did not make the cut because of the criteria set forth above and also due to the constraints on the total page count in a JSAC Special Issue. We hope that such interesting papers will find other venues for publication.

This is the second part of the IEEE JSAC Special Issue on “Game Theory for Networks.” The response of the community to the call has been overwhelming. We received a total of 120 submissions. We want to thank all the authors who submitted their works to this Special Issue. After a strict and selective review process, we accepted 40 papers and decided to publish two issues, each of 20 papers. The first one was published in February 2017. The papers of this second issue cover a wide selection of topics as follows.

Small cells deployed in licensed spectrum and unlicensed access via WiFi provide different ways of expanding wireless services to low mobility users. That reduces the demand for conventional macro-cellular networks, which are better suited for wide-area mobile coverage. The mix of these technologies seen in practice depends in part on the decisions made by wireless service providers that seek to maximize revenue, and allocations of licensed and unlicensed spectrum by regulators. To understand these interactions we present a model in which a service provider allocates available licensed spectrum across two separate bands, one for macro- and one for small-cells, in order to serve two types of users: mobile and fixed. We assume a service model in which the providers can charge a (different) price per unit rate for each type of service (macro- or small-cell); unlicensed access is free. With this setup we study how the addition of unlicensed spectrum affects prices and the optimal allocation of bandwidth across macro-/small-cells. We also characterize the optimal fraction of unlicensed spectrum when new bandwidth becomes available.

In a secondary spectrum market primaries set prices for their unused channels to the secondaries. The payoff of a primary depends on the channel state information (CSI) of its competitors. We consider a model where a primary can acquire its competitors CSI at a cost. We formulate a game between two primaries where each primary decides whether to acquire its competitor's CSI or not and then selects its price based on that. Our result shows that no primary decide to acquire its competitor's CSI with an absolute certainty. When the cost of acquiring the CSI is above a threshold, there is a unique Nash Equilibrium (NE) where both the primaries remain uninformed of their respective competitor's CSI. When the cost is below the threshold, in the unique NE each primary randomizes between its decision to acquire the CSI or not. Our result reveals that irrespective of the cost of acquiring the CSI, the expected payoff of a primary remains the same.

Social learning encompasses situations in which agents attempt to learn from observing the actions of other agents. It is well known that in some cases this can lead to information cascades in which agents blindly follow the actions of others, even though this may not be optimal. Having agents provide reviews in addition to their actions provides one possible way to avoid “bad cascades.” In this paper, we study one such model where agents sequentially decide whether or not to purchase a product, whose true value is either good or bad. If they purchase the item, agents also leave a review, which may be imperfect. Conditioning on the underlying state of the item, we study the impact of such reviews on the asymptotic properties of cascades. For a good underlying state, using Markov analysis we show that depending on the review quality, reviews may in fact increase the probability of a wrong cascade. On the other hand, for a bad underlying state, we use martingale analysis to bound the tail-probability of the time until a correct cascade happens.

There has been much interest in expanding the amount of unlicensed spectrum available for wireless services. Such spectrum reduces the barriers to entry by removing the need to purchase expensive licenses and so may lead to increased competition. However, this spectrum also has the risk of becoming over-congested, which could deter service providers from investing and offering service. Recent work has studied this trade-off by considering game theoretic models where service providers first make decisions to invest and then subsequently compete for customers. In such cases, the only Nash equilibria are often for a single service provider to enter and act as a monopolist. These conclusions are based on a model where service providers simultaneously make investment decisions with full knowledge about their competitors' costs. Here, we relax these assumptions and consider more realistic scenarios where service providers make investment decisions at different times and also consider cases where these decisions are made with incomplete information about the other service provider's costs. For a class of such games, we characterize the resulting Nash equilibria and show that differences in timing and uncertainty lead to a richer class of possible equilibria.

Decentralized downlink beamformer design for coherent coordinated multi-point transmission is proposed with weighted sum rate maximization system performance objective. The weighted sum rate is maximized by successive convex approximation of the corresponding weighted mean-squared error minimization problem. Decentralized beam coordination is achieved by employing a best response design, where each base station designs its own precoders in parallel assuming fixed transmission from the adjacent cells. After each beamformer update, the fixed terms are updated according to the solutions of the cooperating transmitters. The proposed design incorporates a bi-directional beamformer signaling scheme to improve the convergence properties. This scheme exploits the time division duplexing frame structure and is shown to improve the training latency of the iterative transceiver design. Furthermore, the improved transceiver convergence rate enables periodic beamformer reinitialization, which greatly improves the achieved system performance in dense networks.

We consider two small cell operators deployed in the same geographical area, sharing spectrum resources from a common pool. A method is investigated to coordinate the utilization of the spectrum pool without monetary transactions and without revealing operator-specific information to other parties. For this, we construct a protocol based on asking and receiving spectrum usage favors by the operators, and keeping a book of the favors. A spectrum usage favor is exchanged between the operators if one is asking for a permission to use some of the resources from the pool on an exclusive basis, and the other is willing to accept that. As a result, the proposed method does not force an operator to take action. An operator with a high load may take spectrum usage favors from an operator that has few users to serve, and it is likely to return these favors in the future to show a cooperative spirit and maintain reciprocity. We formulate the interactions between the operators as a repeated game and determine rules to decide whether to ask or grant a favor at each stage game. We illustrate that under frequent network load variations, which are expected to be prominent in small cell deployments, both operators can attain higher user rates as compared to the case of no coordination of the resource utilization.

A Gaussian multiple access wire-tap channel with confidential messages is studied, where multiple users attempt to transmit private messages to a legitimate receiver in the presence of an eavesdropper. While prior work focused on the case where the users were cooperative, we assume that each user is selfish and and so are modeled as playing a non-cooperative game. We assume all users send a superposition of two Gaussian codebooks: one for their confidential messages and one for “filling” the evesdropper's channel. For such a scheme, we give a characterization of the achievable rate region defined by Tekin and Yener using polymatroid properties. We then use this to find the Nash equilibrium region for this non-cooperative game. Furthermore, we give algorithms for finding the best and worst Nash equilibria for a given channel.

Two decentralized minimum mean-squared error downlink beamformer designs are proposed for multiple-input single-output coherent coordinated multi-point transmission. We propose a parallel beamformer design with a fast initial rate of convergence for systems with relatively few cooperative base stations (BSs). An alternating direction method of multipliers based design is provided for more complex systems with a large number of cooperating BSs. Support for data sharing among the serving BSs is assumed over limited back-haul connectivity. Channel state information (CSI) is not shared among the cooperating transmitters, and, thus, only local CSI is available at each BS via uplink pilot signaling.

Informational cascades are said to occur when rational agents ignore their own private information and blindly follow the actions of other agents. Models for such cascades have been well studied for Bayesian agents, who observe perfectly the actions of other agents. In this paper, we investigate the impact of errors in these observations; the errors are modelled via a binary symmetric channel (BSC). Using a Markov chain model, we analyze the net payoff of each agent as a function of his signal quality and the crossover error probability in the channel. Our main result is that a lower error level does not always lead to a higher payoff when the number of agents is large.

This paper considers a one-dimensional model for a cellular network in which neighboring base stations may cooperatively transmit to users located between them. A distributed algorithm is given for deciding on the power allocation of each base station as well as on which users to serve either cooperatively or individually. The algorithm is proven to converge monotonically and the sum rate performance of different limit points is illustrated. Numerical results are presented that illustrate the performance of the algorithm in terms of sum rate, convergence speed and a fairness metric.

This work focuses on the design and analysis of distributed stochastic precoding algorithms for multiple-input single-output (MISO) interference channels, where each transmitter is provided with mean information of its intended channel and that of interfering channels. Unlike in cases where exact channel gains are known as in most existing works, here generalrank precoding is required for optimality instead of the rank-one beamforming. An efficient algorithm for the distributed implementation of the Nash equilibrium precoding is first proposed. A sufficient condition for this algorithm to converge to the unique equilibrium is derived for the two-user case based on stochastic ordering, and is valid for a wide range of system parameters. To improve the sum-rate performance under medium to strong interference, a pricing-based algorithm is also provided and its convergence analyzed. The two algorithms are compared in terms of sum-rate and system overhead.

In a limited form cellular providers have long shared spectrum in the form of roaming agreements. The primary motivation for this has been to extend the coverage of a wireless carrier's network into regions where it has no infrastructure. As devices and infrastructure become more agile, such sharing could be done on a much faster time-scale and have advantages even when two providers both have coverage in a given area, e.g., by enabling one provider to acquire “overflow” capacity from another provider during periods of high demand. This may provide carriers with an attractive means to better meet their rapidly increasing bandwidth demands. On the other hand, the presence of such a sharing agreement could encourage providers to underinvest in their networks, resulting in poorer performance. We adapt the newsvendor model from the operations management literature to model such a situation and to gain insight into these trade-offs. In particular, we analyze the structure of revenue-sharing contracts that incentivize both capacity sharing and increased access for end-users.

