Mathematical optimization is fundamental to decision-making across diverse domains, from operations research to healthcare. Yet, translating real-world problems into optimization models remains a difficult task, often demanding specialized expertise. This paper approaches the problem of $\textit{autoformulation}$: the automated creation of solver-ready optimization models from natural language problem descriptions. We identify three core challenges of autoformulation: $\textit{(1)}$ the vast, problem-dependent hypothesis space, $\textit{(2)}$ efficient and diverse exploration of this space under uncertainty, and $\textit{(3)}$ evaluation of formulation correctness against problem description. To address these challenges, we present a novel method leveraging $\textit{Large Language Models}$ (LLMs) with $\textit{Monte-Carlo Tree Search}$, exploiting the hierarchical nature of optimization modeling to generate and systematically explore possible formulations. To enhance search efficiency, we introduce symbolic pruning to eliminate trivially equivalent search paths (branches), and employ LLM-based evaluation of partial formulations to guide search. Empirical analysis on linear and mixed-integer programming benchmarks demonstrates our method’s effectiveness, with significant performance gains from both LLM-based value estimation and symbolic pruning techniques.

This research studies transmit pre-processing designs using dual millimeter wave (mmWave) active arrays with multiple input multiple output (MIMO) software defined radio (SDR) platform for a low Earth orbit (LEO) satellite communication emulator. It involves the fast Doppler, line-ofsight (LOS) with small delay effect, and MIMO array interference effects. In this emulator, we propose spatial nulling and MIMO pre-processing cancellation techniques to eliminate the MIMO streams interference. First, differential training sequences are utilized to acquire and compensate for the LOS fast Doppler offset. Then, a maximum power beam scanning scheme via the phase shifters of mmWave arrays is employed to estimate the arrival angles between MIMO array transmitter and receiver stations. Next, the steering vectors of the desired and interference signals via these estimated angles are adopted by the phase-only optimization algorithms to calculate the spatial beamforming weights of the dual mmWave array, which can null the interference stream and retain the desired stream. After the spatial nulling processing with the interference mitigation, the residual MIMO interference still exists in the MIMO receiver. We further propose MIMO pre-processing filters using the least square method to cancel the residual MIMO interference. Finally, the measurement results of the dual mmWave array SDR platform with the fast Doppler and the over-the-air (OTA) scenarios confirm that the proposed joint spatial nulling and MIMO pre-cancellation techniques can eliminate the MIMO interference and provide the high-quality error vector magnitude (EVM) performance.

Large language models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized prompts to leverage the in-context learning capabilities of LLMs for recommendation purposes. More recent studies have utilized instruction tuning techniques to align LLMs with human preferences, promising more effective recommendations. However, existing methods suffer from several limitations. The full potential of LLMs is not fully elicited due to low-quality tuning data and the overlooked integration of conventional recommender signals. Furthermore, LLMs may generate inconsistent responses for different ranking tasks in the recommendation, potentially leading to unreliable results. In this article, we introduce Ranker for top- k Recommendations (RecRanker), tailored for instruction tuning LLMs to serve as the Ranker for top- k Recommendations. Specifically, we introduce importance-aware sampling, clustering-based sampling, and penalty for repetitive sampling for sampling high-quality, representative, and diverse training data. To enhance the prompt, we introduce a position shifting strategy to mitigate position bias and augment the prompt with auxiliary information from conventional recommendation models, thereby enriching the contextual understanding of the LLM. Subsequently, we utilize the sampled data to assemble an instruction-tuning dataset with the augmented prompts comprising three distinct ranking tasks: pointwise, pairwise, and listwise rankings. We further propose a hybrid ranking method to enhance the model performance by ensembling these ranking tasks. Our empirical evaluations demonstrate the effectiveness of our proposed RecRanker in both direct and sequential recommendation scenarios. 1