The sum-capacity of a 2-user linear deterministic interference channel (LDIC) can always be achieved with simple deterministic codes. The existence and design of such codes has been shown to be related to an underlying combinatorial structure of the channel. This is used here to explore the capacity of parallel or ergodic LDICs and LDICs with output feedback. We present simple algorithms that generate sum-rate optimal schemes in these cases. In the case of parallel LDICs this approach gives insight into when the channels are separable, i.e. when coding over component channels is not required. We further demonstrate that output feedback can change the separability of such channels.

We investigate a non-cooperative interference canceling and power control (IC-PC) game in a Gaussian interference channel, where the receivers are able to process at most two codewords at a time. We characterize the equilibria of this game. As opposed to the pure power control game and the rate splitting game between the same players, we find that in the IC-PC game, there exist Nash equilibria where a player voluntarily reduces his power in order to enable interference canceling and achieve a higher rate.

This work discusses a pricing-based distributed network power minimization approach for multicarrier networks. The aim is to allocate the powers of transmitters over available carriers, such that all Transmitter-Receiver (Tx-Rx) links meet a rate constraint and the total transmit power of the network is minimized. We seek to find a local optimum of the total transmit power by solving the Karush-Kuhn-Tucker (KKT) optimality conditions in a distributed way. Each transmitter minimizes the weighted sum of its powers over carriers, subject to a fixed rate constraint, by a weighted waterfilling principle. The weights consist of interference pricing terms received from interfered links. The exchange of information among the Tx-Rx links enables a non-selfish response that reduces mutual interference. Performance is evaluated in a Small Cell Network (SCN) and compared to a baseline non-cooperative approach. The results show that the proposed algorithm can guarantee a higher rate than the baseline approach, while reducing significantly the total transmit power of the network.

The throughput and stability properties of wireless network coding are evaluated for an arbitrary number of terminals exchanging broadcast traffic with the aid of a relay. First, coding and scheduling schemes are derived that minimize the number of transmissions needed for each node to broadcast one packet. For stochastically varying traffic, the stable throughput is then compared under both digital and analog network coding schemes. The initial analysis focuses on a network with a single relay. Extensions to arbitrary terminal-relay configurations are then outlined for a general multihop network. Backpressure-like algorithms for jointly achieving throughput optimal scheduling and network coding are given for each network coding scheme.

Markets are often viewed as a key ingredient in facilitating more efficient dynamic spectrum access. In this paper we consider how such spectrum markets are influenced by a key property of the wireless medium: interference. Interference can result in "complementarities" among the "spectrum goods" being traded, which complicates the design of an efficient market mechanism. We consider several alternative models for defining such spectrum goods, and explore the impact of these choices on the complexity of the resulting market.

When transmitting stochastically arriving data over fading channels, there is an inherent tradeoff between the required average transmission power and the average queueing delay experienced by the data. This tradeoff can be exploited by appropriately scheduling the transmission of data over time. In this paper, we study the behavior of the optimal power-delay tradeoff for a single user in the regime of asymptotically small delays. In this regime, we first lower bound how much average power is required as a function of the average queueing delay. We show that the rate at which this bound increases as the delay becomes asymptotically small depends on the behavior of the fading distribution near zero, as well as the arrival statistics. We lower bound this rate for two different classes of fading distributions: one class that requires infinite power to minimize the queueing delay and one class that requires only finite power. We then show that for both classes, the bounds can essentially be achieved by a sequence of simple “channel threshold” policies, which only transmit when the channel gain is greater than a given threshold. We also consider several other transmission scheduling policies and characterize their convergence behavior in the small-delay regime.

In this paper we derive the sum-capacity of 3-user linear deterministic interference channels (LDIC) under certain connectivity assumptions with symmetric parameters. The results also directly yield the sum-capacity of a 3-user fully-connected LDIC with symmetric parameters. We further illustrate with an example the potential difficulties in extending the results to asymmetric cases.

Approximate solutions to some long-standing multi-terminal information theoretic problems, in particular interference channels, have been made possible by study of the corresponding linear deterministic models. Here, we illustrate a combinatorial structure underlying linear deterministic interference channels (LDIC). This enables a systematic design of sum-capacity achievable schemes in a general 2-user LDIC.

Interference between nodes is a critical impairment in mobile ad hoc networks. This paper studies the role of multiple antennas in mitigating such interference. Specifically, a network is studied in which receivers apply zero-forcing beamforming to cancel the strongest interferers. Assuming a network with Poisson-distributed transmitters and independent Rayleigh fading channels, the transmission capacity is derived, which gives the maximum number of successful transmissions per unit area. Mathematical tools from stochastic geometry are applied to obtain the asymptotic transmission capacity scaling and characterize the impact of inaccurate channel state information (CSI). It is shown that, if each node cancels interferers, the transmission capacity decreases as as the outage probability vanishes. For fixed , as grows, the transmission capacity increases as where is the path-loss exponent. Moreover, CSI inaccuracy is shown to have no effect on the transmission capacity scaling as vanishes, provided that the CSI training sequence has an appropriate length, which we derive. Numerical results suggest that canceling merely one interferer by each node may increase the transmission capacity by an order of magnitude or more, even when the CSI is imperfect.

We explore the feasibility of linear interference alignment (IA) in MIMO cellular networks. Each base station (BTS) has Nttransmit antennas, each mobile has Nrreceive antennas, and a BTS transmits a single beam to each active user. We present a necessary Zero-Forcing (ZF) condition for zero interference in terms of the number of users, the number of cells, Ntand Nr. We then examine the performance of iterative (forward-backward) algorithms for jointly optimizing the transmit precoders with linear receivers. Modifications of the max-SINR and minimum leakage algorithms are presented, which are observed to converge to a ZF solution whenever the necessary conditions are satisfied. In contrast, convergence of the (original) max-SINR algorithm is problematic when the necessary conditions are satisfied with (near) equality. A more restrictive ZF condition is presented, which predicts when these convergence problems are unlikely to occur.

Distributed medium access control (MAC) protocols are proposed for wireless networks assuming that one-hop peers can exchange a small amount of state information periodically. Each station maintains a state and makes state transitions and transmission decisions based on its state and recent state information collected from its one-hop peers. A station can adapt its packet length and the size of its state space to the amount of traffic in its neighborhood. It is shown that these protocols converge to a steady state, where stations take turns to transmit in each neighborhood without collision. An important consequence of this work is that using such protocols, an efficient time-division multiple access (TDMA) like schedule can be formed in a distributed manner, as long as the topology of the network remains static or changes slowly with respect to the execution of the protocol.

Recently, Berry-Tse introduced a model for information theoretic games on interference channels, which combines game theory and information theory to analyze the interaction of selfish users. The fundamental quantity in such games is the Nash equilibrium region which has been characterized in several specific interference channels. This paper uses the game theoretic techniques of potential functions to study this region for general K-user linear deterministic interference channels. In particular, it is shown that the Nash equilibrium region is non-empty for any such K-user interference channel.

We consider an interference network with multi-carrier transmission over M parallel sub-channels. There are K transmitter-receiver pairs, each transmitter transmits a single data stream with a rank-one precoding matrix, and the receivers are assumed to be linear. We show that a necessary condition for zero interference (alignment across sub-channels) is K ≤ 2M-2. In contrast, for a Multi-Input Multi-Output (MIMO) interference network with M×M spatial channels (full channel matrices) the corresponding condition is known to be K ≤ 2M - 1. We also characterize the sum rate at high Signal-to-Noise Ratios (SNR) by bounding the SNR offset (x-intercept) of the asymptote of the sum rate vs SNR curve. For a randomly chosen aligned solution as M increases, this offset shifts to the right as logM. In contrast, the SNR offset for a MIMO interference network does not increase with M. An approximation for the performance of sampling the best out of L aligned solutions is also presented. Numerical results show the analytical asymptotes accurately predict the sum rate curves at moderate to high SNRs.

Extensive spectrum markets have the potential to enable more efficient use of this limited resource. Such markets must account for particular properties of the underlying wireless medium. In this paper we focus on one such aspect: the role of interference created among different agents who may purchase the right to use the same spectrum at nearby locations. Such interference can result in “complementarities” among the spectrum goods being traded, which complicates the design of an efficient market. We begin with a simple linear model for these complementarities that was shown to be computationally difficult in earlier work. We give several approximation algorithms for this model. We then consider several alternative models in which the spectrum goods are defined in different ways and explore the impact of these choices on the complexity of the resulting market.

The interference channel is the simplest communication scenario where multiple autonomous users compete for shared resources. We combine game theory and information theory to define the notion of a Nash equilibrium region of the interference channel. The notion is game theoretic: it captures the selfish behavior of each user as they compete. The notion is also information theoretic: it allows each user to use arbitrary communication strategies as it optimizes its own performance. We give an exact characterization of the Nash equilibrium region of the two-user linear deterministic interference channel and an approximate characterization of the Nash equilibrium region of the two-user Gaussian interference channel to within 1 bit/s/Hz.

We consider two sources in a wireless network exchanging stochastically varying traffic using an intermediate relay. Each relay use incurs some cost, which, for example, could be transmission energy. This cost is shared between the sources when packets from both are transmitted simultaneously by the relay using network coding. If the relay transmits a packet originating from one source only, the cost is incurred by that source only. In this setting, we study transmission policies that tradeoff the average cost with the average packet delay. We first present the cost-delay tradeoff for a centralized scheme using Lyapunov stability arguments. Next, we consider a distributed policy, where each source aims to optimize its own cost-delay tradeoff. We determine the Nash equilibrium of the resulting non-cooperative game and show that it performs worse than the centralized algorithm. To overcome this limitation, we introduce a pricing mechanism at the relay, which is shown to achieve the centralized performance. These algorithms, though oblivious to the arrival statistics, do require global knowledge of queue backlogs. Lastly, we consider distributed algorithms that overcome this requirement. Among those, we observe that simple queue-length threshold algorithms perform remarkably well.

This paper studies medium access control (MAC) protocols for regular wireless networks, where only nearest-neighbor interactions are involved. Each station chooses a state in the current time slot, which determines whether it transmits or not, based on its own state and the states of all its nearest neighbors in the previous time slot. The dynamics of the network follow that of a Markov Chain of Markov Fields, which is shown to converge to a stationary distribution for certain types of interactions. It is found that this type of protocols can achieve the optimal one-hop broadcast throughput in regular wireless networks. In case each station can only distinguish between transmitting and idle neighbors, the interactions of the network can be described using the Ising model in statistical mechanics. For this case, a MAC protocol is designed that can achieve a throughput close to the optimum.

Due to the broadcast nature of the wireless medium, wireless networks are highly susceptible to jamming attacks. Such attacks are often studied in a game theoretic framework under the assumption of uninterrupted traffic subject to continuous jamming opportunities. Instead, we analyze the effect of dynamically changing traffic on jamming games for power controlled medium access. Random packet arrivals raise the possibility that the transmitter queues may be empty when jamming attacks start and thus waste the energy of jammers. We consider a non-cooperative game in which transmitters and jammers select their transmission power to balance the transmission cost subject to delay and energy constraints. We show that jammers incur a significant performance loss when they do not have knowledge of transmitter queue states. Dynamic traffic increases the immunity to jamming attacks and gives insights into defense mechanisms.

We consider optimizing packet sizes and the reuse factor to minimize the delay required to send a message between two nodes in a linear multi-hop wireless networks subject to a reliability constraint. In earlier work, this problem was considered for a network in which each node only decoded the transmission of the previous node, treating the transmissions of all other nodes as noise. Here, we consider a cooperative transmission scheme in which a node uses the transmissions of all previous nodes to decode a given message. We analyze the growth of the delay as well as the optimized system parameters as a function of the message size.

In this paper, the scheduling and resource allocation problem for the downlink in a code-division multiple access (CDMA)-based wireless network is considered. The problem is to select a subset of the users for transmission and for each of the users selected, to choose the modulation and coding scheme, transmission power, and number of codes used. We refer to this combination as the physical layer operating point (PLOP). Each PLOP consumes different amounts of code and power resources. The resource allocation task is to pick the ¿optimal¿ PLOP taking into account both system-wide and individual user resource constraints that can arise in a practical system. This problem is tackled as part of a utility maximization problem framed in earlier papers that includes both scheduling and resource allocation. In this setting, the problem reduces to maximizing the weighted throughput over the state-dependent downlink capacity region while taking into account the system-wide and individual user constraints. This problem is studied for the downlink of a Gaussian broadcast channel with orthogonal CDMA transmissions. This results in a tractable convex optimization problem. A dual formulation is used to obtain several key structural properties. By exploiting this structure, algorithms are developed to find the optimal solution with geometric convergence.

We study distributed algorithms for updating transmit preceding matrices for a two-user Multi-Input/Multi-Output (MIMO) interference channel. Our objective is to maximize the sum rate with linear Minimum Mean Squared Error (MMSE) receivers, treating the interference as additive Gaussian noise. An iterative approach is considered in which given a set of preceding matrices and powers, each receiver announces an interference price (marginal decrease in rate due to an increase in interference) for each received beam, corresponding to a column of the precoding matrix. Given the interference prices from the neighboring receiver, and also knowledge of the appropriate cross-channel matrices, the transmitter can then update the beams and powers to maximize the rate minus the interference cost. Variations on this approach are presented in which beams are added sequentially (and then fixed), and in which all beams and associated powers are adjusted at each iteration. Numerical results are presented, which compare these algorithms with iterative water-filling (which requires no information exchange), and a centralized optimization algorithm, which finds locally optimal solutions. Our results show that the distributed algorithms perform close to the centralized algorithm, and by adapting the rank of the precoder matrices, achieve the optimal high-SNR slope.

Vehicular networks are emerging as a new distributed system environment with myriad possible applications. Most studies on vehicular networks are carried out via simulation, given the logistical and economical problems with large-scale deployments. This paper investigates the impact of realistic radio propagation settings on the evaluation of VANET-based systems. Using a set of instrumented cars, we collected IEEE 802.11b signal propagation measurements between vehicles in a variety of urban and suburban environments. We found that signal propagation between vehicles varies in different settings, especially between line-of-sight ("down the block") and non line-of-sight ("around the corner") communication in the same setting. Using a probabilistic shadowing model, we evaluate the impact of different parameter settings on the performance of an epidemic data dissemination protocol and discuss the implications of our findings. We also suggest a variation of a basic signal propagation model that incorporates additional realism without sacrificing scalability by taking advantage of environmental information, including node locations and street information.

Routing to mobile nodes in a wireless network is conventionally performed by associating a static IP address (or a geographic location) to each node, and routing to that address using routing tables at intermediate nodes that are updated periodically to reflect mobility-induced network topology changes. This mode of routing works when the mobiles' speeds as well as the number of mobiles are small. However, in the presence of large number of fast-moving mobiles, such approaches are infeasible and can lead to excessive overheads, routing failures and hence, throughput loss. In this paper, we consider a wireless network over a domain with a collection of static nodes (that form a connected cover of the domain) and mobile nodes, where the mobile nodes can move in an arbitrary (non-ergodic) manner over sub-domains of the network. For such a system, we develop new routing algorithms (based on a spatial multi-resolution search) that we show are efficient both in terms of routing overheads and throughput. In particular, we show that the achievable rate region of the proposed algorithm is within a poly-logarithmic constant of the optimal rate region with non-ergodic mobility.

We study distributed algorithms for allocating powers and/or adjusting beamforming vectors in a peer-to-peer wireless network which may have multiple-input-single-output (MISO) links. The objective is to maximize the total utility summed over all users, where each user's utility is a function of the received signal-to-interference-plus-noise ratio (SINR). Each user (receiver) announces an interference price, representing the marginal cost of interference from other users. A particular user (transmitter) then updates its power and beamforming vector to maximize its utility minus the interference cost to other users, which is determined from their announced interference prices. We show that if each transmitter update is based on a current set of interference prices and the utility functions satisfy certain concavity conditions, then the total utility is non-decreasing with each update. The proof is based on the convexity of the utility functions with respect to received interference, and applies to rate utility functions, and an arbitrary number of interfering MISO links. The extension to multi-carrier links is discussed as well as algorithmic variations in which the prices are not immediately updated after power or beam updates.

We consider a game theoretic model for two users communicating over an interference channel, in which each user can autonomously select its encoding and decoding strategy with the objective of maximizing its own rate. We give an information theoretic formulation for this game, which enables us to define a Nash equilibrium region that is a natural extension of the information theoretic capacity region of this channel. In previous work, we completely characterized this Nash equilibrium region for a deterministic interference channel model. Here, we show that certain properties of this analysis extend to a Gaussian channel model. In particular, we show that for a symmetric channel, the symmetric sum-rate point is always achieved as an approximate equilibrium.

Orthogonal frequency division multiplexing (OFDM) with dynamic scheduling and resource allocation is a key component of most emerging broadband wireless access networks such as WiMAX and LTE (long term evolution) for 3GPP. However, scheduling and resource allocation in an OFDM system is complicated, especially in the uplink due to two reasons: (i) the discrete nature of subchannel assignments, and (ii) the heterogeneity of the users' subchannel conditions, individual resource constraints and application requirements. We approach this problem using a gradient-based scheduling framework. Physical layer resources (bandwidth and power) are allocated to maximize the projection onto the gradient of a total system utility function which models application-layer Quality of Service (QoS). This is formulated as a convex optimization problem and solved using a dual decomposition approach. This optimal solution has prohibitively high computational complexity but reveals guiding principles that we use to generate lower complexity sub-optimal algorithms. We analyze the complexity and compare the performance of these algorithms via extensive simulations.