In this paper, an unsupervised deep learning-based framework based on dual-path model-driven auto-encoders (AE) is proposed for angle-of-arrivals (AoAs) estimation in massive MIMO systems. Specifically designed for AoA estimation, the proposed framework differs from the conventional AE in two aspects. Firstly, unlike conventional auto-encoders, our framework employs a dual-path neural network for the encoder, decoupling the estimated parameters and enabling independent updates of each paths. Secondly, the decoder has fixed weights that implement the signal propagation model, instead of learnable parameters. This knowledge-aware decoder ensures the output of meaningful physical parameters (i.e., AoAs) which is unattainable by conventional AEs. We also conduct a thorough analysis to characterize the multiple global optima and local optima of the estimation problem. This analysis inspires the design of a low-complexity two-phase training scheme and confirms the convergence of our proposed framework. Consequently, our framework addresses two key challenges in unsupervised learning: the lack of interpretability and the convergence to local optima. Extensive simulations validate our theoretical analysis and demonstrate the performance improvements of our proposed framework.

To address the communication bottleneck challenge in distributed learning, our work introduces a novel two-stage quantization strategy designed to enhance the communication efficiency of distributed Stochastic Gradient Descent (SGD). The proposed method initially employs truncation to mitigate the impact of long-tail noise, followed by a non-uniform quantization of the post-truncation gradients based on their statistical characteristics. We provide a comprehensive convergence analysis of the quantized distributed SGD, establishing theoretical guarantees for its performance. Furthermore, by minimizing the convergence error, we derive optimal closed-form solutions for the truncation threshold and non-uniform quantization levels under given communication constraints. Both theoretical insights and extensive experimental evaluations demonstrate that our proposed algorithm outperforms existing quantization schemes, striking a superior balance between communication efficiency and convergence performance.

In this paper, we propose a highly accurate unsu-pervised deep learning framework based on auto-encoder (AE) for angle-of-arrival (AoA) estimation in massive MIMO systems. Our method builds on an improvement of the vanilla AE by incorporating the knowledge of signal propagation models into the decoder. In our proposed knowledge-guided AE (KG-AE), instead of having learnable parameters, the decoder has fixed weights that implement the signal propagation model. Such modification forces the encoder to output meaningful physical parameters of interest (i.e., AoA), which cannot be achieved by standard AE. Furthermore, we rigorously analyze the multiplicity of local optima in unsupervised channel estimation problems. Our analysis informs the design of the cost function and the training scheme for the proposed KG-AE. Specifically, we design a two-stage training scheme, different loss functions in the two stages to achieve good initial points and boost the performance of the proposed KG-AE, respectively. Finally, extensive simulations are performed, and the results corroborate the analysis and demonstrate the performance improvements of the proposed KG-AE over the subspace-based algorithms and the state-of-the-art unsupervised learning-based algorithm.

Federated recommendation systems employ federated learning techniques to safeguard user privacy by transmitting model parameters instead of raw user data between user devices and the central server. Nevertheless, the current federated recommender system faces three significant challenges: (1) data heterogeneity: the heterogeneity of users’ attributes and local data necessitates the acquisition of personalized models to improve the performance of federated recommendation; (2) model performance degradation: the privacy-preserving protocol design in the federated recommendation, such as pseudo item labeling and differential privacy, would deteriorate the model performance; (3) communication bottleneck: the standard federated recommendation algorithm can have a high communication overhead. Previous studies have attempted to address these issues, but none have been able to solve them simultaneously. In this article, we propose a novel framework, named PerFedRec++ , to enhance the personalized federated recommendation with self-supervised pre-training. Specifically, we utilize the privacy-preserving mechanism of federated recommender systems to generate two augmented graph views, which are used as contrastive tasks in self-supervised graph learning to pre-train the model. Pre-training enhances the performance of federated models by improving the uniformity of representation learning. Also, by providing a better initial state for federated training, pre-training makes the overall training converge faster, thus alleviating the heavy communication burden. We then construct a collaborative graph to learn the client representation through a federated graph neural network. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Each user learns a personalized model by combining the global federated model, the cluster-level federated model, and its own fine-tuned local model. Experiments on three real-world datasets show that our proposed method achieves superior performance over existing methods.

The ubiquitous long-tail distribution of inherent user behaviors results in worse recommendation performance for the items with fewer user records (i.e., tail items) than those with richer ones (i.e., head items). Graph-based recommendation methods (e.g., using graph neural networks) have recently emerged as a powerful tool for recommender systems, often outperforming traditional methods. However, existing techniques for alleviating the long-tail problem mainly focus on traditional methods. There is a lack of graph-based methods that can efficiently deal with the long-tail problem.

Distributed stochastic gradient descent (SGD) with gradient compression has become a popular communication-efficient solution for accelerating distributed learning. One commonly used method for gradient compression is Top-K sparsification, which sparsifies the gradients by a fixed degree during model training. However, there has been a lack of an adaptive approach to adjust the sparsification degree to maximize the potential of the model's performance or training speed. This paper proposes a novel adaptive Top-K in SGD framework that enables an adaptive degree of sparsification for each gradient descent step to optimize the convergence performance by balancing the tradeoff between communication cost and convergence error. Firstly, an upper bound of convergence error is derived for the adaptive sparsification scheme and the loss function. Secondly, an algorithm is designed to minimize the convergence error under the communication cost constraints. Finally, numerical results on the MNIST and CIFAR-10 datasets demonstrate that the proposed adaptive Top-K algorithm in SGD achieves a significantly better convergence rate compared to state-of-the-art methods, even after considering error compensation.

Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user devices and the central server. Due to the heterogeneity in user's attributes and local data, attaining personalized models is critical to help improve the federated recommendation performance. In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation. Specifically, we construct a collaborative graph and incorporate attribute information to jointly learn the representation through a federated GNN. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Then each user learns a personalized model by combining the global federated model, the cluster-level federated model, and the user's fine-tuned local model. To alleviate the heavy communication burden, we intelligently select a few representative users (instead of randomly picked users) from each cluster to participate in training. Experiments on real-world datasets show that our proposed method achieves superior performance over existing methods.

The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI) recommendations and computing resource-aware mobile app recommendations. In many such recommendation scenarios, contexts are drifting over time. For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time. However, most existing graph-based collaborative filtering methods are designed under the assumption of static features. Therefore, they would require frequent retraining and/or yield graphical models burgeoning in sizes, impeding their suitability for context-drifting recommendations.

The exponential back-off mechanism, proposed for reducing MAC- layer contention in the 802.11 standard, is sub-optimal in terms of the network throughput. This back-off mechanism and its improved variants are especially inefficient under unknown dynamics such as packet arrivals and user entry/exit. In this paper, we formulate the problem of optimizing this back-off mechanism as a Markov decision process, and propose online learning algorithms to learn the optimal back-off schemes under unknown dynamics. By exploiting the fact that some components of the system dynamics (such as protocol states) are known because the users follow the common 802.11 protocol, we propose a post-decision state (PDS)- based learning algorithm to speed up the learning process. Compared to traditional Q-learning algorithms, the advantages of the proposed online learning algorithm are that 1) it exploits partial information about the system so that less information needs to be learned in comparison to other learning algorithms, and 2) it removes the necessity for action exploration which usually impedes the learning process of conventional learning algorithms (such as Q-Learning). We prove the optimality of the proposed PDS-based learning algorithm and via numerical results demonstrate the improvement over existing protocols and Q-learning in terms of throughput and convergence speed. We first address this problem from a single-user perspective and later describe the challenges involved and present new insights into the multi-user learning scenarios, especially in cases where the MDP models of the users are coupled with each other.