We consider scheduling and resource allocation for the downlink of a cellular OFDM system, with various practical considerations including integer tone allocations, different sub-channelization schemes, maximum SNR constraint per tone, and "self-noise" due to channel estimation errors and phase noise. During each time-slot a subset of users must be scheduled, and the available tones and transmission power must be allocated among them. Employing a gradient-based scheduling scheme presented in earlier papers reduces this to an optimization problem to be solved in each time-slot. Using a dual formulation, we give an optimal algorithm for this problem when multiple users can time-share each tone. We then give several low complexity heuristics that enforce integer tone allocations. Simulations are used to compare the performance of different algorithms.

Interference between nodes directly limits the capacity of mobile ad hoc networks. This paper focuses on spatial interference cancellation with perfect channel state information (CSI), and analyzes the corresponding network capacity. Specifically, by using multiple antennas, zero-forcing beamforming is applied at each receiver for canceling the strongest interferers. Given spatial interference cancellation, the network transmission capacity is analyzed in this paper, which is defined as the maximum transmitting node density under constraints on outage and the signal-to-interference-plus-noise ratio. Assuming that the locations of network nodes are Poisson distributed and spatially i.i.d. Rayleigh fading channels, mathematical tools from stochastic geometry are applied for deriving scaling laws for transmission capacity. Specifically, for a large number of antennas per node, the transmission capacity scales with the number of antennas raised to a fractional power, which depends only on the path-loss exponent. Moreover, for small target outage probability, transmission capacity is proved to increase following a power law, where the exponent is the inverse of the size of antenna array or larger depending on the pass-loss exponent. As shown by simulations, spatial interference cancellation increases transmission capacity by an order of magnitude or more even if only one extra antenna is added to each node.

We provide a natural formulation of information theoretic games on interference channels. We analyze this game on a class of deterministic interference channels recently introduced to approximate Gaussian channels in the interference-limited regime. Our main result is a complete and simple characterization of the subset of the interference channel capacity region that can be achieved as Nash equilibria. We show that for all parameter values of the interference channel, there are always Nash equilibria which are efficient, i.e. on the boundary of the capacity region.

We consider optimizing the packet-sizes and the reuse factor to minimize the delay required to send a message between two nodes in a linear multi-hop wireless network subject to a reliability constraint. Initially assuming no re-use, we give a bound on the required delay. Next, in an infinite system with re-use, we analyze the rate of growth of the delay as a function of the message size. Two cases are considered: one in which packets are decoded/re-encoded on each hop and one in which this is concatenated with an end-to-end outer code. The later is shown to result in lower delays.

We consider a pair of nodes who wish to communicate with each other via intermediate relays. In this bi-directional network with stochastic flows, we develop the throughput optimal control policy, i.e., a policy that stabilizes the network whenever the arrival rates are within the region established. We investigate the effect of implementing different practical transmission protocols and network coding. The network control policies we present offer diverse possibilities in relaying and cooperation structure depending on the channel and the queue states.

A multihop relay extension for IEEE 802.16e systems is the subject of ongoing standardization activities within the IEEE 802.16j Task Group. The emerging IEEE 802.16J standard enhances the 802.16e PHY and MAC to enable support of multihop routes between a mobile station and a base station through intermediate relay stations. Since it is believed that the capacity of a single-hop 802.16e system is uplink-limited, this paper evaluates potential capacity gains attained with the relay enhancement of the 802.16e uplink. The capacity here denotes either cumulative throughput for data traffic or total number of users for voice traffic supported, under certain system-specific constrains detailed below. We first develop a simplified one-dimensional model of a relay-enhanced 802.16e system and estimate the capacity gains via analysis and numerical optimization. Motivated by the capacity gains predicted by this first-order analysis, simulation results obtained from a full two-dimensional simulator modeling a realistic deployment of a relay-enhanced system are then presented. Based on the simulation results, a parametric analysis of relay deployment cost vs. the capacity gain is also presented.

We study a sequential auction for sharing a wireless resource (bandwidth or power) among competing transmitters. The resource is assumed to be managed by a spectrum broker (auctioneer), who collects bids and allocates discrete units of the resource via a sequential second-price auction. It is well known that a second price auction for a single indivisible good has an efficient dominant strategy equilibrium; this is no longer the case when multiple units of a homogeneous good are sold in repeated iterations. For two users with full information, we show that such an auction has a unique equilibrium allocation. The worst-case efficiency of this allocation is characterized under the following cases: (i) both bidders have a concave valuation for the spectrum resource, and (ii) one bidder has a concave valuation and the other bidder has a convex valuation (e.g., for the other useriquests power). Although the worst-case efficiency loss can be significant, numerical results are presented, which show that for randomly placed transmitter-receiver pairs with rate utility functions, the sequential second-price auction typically achieves the efficient allocation. For more than two users it is shown that this mechanism always has a pure strategy equilibrium, but in general there may be multiple equilibria. We give a constructive procedure for finding one equilibrium; numerical results show that when all users have concave valuations the efficiency loss decreases with an increase in the number of users.

In a downlink orthogonal frequency division multiple access (OFDMA) system, optimally allocating sub-channels across mobile users can require excessive feedback of channel state information (CSI). We consider an OFDMA model in which the feedback overhead is explicitly taken into account, given a fixed feedback rate and finite coherence time. The tradeoff between feedback rate and sum capacity is studied for two limited feedback schemes: a sequential scheme in which the users send compressed feedback bits over consecutive time slots, and a contention scheme in which users send their feedback via a random access protocol. For both schemes each feedback bit indicates a request for a group containing multiple subchannels. We show that the sum capacity for both schemes with optimized sub-channel groups grows linearly with the number of sub-channels N, and that the associated constant increases as the log of the normalized feedback rate measured in bits per coherence time per sub-channel. We also compare the asymptotic (large N) performance of the two limited feedback schemes as a function of the feedback rate and load (users per sub-channel). The sequential scheme performs best with moderate to large feedback rates, or small loads, whereas the contention scheme performs best with small feedback rates or large loads.

Demand for multimedia services, such as video streaming over wireless networks, has grown dramatically in recent years. The downlink transmission of multiple video sequences to multiple users over a shared resource-limited wireless channel, however, is a daunting task. Among the many challenges in this area are the time-varying channel conditions, limited available resources, such as bandwidth and power, and the different transmission requirements of different video content. This work takes into account the time-varying nature of the wireless channels, as well as the importance of individual video packets, to develop a cross-layer resource allocation and packet scheduling scheme for multiuser video streaming over lossy wireless packet access networks. Assuming that accurate channel feedback is not available at the scheduler, random channel losses combined with complex error concealment at the receiver make it impossible for the scheduler to determine the actual distortion of the sequence at the receiver. Therefore, the objective of the optimization is to minimize the expected distortion of the received sequence, where the expectation is calculated at the scheduler with respect to the packet loss probability in the channel. The expected distortion is used to order the packets in the transmission queue of each user, and then gradients of the expected distortion are used to efficiently allocate resources across users. Simulations show that the proposed scheme performs significantly better than a conventional content-independent scheme for video transmission.

Wireless video transmission is prone to unpredictable degradations due to time-varying channel conditions. Such degradations are difficult to overcome using conventional video coding techniques. Scalable video coding offers a flexible bitstream that can be dynamically adapted to fit the prevailing channel conditions. Within a scalable video coding framework, we develop simple packet prioritization strategies, which, when combined with a reasonable error concealment scheme and a content-aware resource allocation technique, provide for robust video transmission over time-varying channels. The packet prioritization as well as the calculation of the content-aware scheduling metric can be performed offline and signaled to the wireless scheduler.

The emergence of 3G and 4G wireless networks brings with it the possibility of streaming high quality video content on-demand to mobile users. Wireless video applications require appropriate scheduling techniques that make use of the specific characteristics of video content, as well as the well known gains from multiuser diversity. While fast and frequent channel feedback is available in the new generation of wireless networks, the channel estimates cannot be perfect, and channel losses should be taken into account in the packet scheduling and resource allocation. The proposed scheme is formulated as a joint optimization over the resource allocation and channel loss protection, in order to minimize the distortion of the received video sequences. The distortion is a function of the packets deliberately dropped at the transmission queue due to congestion, as well as of random channel losses. The scheme makes use of a packet prioritization strategy that orders video packets based on their contribution to reducing the expected distortion of the received video sequence. Simulation results show that the proposed technique significantly outperforms content-independent packet scheduling schemes.

We consider the capacity of a downlink orthogonal frequency division multiple access (OFDMA) system with limited feedback rate RFper sub-channel and finite coherence time T. The feedback is used to relay channel state information (CSI) from K users to the base station. The order-optimal capacity growth with Rayleigh fading sub-channels is ominus(N log log K) as N and K increase with fixed ratio, where N is the number of sub-channels. However, to achieve this, previous work requires a feedback rate per subchannel that scales linearly with the system size. Here we explicitly include the feedback overhead when calculating the sum capacity, and study the tradeoff between feedback rate and sum capacity. We propose two limited feedback schemes, one based on sequential transmissions across users and the other based on random access, in which the each feedback bit requests the use of a sub-channel group containing multiple subchannels. With fixed RFT, the sum capacity for both schemes with optimized sub-channel groups increases as ominus(N). If RFT grows faster than log K, then both schemes can achieve the order- optimal capacity growth. We also show that when RFT is small, the random access scheme performs better than the sequential transmission scheme, whereas the reverse is true for large RFT.

We consider cooperative relay networks with multiple stochastically varying end-to-end flows. For such networks, we study throughput optimal network control policies which stabilize the network's queues for any arrival rate in its stability region. In earlier work, we have developed such a policy for a simple four-node parallel relay network. In this paper, we show that this policy can be generalized to a much larger class of cooperative relay networks with various types of two-hop "cooperative links".

A cross-layer packet scheduling scheme that streams pre-encoded video over wireless downlink packet access networks to multiple users is presented. The scheme can be used with the emerging wireless standards such as HSDPA and IEEE 802.16. A gradient based scheduling scheme is used in which user data rates are dynamically adjusted based on channel quality as well as the gradients of a utility function. The user utilities are designed as a function of the distortion of the received video. This enables distortion-aware packet scheduling both within and across multiple users. The utility takes into account decoder error concealment, an important component in deciding the received quality of the video. We consider both simple and complex error concealment techniques. Simulation results show that the gradient based scheduling framework combined with the content-aware utility functions provides a viable method for downlink packet scheduling as it can significantly outperform current content-independent techniques. Further tests determine the sensitivity of the system to the initial video encoding schemes, as well as to non-real-time packet ordering techniques.

The thirty-four papers in this special issue are devoted to models, theories, and codes for relaying and cooperation in communication networks. The demand for large, more efficient, reliable, and cost effective communication networks is motivating new network architectures for cellular and wireless communications as well as cognitive radio and sensor networks.

In cooperative relaying, multiple nodes cooperate to forward a packet within a network. To date, such schemes have been primarily investigated at the physical layer with the focus on communication of a single end-to-end flow. This paper considers cooperative relay networks with multiple stochastically varying flows, which may be queued within the network. Throughput optimal network control policies are studied that take into account queue dynamics to jointly optimize routing, scheduling and resource allocation. To this end, a generalization of the maximum differential backlog algorithm is given, which takes into account the cooperative gains in the network. Several structural characteristics of this policy are discussed for the special case of parallel relay networks.

Radiological imaging is a rapidly growing business. The field is quickly evolving from films to electronic or digital imaging. By the year 2015, the amount of radiological image data that will have been generated in the U.S. alone is projected to be between 100 and 300 000 PB. (1 PB equals 2(50) B or about 10(15) B.) As the volume of radiological data increases, the need to transmit the data over long distances also increases. For example, radiologists in Chicago, India, or Israel, working in the same medical practice, may read images taken in Chicago. Globally distributed research requires that images be transmitted around the world. Radiologists and researchers want to be able to download files containing hundreds of megabytes in seconds. This service requirement suggests that multiple copies of images should be retained in globally distributed databases to minimize access and transmission delays. Key design issues for such a database include the location of the data repositories relative to the generating and retrieval (reading) sites and the number and location of the copies of the files that are generated. In this paper, we approximate the time to retrieve images stored in city j by a radiologist in city k at time period t. Next, we formulate a model designed to minimize the average retrieval time weighted over all demands. We briefly outline a nested Lagrangian relaxation approach to the problem. Computational results are then summarized. The paper ends with conclusion and directions for future research.

We consider the allocation of power across forward-link packets in a wireless data network. The packets arrive according to a random (Poisson) process, and have fixed length so that the data rate for a given packet is determined by the assigned power and the channel gain to the designated user. Each user's service preferences are specified by a utility function that depends on the received data rate. The objective is to determine a power assignment policy that maximizes the time-averaged utility rate, subject to a constraint on the probability that the total power exceeds a limit (corresponding to an outage). For a large, heavily loaded network, we introduce a Gaussian approximation for the total transmitted power, which is used to decompose the power constraint into three more tractable constraints. We present a solution to the modified optimization problem that is a combination of admission control and pricing. The optimal trade-off between these approaches is characterized. Numerical examples illustrate the achievable utility rate and power allocation as a function of the packet arrival rate.

We study the downlink coverage of a base station terminal (BST), which has access to a relay node. Continuing a previous study in which the BST is assumed to provide a variable-rate data service, here we assume that each active user requires a target data rate, corresponding to a voice type of service. The relay is assumed to serve a separate set of (non- cellular) users, corresponding to a WiFi Access Point (AP). A one-dimensional model is considered in which cellular and non- cellular users are uniformly distributed along a line. The BST and AP jointly allocate available power across users and the BST-AP link to maximize the total number of users served. We characterize the optimized set of active cellular users served by the BST directly and the AP relay, and the non-cellular users served by the AP. We also give a closed-form upper bound on the increase in the total number of users provided by the relay as a function of user densities and path loss exponents. Our results show that depending on the distance between the BST and the AP, the addition of a relay gives a modest increase in the total number of active users.

The coverage of a base station terminal (BST) in a cellular network can generally be increased through the use of a relay node within the cell boundary. We consider the downlink for a single, one-dimensional cell with a relay node, or access point (AP), which serves a separate set of (non-cellular) users (e.g., corresponding to a WiFi system). The BST and AP jointly allocate available power across users and the BST-AP link to maximize the sum data rate across all cellular and AP users. Two relay schemes are considered: (i) the information flows to the cellular users served by the relay are jointly encoded and transmitted from the BST to the AP; and (ii) the preceding information flows are transmitted in parallel from the BST to the AP. We give an upper bound on the increase in rate provided by the AP, which depends on the relative powers and bandwidths available to the BST and AP. Although the increase in total rate provided by sharing AP resources is typically modest, it can provide a more equitable rate distribution across cellular users, and extend the coverage of the BST.

We consider allocation of sub-channels to users in a downlink OFDMA system. Each user feeds back one bit per sub-channel, which indicates whether or not the gain exceeds a threshold. Users are assigned priority weights, and the thresholds are selected to maximize the weighted sum capacity. We analyze the behavior of the optimal thresholds and growth in capacity, assuming i.i.d. Rayleigh fading sub-channels, in the large system limit in which users K and sub-channels tend to infinity with fixed ratio. If all users have the same priority weight, then the optimized threshold increases as log K minus a second-order term, which is asymptotically bounded between log log K and log log log K. Furthermore, the sum capacity per sub-channel increases as log log K plus a second-order term, which decreases to a constant as log log K/ log K. We then consider two classes of users, each assigned a different weight, and show that the capacity of the low priority group tends to zero. Finally, we solve for the optimal thresholds given a fairness constraint on the ratio between the rates of different classes

In wireless networks, it is well established that the throughput can be increased by opportunistically scheduling transmissions to users that have good channel conditions. Several “opportunistic” medium access control protocols have been developed, which enable distributed users to opportunistically transmit without requiring a centralized scheduler. In this paper, we consider opportunistic splitting algorithms, where a sequence of mini-slots is used to determine the appropriate user to schedule at each time. In prior work, this type of algorithm has been developed for homogeneous systems in which all users have independent and identically distributed (i.i.d.) channel statistics. Here, we specify new splitting algorithms for a heterogeneous environment that may also include fairness constraints. The performance of the splitting algorithms are characterized via analysis and simulations. In particular, we show that in certain cases, a heterogeneous algorithm will perform at least as well as the homogeneous algorithm in a system with the same total number of users.

We consider a distributed power control scheme for wireless ad hoc networks, in which each user announces a price that reflects compensation paid by other users for their interference. We present an asynchronous distributed algorithm for updating power levels and prices. By relating this algorithm to myopic best response updates in a fictitious game, we are able to characterize convergence using supermodular game theory. Extensions of this algorithm to a multichannel network are also presented, in which users can allocate their power across multiple frequency bands.

Characterizing the video quality seen by an end-user is a critical component of any video transmission system. In packet-based communication systems, such as wireless channels or the Internet, packet delivery is not guaranteed. Therefore, from the point-of-view of the transmitter, the distortion at the receiver is a random variable. Traditional approaches have primarily focused on minimizing the expected value of the end-to-end distortion. This paper explores the benefits of accounting for not only the mean, but also the variance of the end-to-end distortion when allocating limited source and channel resources. By accounting for the variance of the distortion, the proposed approach increases the reliability of the system by making it more likely that what the end-user sees, closely resembles the mean end-to-end distortion calculated at the transmitter. Experimental results demonstrate that variance-aware resource allocation can help limit error propagation and is more robust to channel-mismatch than approaches whose goal is to strictly minimize the expected distortion.