We study the problem of distributed interference management in a network of heterogeneous small cells with different cell sizes, different numbers of user equipments (UEs) served, and different throughput requirements by UEs. We consider the uplink transmission, where each UE determines when and at what power level it should transmit to its serving small cell base station (SBS). We propose a general framework for designing distributed interference management policies, which exploits weak interference among non-neighboring UEs by letting them transmit simultaneously (i.e., spatial reuse), while eliminating strong interference among neighboring UEs by letting them transmit in different time slots. The design of optimal interference management policies has two key steps. Ideally, we need to find all the subsets of non-interfering UEs i.e., the maximal independent sets (MISs) of the interference graph, but this is computationally intractable even when solved in a centralized manner. Then, to maximize some given network performance criterion subject to UEs' minimum throughput requirements, we need to determine the optimal fraction of time occupied by each MIS, which requires global information (e.g., all the UEs' throughput requirements and channel gains). In our framework, we first propose a distributed algorithm for the UE-SBS pairs to find a subset of MISs in logarithmic time (with respect to the number of UEs). Then we propose a novel problem reformulation which enables UE-SBS pairs to determine the optimal fraction of time occupied by each MIS with only local message exchange among the neighbors in the interference graph. Despite the fact that our interference management policies are distributed and utilize only local information, we can analytically bound their performance under a wide range of heterogeneous deployment scenarios in terms of the competitive ratio with respect to the optimal network performance, which can only be obtained in a centralized manner with NP complexity. Remarkably, we prove that the competitive ratio is independent of the network size. Through extensive simulations, we show that our proposed policies achieve significant performance improvements (ranging from 160% to 700%) over state-of-the-art policies.

Managing interference in a network of macrocells underlaid with femtocells presents an important, yet challenging problem. A majority of spatial (frequency/time) reuse based approaches partition the users based on coloring the interference graph, which is shown to be suboptimal. Some spatial time reuse based approaches schedule the maximal independent sets (MISs) in a cyclic, (weighted) round-robin fashion, which is inefficient for delay-sensitive applications. Our proposed policies schedule the MISs in a non-cyclic fashion, which aim to optimize any given network performance criterion for delay-sensitive applications while fulfilling minimum throughput requirements of the users. Importantly, we do not take the interference graph as given as in existing works; we propose an optimal construction of the interference graph. We prove that under certain conditions, the proposed policy achieves the optimal network performance. For large networks, we propose a low-complexity algorithm for computing the proposed policy. We show that the policy computed achieves a constant competitive ratio (with respect to the optimal network performance), which is independent of the network size, under wide range of deployment scenarios. The policy can be implemented in a decentralized manner by the users. Compared to the existing policies, our proposed policies can achieve improvement of up to 130% in large-scale deployments.

We study a wireless network in which multiple users stream delay-sensitive applications such as video conferencing and video streaming. Existing spectrum sharing policies, which determine when users access the spectrum and at what power levels, are either constant (i.e. users transmit simultaneously, at constant power levels) or weighted round-robin time-division multiple access (TDMA) (i.e. users access the spectrum in turn, one at a time). Due to multi-user interference, constant policies have low spectrum efficiency. We show that round-robin policies are inefficient for delay-sensitive applications because the various "positions" (i.e. transmission opportunities) in a cycle are not created equal: earlier transmission opportunities are more desirable since they enable users to transmit with lower delays. Specifically, we show that (weighted) round-robin TDMA policies cannot simultaneously achieve high network performance and low transmission delays. This problem is exacerbated when the number of users is large. We propose a novel framework for designing optimal TDMA spectrum sharing policies for delay-sensitive applications, which can guarantee their continuing QoS (CQoS), i.e. the desired throughput (and the resulting transmission delay) starting from every moment in time is guaranteed for each user. We prove that the fulfillment of CQoS guarantees provides strict upper bounds on the transmission delays incurred by the users. We construct the optimal TDMA policy that maximizes the desired network performance (e.g. max-min fairness or social welfare) subject to the users' CQoS guarantees. The key feature of the proposed policy is that it is not cyclic as in (weighted) round-robin policies. Instead, it adaptively determines which user should transmit next, based on the users' remaining amounts of transmission opportunities needed to achieve the desired performance. We also propose a low-complexity algorithm, which is run by each user in a distributed manner, to construct the optimal policy. Simulation results demonstrate that our proposed policy significantly outperforms the optimal constant policy and round-robin policies by up to 6 dB and 4 dB in peak signal-to-noise ratio (PSNR) for video streaming.

Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. The consumers choose their power consumption patterns according to different prices charged at different times of the day. Importantly, consumers incur discomfort costs from altering their power consumption patterns. Existing works propose stationary strategies for consumers that myopically minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested consumers as a repeated energy scheduling game which foresightedly minimizes their long-term total costs. We then propose a novel methodology for determining optimal nonstationary DSM strategies in which consumers can choose different daily power consumption patterns depending on their preferences and routines, as well as on their past history of actions. We prove that the existing stationary strategies are suboptimal in terms of long-term total billing and discomfort costs and that the proposed strategies are optimal and incentive-compatible (strategy-proof). Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

We study joint resource allocation and packet scheduling for multi-user video transmission in a 4G cellular network, where the base station (BS) allocates resources (i.e. bandwidth) among the users and each user schedules its video packets based on the allocated resources. Most existing works either propose myopic solutions for multi-user video transmission, in which the resource allocation and packet scheduling is designed to maximize the short-term video quality, or propose foresighted packet scheduling solutions for single-user video transmission which maximize the long-term video quality. In this work, we propose foresighted resource allocation and packet scheduling solutions for multi-user video transmission. Specifically, we develop a low-complexity algorithm in which the BS updates the prices of resources for each user and the users make individual packet scheduling decisions based on the prices. The algorithm can be implemented by the BS and the users in a decentralized manner, and converges to the optimal prices under which the users' optimal decisions maximize the long-term total video quality subject to per-user minimum video quality guarantees. Simulation results show 7 dB and 3 dB improvements in PSNR (Peak Signal-to-Noise Ratio) over myopic solutions and existing foresighted solutions, respectively.

Demand-side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers the differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off-peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested and foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

Due to the high bandwidth requirements and stringent delay constraints of multi-user wireless video transmission applications, ensuring that all video senders have sufficient transmission opportunities to use before their delay deadlines expire is a longstanding research problem. We propose a novel solution that addresses this problem without assuming detailed packet-level knowledge, which is unavailable at resource allocation time (i.e. prior to the actual compression and transmission). Instead, we translate the transmission delay deadlines of each sender's video packets into a monotonically-decreasing weight distribution within the considered time horizon. Higher weights are assigned to the slots that have higher probability for deadline-abiding delivery. Given the sets of weights of the senders' video streams, we propose the low-complexity Delay-Aware Resource Allocation (DARA) approach to compute the optimal slot allocation policy that maximizes the deadline-abiding delivery of all senders. A unique characteristic of the DARA approach is that it yields a non-stationary slot allocation policy that depends on the allocation of previous slots. This is in contrast with all existing slot allocation policies such as round-robin or rate-adaptive round-robin policies, which are stationary because the allocation of the current slot does not depend on the allocation of previous slots. We prove that the DARA approach is optimal for weight distributions that are exponentially decreasing in time. We further implement our framework for real-time video streaming in wireless personal area networks that are gaining significant traction within the new Internet-of-Things (IoT) paradigm. For multiple surveillance videos encoded with H.264/AVC and streamed via the 6tisch framework that simulates the IoT-oriented IEEE 802.15.4e TSCH medium access control, our solution is shown to be the only one that ensures all video bitstreams are delivered with acceptable quality in a deadline-abiding manner.

We develop a novel design framework for energy-efficient spectrum sharing among autonomous users who aim to minimize their energy consumptions subject to minimum throughput requirements. Most existing works proposed stationary spectrum sharing policies, in which users transmit at fixed power levels. Since users transmit simultaneously under stationary policies, to fulfill minimum throughput requirements, they need to transmit at high power levels to overcome interference. To improve energy efficiency, we construct nonstationary spectrum sharing policies, in which the users transmit at time-varying power levels. Specifically, we focus on TDMA (time-division multiple access) policies in which one user transmits at each time (but not in a round-robin fashion). The proposed policy can be implemented by each user running a low-complexity algorithm in a decentralized manner. It achieves high energy efficiency even when the users have erroneous and binary feedback about their interference levels. Moreover, it can adapt to dynamic entry and exit of users. The proposed policy is also deviation-proof, namely autonomous users will find it in their self-interests to follow it. Compared to existing policies, the proposed policy can achieve an energy saving of up to 90% under a large number of users.