The problem of application-layer error control for real-time video transmission over packet lossy networks is commonly addressed via joint source-channel coding (JSCC), where source coding and forward error correction (FEC) are jointly designed to compensate for packet losses. In this paper, we consider hybrid application-layer error correction consisting of FEC and retransmissions. The study is carried out in an integrated joint source-channel coding (IJSCC) framework, where error resilient source coding, channel coding, and error concealment are jointly considered in order to achieve the best video delivery quality. We first show the advantage of the proposed IJSCC framework as compared to a sequential JSCC approach, where error resilient source coding and channel coding are not fully integrated. In the USCC framework, we also study the performance of different error control scenarios, such as pure FEC, pure retransmission, and their combination. Pure FEC and application layer retransmissions are shown to each achieve optimal results depending on the packet loss rates and the round-trip time. A hybrid of FEC and retransmissions is shown to outperform each component individually due to its greater flexibility.

In this paper, we consider packet scheduling for the downlink in a wireless network, where each packet's service preferences are captured by a utility function that depends on the total delay incurred. The goal is to schedule packet transmissions to maximize the total utility. In this setting, we examine a simple gradient-based scheduling algorithm called the U/spl dot/R-rule, which is a type of generalized c/spl mu/-rule (Gc/spl mu/) that takes into account both a user's channel condition and derived utility when making scheduling decisions. We study the performance of this scheduling rule for a draining problem, where there is a given set of initial packets and no further arrivals. We formulate a "large system" fluid model for this draining problem where the number of packets becomes large while the packet-size decreases to zero, and give a complete characterization of the behavior of the U/spl dot/R scheduling rule in this limiting regime. Comparison with simulation results show that the fluid limit accurately predicts the corresponding behavior of finite systems of interest. We then give an optimal control formulation for finding the optimal scheduling policy for the fluid draining model. Using Pontryagin's minimum principle, we show that, when the user rates are chosen from a TDM-type of capacity region, the U/spl dot/R rule is in fact optimal in many cases. Sufficient conditions for optimality are also given. Finally, we consider a general capacity region and show that the U/spl dot/R rule is optimal only in special cases.

In wireless fading channels, multiuser diversity can be exploited by scheduling users to transmit when their channel conditions are favorable. This leads to a sum throughput that increases with the number of users and, in certain cases, achieves capacity. However, such scheduling requires global knowledge of every user's channel gain, which may be difficult to obtain in some situations. This paper addresses contention-based protocols for exploiting multiuser diversity with only local channel knowledge. A variation of the ALOHA protocol is given in which users attempt to exploit multiuser diversity gains, but suffer contention losses due to the distributed channel knowledge. The growth rate of the sum throughput for this protocol is characterized in a backlogged system under both short-term and long-term average power constraints. A simple "fixed-rate" system is shown to be asymptotically optimal and to achieve the same growth rate as in a system with an optimal centralized scheduler. Moreover, asymptotically, the fraction of throughput lost due to contention is shown to be 1/e. Also, in a system with random arrivals and an infinite user population, a variation of this ALOHA protocol is shown to be stable for any total arrival rate, given that users can estimate the backlog.

We consider energy-efficient resource allocation for wireless fading channels. We study the case where a sliding window ARQ protocol such as Go-Back-N is used to provide reliable communication. In particular, we consider power allocation policies that take into account the underlying window dynamics. An optimal dynamic programming approach and a sub-optimal approach based on renewal theory are given. Numerical results comparing these approaches are also presented.

We consider a distributed power control scheme in a spread spectrum (SS) wireless ad hoc network, in which each user announces a price that reflects his current interference level. Given these prices, we present an asynchronous distributed algorithm for updating power levels, and provide conditions under which this algorithm converges to an optimal power allocation. We relate this algorithm to myopic best response updates of a fictitious game, and characterize the algorithm's convergence using supermodular game theory

We give a model for cooperative communication in a parallel relay network that includes the stochastic arrival of packets and queueing. Exogenous arrivals at both the non-relay and the relay nodes are allowed. For this model, we provide a throughput optimal network control policy which stabilizes the network for any vector of arrival rates in its stability region. This policy generalizes the maximum differential backlog policies, taking into account potential cooperative gains in the network. Some structural properties of this policy are also discussed

In this paper, we develop distributed approaches for power allocation and scheduling in wireless access networks. We consider a model where users communicate over a set of parallel multi-access fading channels, as in an OFDM or multi-carrier system. At each time, each user must decide which channels to transmit on and how to allocate its power over these channels. We give distributed power allocation and scheduling policies where each user's actions depend only on knowledge of their own channel gains. We characterize an optimal policy which maximizes the system throughput and also give a simpler sub-optimal policy which is shown to have the optimal scaling behavior in several asymptotic regimes.

In this paper, we study the benefits of using tunable transceivers for reducing the required number of electronic ports in wavelength-division-multiplexing/time-division multiplexing optical networks. We show that such transceivers can be used to efficiently "groom" subwavelength traffic in the optical domain and so can significantly reduce the amount of terminal equipment needed compared with the fixed-tuned case. Formulations for this "tunable grooming" problem are provided, where the objective is to schedule transceivers so as to minimize the required number of ports needed for a given traffic demand. We establish a relationship between this problem and edge colorings of graphs which are determined by the offered traffic. Using this relationship, we show that, in general, this problem is NP-complete, but we are able to efficiently solve it for many cases of interest. When the number of wavelengths in the network is not limited, each node is shown to only require the minimum number of transceivers (i.e., no more transceivers than the amount of traffic that it generates). This holds regardless of the network topology or traffic pattern. When the number of wavelengths is limited, an analogous result is shown for both uniform and hub traffic in a ring. We then develop a heuristic algorithm for general traffic that uses nearly the minimum number of transceivers. In most cases, tunable transceivers are shown to reduce the number of ports per node by as much as 60%. We also consider the case where traffic can dynamically change among an allowable set of traffic demands. Tunability is again shown to significantly reduce the port requirement for a nonblocking ring, both with and without rearrangements.

Multimedia applications involving the transmission of video over communication networks are rapidly increasing in popularity. Such applications can greatly benefit from adapting video coding parameters to network conditions as well as adapting network parameters to better support the application requirements. These two dimensions can both be viewed as allocating source and network resources to improve video quality. We highlight recent advances in optimal resource allocation for real-time video communications over unreliable and resource constrained communication channels. More specifically, we focus on point-to-point coding and delivery schemes in which the sequences are encoded on the fly. We present a high-level framework for resource-distortion optimization. The framework can be used for jointly considering factors across network layers, including source coding, channel resource allocation, and error concealment. For example, resources can take the form of transmission energy in a wireless channel, and transmission cost in a DiffServ-based Internet channel. This framework can be used to optimally trade off resource consumption with end-to-end video quality in packet-based video transmission. After giving an overview of this framework, we review recent work in two areas-energy efficient wireless video transmission and resource allocation for Internet-based applications.

Differentiated Services (DiffServ) is one of the leading architectures for providing quality of service in the Internet. We propose a scheme for real-time video transmission over a DiffServ network that jointly considers video source coding, packet classification, and error concealment within a framework of cost-distortion optimization. The selections of encoding parameters and packet classification are both used to manage end-to-end delay variations and packet losses within the network. We present two dual formulations of the proposed scheme: the minimum distortion problem, in which the objective is to minimize the end-to-end distortion subject to cost and delay constraints, and the minimum cost problem, which minimizes the total cost subject to end-to-end distortion and delay constraints. A solution to these problems using Lagrangian relaxation and dynamic programming is given. Simulation results demonstrate the advantage of jointly adapting the source coding and packet classification in DiffServ networks.

A model for downlink wireless scheduling is studied, which takes into account both user-channel conditions and retransmissions with packet combining hybrid [automatic repeat request (ARQ)]. Quality-of-service (QoS) requirements for each user are represented by a cost function, which is an increasing function of queue length. The objective is to find a scheduling rule that minimizes the average cost over time. We consider two scenarios: 1) the cost functions are linear, and packets arrive to the queues according to a Poisson process and 2) the cost functions are increasing, convex, and there are no new arrivals (draining problem). In each case, we transform the system model into a different model that fits into a framework for stochastic scheduling developed by Klimov. Applying Klimov's results, we show that the optimal schedulers for the transformed models in both scenarios are specified by fixed priority rules. Applying the inverse transformation in each case gives the optimal scheduling policy for the original problem. The priorities can be explicitly computed, and in the first scenario, are given by simple closed-form expressions. For the draining problem, we show that the optimal policy never interrupts the retransmissions of a packet. We also show that a simple myopic scheduling policy, called the U'R rule, performs very close to the optimal scheduling policy in specific cases. We present numerical examples, which compare the performance of the optimal scheduling rule with several heuristic rules.

The problem of encoding and transmitting a video sequence over an IP-based wireless network, consisting of both wired and wireless links, is addressed. To combat the different types of packet loss in the heterogeneous network, the use of a product code forward error correction (FEC) scheme capable of providing unequal error protection is considered. At the transport layer, Reed-Solomon (RS) coding is used to provide inter-packet protection. In addition, rate-compatible punctured convolutional (RCPC) coding is used at the link layer to provide unequal intra-packet protection. Optimal bit allocation is performed in a rate-distortion optimized joint source-channel coding and power allocation framework to achieve the best video quality. Simulation results illustrate the advantage of the proposed product code FEC scheme over previously studied approaches.

In this paper, hybrid error control for real-time video transmission is studied. The study is carried out using a proposed integrated joint source-channel coding framework, which jointly considers error resilient source coding, channel coding, and error concealment, in order to achieve the best video quality and focuses on the performance comparison of several error correction scenarios, such as forward error correction (FEC), retransmission, and the combination of both. Simulation results show that either FEC or retransmission can be optimal depending on the packet loss rates and network round trip time. The proposed hybrid FEC/retransmission scheme outperforms both.

A major limitation faced by a mobile user is their dependence on a limited battery supply. For wireless video communications, joint source coding and transmission power management (JSCPM) has recently been considered as a means of efficiently allocating transmission energy. In order to reduce complexity, the design of many of these adaptive resource allocation algorithms utilizes simplified channel models that do not account for the burstiness of the channel. We analyze the effects of such channel model simplifications on the end-to-end distortion. We present a channel model that is based on information theoretic considerations, which captures the bursty nature of wireless channels and accounts for packet lengths when calculating the probability of loss. Given the source coding and transmission parameters derived using a simplified channel model, our goal is to analyze how the end-to-end distortion is affected when a more realistic complex channel model is used to simulate losses. Experimental results suggest that the performance gain predictions for JSCPM using a simpler channel model are also valid when more sophisticated channel simulations are used, provided that a number of additional steps are taken after the optimization to account for the complex characteristics of wireless channels.

The problem of application-layer error control for real-time video transmission over packet lossy networks is commonly addressed by joint source-channel coding (JSCC). The traditional JSCC approaches solve this problem in a sequential manner, where source coding and channel coding are not fully integrated. In this paper, we present an integrated joint source-channel coding (IJSCC) framework, where error resilient source coding, channel coding and error concealment are jointly considered in an integrated manner. We show through both analysis and simulations the advantages of the proposed IJSCC approach, in comparison to a sequential JSCC approach.

We study the benefits of using tunable transceivers for reducing the required number of electronic ports in WDM/TDM networks. We show that such transceivers can be used to efficiently "groom" sub-wavelength traffic in the optical domain and so can significantly reduce the number of electronic ports compared to the fixed tuned case. We provide a new formulation for this "tunable grooming" problem. We show that in general this problem is NP-complete, but we are able to efficiently solve it for many cases of interest. When the number of wavelengths in the network is not limited, we show that each node only needs the minimum number of transceivers (i.e., no more transceivers than the amount of traffic that it generates). This holds regardless of the network topology or traffic pattern. When the number of wavelengths is limited, we show an analogous result for both uniform and hub traffic in a ring. We also develop a heuristic algorithm for general traffic that uses nearly the minimum number of transceivers. In most cases, tunable transceivers are shown to reduce the number of ports per node by as much as 60%.

We develop medium access control protocols to enable users in a wireless network to opportunistically transmit when they have favorable channel conditions, without requiring a centralized scheduler. We consider approaches that use splitting algorithms to resolve collisions over a sequence of minislots, and determine the user with the best channel. First, we present a basic algorithm for a system with i.i.d. block fading and a fixed number of backlogged users. We give an analysis of the throughput of this system and show that the average number of minislots required to find the user with the best channel is less than 2.5 independent of the number of users or the fading distribution. We then extend this algorithm to a channel with memory and also develop a reservation based scheme that offers improved performance as the channel memory increases. Finally we consider a model with random arrivals and propose a modified algorithm for this case. Simulation results are given to illustrate the performance in each of these settings.

In a wireless system with stochastically arriving traffic, scheduling the amount of data transmitted at any time is a basic technique for improving the energy efficiency. A discrete-time model for transmission scheduling over a fading channel is considered in this paper. The arrived data is placed into a transmission buffer, once the data is removed after the buffer; it is transmitted over the fading channel. The channel is modeled as a block-fading channel with Gaussian noise. For this model the optimal trade-off between the average queueing delay and the long-term average power was characterized in the asymptotic regime of large delays. The average power decreases as the average delay increases at the optimal rate. This rate can be achieved by a sequence of policies on the buffer occupancy via a simple threshold rule.

We consider the role of switching in minimizing the number of electronic ports [e.g., synchronous optical network (SONET) add/drop multiplexers] in an optical network that carries subwavelength traffic. Providing nodes with the ability to switch traffic between wavelengths, such as through the use of SONET cross-connects, can reduce the required number of electronic ports. We show that only limited switching ability is needed for significant reductions in the number of ports. First, we consider architectures where certain "hub" nodes can switch traffic between wavelengths and other nodes have no switching capability. For such architectures, we provide a lower bound on the number of electronic ports that is a function of the number of hub nodes. We show that our lower bound is relatively tight by providing routing and grooming algorithms that nearly achieve the bound. For uniform traffic, we show that the number of electronic ports is nearly minimized when the number of hub nodes used is equal to the number of wavelengths of traffic generated by each node. Next, we consider architectures where the switching ability is distributed throughout the network. Such architectures are shown to require a similar number of ports as the hub architectures, but with a significantly smaller "switching cost." We give an algorithm for designing such architectures and characterize a class of topologies, where the minimum number of ports is used. Finally, we provide a general upper bound on the amount of switching required in the network. For uniform traffic, our bound shows that as the size of the network increases, each traffic stream must be switched at most once in order to achieve the minimum port count.

The problem of encoding and transmitting a video sequence over a wireless channel is considered. Our objective is to minimize the end-to-end distortion while using a limited amount of transmission energy and delay. In our approach, we jointly adapt the source-coding parameters and transmission power per packet. We introduce the concept of "variance-aware distortion estimation" (VADE), and present a framework for controlling both the expected value and the variance of the end-to-end distortion. This framework is based on knowledge of how the video is compressed, the probability of packet loss, and the concealment strategy. To the best of our knowledge, this paper is the first to address the trade-off between the mean and variance of the end-to-end distortion. Experimental results demonstrate the potential of the proposed approach.

This paper presents a novel framework for streaming video over a Differentiated Services (DiffServ) network that jointly considers video source coding, packet classification and error concealment within the scope of cost-distortion optimization. Our formulation incorporates the random network delay for each packet into the calculation of the probability of packet loss and manages the end-to-end packet delay by selecting the encoding parameters and packet priority. We formulate two approaches to evaluate the performance of the proposed framework: a minimum distortion approach and a minimum cost approach, in which the encoding mode and priority class for each packet are optimally selected so as to minimize the total distortion subject to cost constraints, or to minimize the total cost subject to end-to-end distortion constraints. Simulation results demonstrate the advantage of jointly adapting the source coding and packet classification.

Video streaming over the Internet is a challenging task due, in part to the wide range of bandwidth variations caused by network congestion. To deal with this challenge, we propose an optimal error control scheme for scalable video transmission over the Internet. The three major components of error controlerror resilience, forward error correction (FEC), and error concealment- are considered in the proposed framework. Rate-distortion (R-D) optimization is carried out to determine the encoding mode for each packet and the channel coding rates, in order to minimize the overall expected end-to-end distortion. Our simulation study demonstrates that the proposed approach is robust to the wide range channel bandwidth variations and greatly outperforms the classical R-D optimization scheme.

Multiuser diversity refers to a type of diversity present across different users in a fading environment. This diversity can be exploited by scheduling transmissions so that users transmit when their channel conditions are favorable. Using such an approach leads to a system capacity that increases with the number of users. However, such scheduling requires centralized control. In this paper, we consider a decentralized medium access control (MAC) protocol, where each user only has knowledge of its own channel gain. We consider a variation of the ALOHA protocol, channel-aware ALOHA; using this protocol we show that users can still exploit multiuser diversity gains. First we consider a backlogged model, where each user always has packets to send. In this case we show that the total system throughput increases at the same rate as in a system with a centralized scheduler. Asymptotically, the fraction of throughput lost due to the random access protocol is shown to be 1/e. We also consider a splitting algorithm, where the splitting sequence depends on the users' channel gains; this algorithm is shown to approach the throughput of an optimal centralized scheme. Next we consider a system with an infinite user population and random arrivals. In this case, it is proved that a variation of channel-aware ALOHA is stable for any total arrival rate in a memoryless channel, given that users can estimate the backlog. Extensions for channels with memory are also discussed.

We consider "opportunistic" downlink scheduling of data traffic in a wireless network. In particular, we focus on the delay performance of such schedulers. First a channel-dependent scheduling algorithm is considered that maximizes throughput by always transmitting to the user with the best channel conditions. The delay distribution of this scheduling rule is analyzed and asymptotic results are given when the number of competing users becomes large. Simulations show these asymptotic results are a good approximation for even a small number of users. This scheduling rule may result in unfair treatment of users that have relative bad channels for a long period of time; to remedy this we propose a simple utility-based scheduling algorithm. The motivation is to maximize the time-averaged utility, where utility is a decreasing function of the delay incurred when serving a request. The scheduling algorithm takes into account both the utility function and the channel state. We give simulation results that characterize the performance of the scheduling algorithm. The effect of the temporal correlation of the channel of the performance is also studied.

Resource allocation is studied for a forward-link two-cell code division multiple access (CDMA) voice network with multiple service classes. System resources are transmitted power and codes. The service classes are specified by different user utility functions that relate utility to received signal-to-interference-plus-noise-ratio (SINR). The objective of the resource allocation is to maximize total utility over the two cells. The optimal power allocation is characterized by a set of distances, or radii, from the desired base station. Each radius corresponds to the set of active users in a particular service class, and can be enforced through a pricing scheme. We also consider setting prices to maximize revenue. In general, the prizes and power allocation that maximize revenue differ from those that maximize utility.

Rapid growth in wireless networks is fueling demand for video services from mobile users. While the problem of transmitting video over unreliable channels has received some attention, the wireless network environment poses challenges such as transmission power management that have received little attention previously in connection with video. Transmission power management affects battery life in mobile devices, interference to other users, and network capacity. We consider energy efficient transmission of a video sequence under delay and quality constraints. The selection of source coding parameters is considered jointly with transmitter power and rate adaptation, and packet transmission scheduling. The goal is to transmit a video frame using the minimal required transmission energy under delay and quality constraints. Experimental results are presented that illustrate the advantages of the proposed approach.

We consider forward-link power allocation in a wireless network with stochastically varying data requests. We assume a user's service preferences are specified via a utility function that depends on the received data rate. The allocation of power across users is studied, where this allocation may depend on both a user's channel and utility. The objective is to maximize the time-averaged utility rate subject to a stochastic total power constraint at the transmitter. For a large, heavily loaded network, we introduce a Gaussian approximation for the total transmitted power, which is used to decompose the power constraint into three more tractable constraints. We present a solution to this problem that is a combination of admission control and pricing of power. The optimal trade-off between these approaches is characterized. Numerical examples are given to illustrate these ideas.

Power allocation across users in two adjacent cells is studied for a wireless code division multiple access (CDMA) network with mixed voice and data services. We assume that each user has a utility function that measures the user's satisfaction, or utility, as a function of the received signal-to-interference-plus-noise ratio (SINR). Each particular service (voice or data) is associated with a different utility function. We consider the forward link. Our objective is to allocate transmitted power to maximize the total utility summed over all active users subject to rate and power constraints. We show that the maximum utility can be achieved with a pricing scheme. We characterize the solution to a one-cell utility maximization problem with fixed interference from the other cell. For two-cell utility maximization, the two cells must cooperate to achieve the maximum utility.

In the future, digital set-top boxes may serve as the primary access point for wireless home networks, enabling mobile users to use videoconferencing as well as streaming applications on hand-held devices. In this scenario, an important issue that must be addressed is the limited energy supply of a mobile device. This is of course a relevant issue for any wireless device. We focus on methods for efficiently utilizing transmission energy in wireless video communications. We present a general framework for the problem of minimizing the transmission energy required to provide an acceptable level of video quality. We discuss two special cases in which communication resources are adjusted simultaneously with the source coding parameters in order to provide (i) packet loss adaptation and (ii) transmission rate adaptation.

We consider the problem of compressing a video sequence for transmission over a wireless channel. In our approach we jointly consider error resilience and concealment techniques, at the source coding level, and transmission power management at the physical layer. We formulate a minimum-maximum distortion problem, where our goal is to either (i) minimize the total transmission energy for a given maximum expected distortion, or (ii) minimize the maximum expected distortion at the receiver for a given maximum transmission energy. Experimental results show that simultaneously adjusting the source coding and transmission power is more energy efficient than considering these factors separately.

This paper considers resource allocation and pricing for the downlink of a wireless network. We describe a model that applies to either a time-slotted system (e.g. Qualcomm's HDR proposal) or a CDMA system; the main feature of this model is that the channel quality varies across the users. We study using a pricing scheme for the allocation of radio resources. We show that to maximize revenue in such a system, the base station should allocate resources in a discriminatory manner, where different users are charged different prices based in part on their channel quality. However, optimally allocating resources in this way is shown to require knowledge about each user's utility function. We consider a suboptimal scheme which does not require knowledge of the users' utility functions, and show that this scheme is asymptotically optimal, in the limit of large demand. Moreover, such a scheme is shown to maximize social welfare. We also consider a heuristic scheme for the case of small demand, which does not require perfect knowledge about the users' utility functions. We provide numerical results that illustrate the performance of this heuristic.

We consider a situation where a video sequence is to be compressed and transmitted over a wireless channel. Our goal is to limit the amount of distortion in the received video sequence, while minimizing transmission energy. To accomplish this goal, we consider error resilience and concealment techniques at the source coding level, and transmission power management at the physical layer. We jointly consider these approaches in a novel framework. In this setting, we formulate and solve an optimization problem that corresponds to minimizing the energy required to transmit video under distortion and delay constraints. Experimental results show that simultaneously adjusting the source coding and transmission power is more energy efficient than considering these factors separately.

We consider a user communicating over a fading channel with perfect channel state information. Data are assumed to arrive from some higher layer application and are stored in a buffer until transmitted. We study adapting the user's transmission rate and power based on the channel state information as well as the buffer occupancy; the objectives are to regulate both the long-term average transmission power and the average buffer delay incurred by the traffic. Two models for this situation are discussed; one corresponding to fixed-length/variable-rate codewords and one corresponding to variable-length codewords. The tradeoff between the average delay and the average transmission power required for reliable communication is analyzed. A dynamic programming formulation is given to find all Pareto optimal power/delay operating points. We then quantify the behavior of this tradeoff in the regime of asymptotically large delay. In this regime, we characterize simple buffer control policies which exhibit optimal characteristics. Connections to the delay-limited capacity and the expected capacity of fading channels are also discussed.

A key constraint in mobile communications is the reliance on a battery with a limited energy supply. Efficiently utilizing the available energy is therefore an important design consideration. We consider a situation where a video sequence is to be compressed and transmitted over a wireless channel. The goal is to limit the amount of distortion in the received video sequence while using the minimum required transmission energy. To accomplish this goal, we consider error resilience and concealment techniques, at the source coding level, as well as the dynamic allocation of physical layer communication resources. We consider these approaches jointly in a novel framework. We formulate an optimization problem that corresponds to minimizing the energy required to transmit a video frame with an acceptable level of distortion. We present methods for solving this problem and other extensions.

In this paper, we consider traffic grooming in WDM/SONET ring networks when the offered traffic is characterized by a set of traffic matrices. Our objective is to minimize the cost of electronic add/drop multiplexers (ADMs) in the network, while being able to support any offered traffic matrix in a rearrangeably nonblocking manner. We provide several methods for reducing the required number of ADMs for an arbitrary class of traffic matrices. We then consider the special case where the only restriction on the offered traffic is a constraint on the number of circuits a node may source at any given time. For this case, we provide a lower bound on the number of ADMs required and give conditions that a network must satisfy in order for it to support the desired set of traffic patterns. Circuit assignment and ADM placement algorithms with performance close to this lower bound are provided. These algorithms are shown to reduce the electronic costs of a network by up to 27%. Finally, we discuss extensions of this work for supporting dynamic traffic in a wide-sense or strict sense nonblocking manner as well as the benefits of using a hub node and tunable transceivers. Much of this work relies on showing that these grooming problems can often be formulated as standard combinatorial optimization problems.

In this paper we consider the circuit assignment algorithm for dynamic traffic in unidirectional WDM/SONET ring networks. Our objective is to minimize the cost of electronic add/drop multiplexers (ADMs) in the network, while being able to support any offered traffic matrix in a rearrangeably non-blocking manner. The only restriction on the offered traffic is a constraint on the number of circuits a node may source at any given time. We provide a lower bound on the number of ADMs required and give conditions that a network must satisfy in order for it to support the desired set of traffic patterns. Circuit assignment and ADM placement algorithms that perform closely to this lower bound are provided. These algorithms are shown to reduce the electronic costs of a network by over 30%. Finally, we discuss extensions of this work for supporting dynamic traffic in a wide-sense or strict sense non-blocking manner as well as the benefits of using a hub node and tunable transceivers.

Rate-based feedback congestion control has been proposed as a form of traffic management for available bit rate traffic in ATM networks. This paper discusses applying linear control theory to these algorithms. A congestion control scheme for simple networks is designed and analyzed using the tools of classical control theory. This allows an insight into the trade-offs in such schemes and suggests approaches to larger networks.