This paper studies the provision of a wireless network by a monopolistic provider who may be either benevolent (seeking to maximize social welfare, namely the sum utility of all the users) or selfish (seeking to maximize provider profit). The paper addresses the following questions: Under what circumstances is it feasible for a provider, either benevolent or selfish, to operate a network in such a way as to cover costs? How is the optimal behavior of a benevolent provider different from the optimal behavior of a selfish provider? And, most importantly, how does the medium access control (MAC) technology influence the answers to these questions? To address these questions, we build a general model, and provide analysis and simulations for simplified but typical scenarios; the focus in these scenarios is on the contrast between the outcomes obtained under carrier-sensing multiple access (CSMA) and outcomes obtained under time-division multiple access (TDMA). Simulation results demonstrate that differences in MAC technology can have a significant effect on social welfare, on provider profit, and even on the (financial) feasibility of a wireless network.

Spectrum sharing policies are essential for cognitive radio networks, where primary and secondary users aim to minimize their average energy consumptions subject to minimum throughput requirements. Most existing works proposed stationary spectrum sharing policies, in which users transmit simultaneously at fixed power levels, and need to transmit at high power levels due to multi-user interference. In this paper, we propose nonstationary spectrum sharing policies in which users transmit in a TDMA fashion (but not necessarily in a round-robin manner). Due to the absence of multi-user interference and the ability to let users adaptively switch between transmission and dormancy, our proposed policy greatly improves the spectrum and energy efficiency, and ensures no interference to primary users. Moreover, the proposed policy achieves high energy efficiency even when users have erroneous and binary feedback about their received interference and noise power levels. The proposed policy is also deviation-proof, namely the autonomous users find it in their self-interests to comply with the policy. The proposed policy can be implemented by each user running a low-complexity algorithm in a distributed fashion. Compared to existing policies, the proposed policies can achieve an energy saving of up to 80%.

Crowdsourcing systems (e.g. Yahoo! Answers and Amazon Mechanical Turk) provide a platform for requesters, who have tasks to solve, to ask for help from workers. Vital to the proliferation of crowdsourcing systems is incentivizing the workers to exert high effort to provide high-quality services. Reputation mechanisms have been shown to work effectively as incentive schemes in crowdsourcing systems. A reputation agency updates the reputations of the workers based on the requesters' reports on the quality of the workers' services. A low-reputation worker is less likely to get served when it requests help, which provides incentives for the workers to obtain a high reputation by exerting high effort. However, reputation update errors are inevitable, because of either system errors such as loss of reports, or inaccurate reports, resulting from the difficulty in accurately assessing the quality of a worker's service. The reputation update error prevents existing reputation mechanisms from achieving the social optimum. In this paper, we propose a simple binary reputation mechanism, which has only two reputation labels (“good” and “bad”). To the best of our knowledge, our proposed reputation mechanism is the first that is proven to be able to achieve the social optimum even in the presence of reputation update errors. We provide design guidelines for socially-optimal binary reputation mechanisms.

This paper studies the interaction between a designer and a group of strategic and self-interested users who possess information the designer does not have. Because the users are strategic and self-interested, they will act to their own advantage, which will often be different from the interest of the designer, even if the latter is benevolent and seeks to maximize (some measure of) social welfare. In the settings we consider, the designer and the users can communicate (perhaps with noise), the designer can observe the actions of the users (perhaps with error) and the designer can commit to (plans of) actions - interventions - of its own. The designer's problem is to construct and implement a mechanism that provides incentives for the users to communicate and act in such a way as to further the interest of the designer - despite the fact that they are strategic and self-interested and possess private information. To address the designer's problem we propose a general and flexible framework that applies to many scenarios. To illustrate the usefulness of this framework, we discuss some simple examples, leaving further applications to other papers. In an important class of environments, we find conditions under which the designer can obtain its benchmark optimum - the utility that could be obtained if it had all information and could command the actions of the users - and conditions under which it cannot. More broadly we are able to characterize the solution to the designer's problem, even when it does not yield the benchmark optimum. Because the optimal mechanism may be difficult to construct and implement, we also propose a simpler and more readily implemented mechanism that, while falling short of the optimum, still yields the designer a "good" result.

Most congestion control schemes are based on user cooperation, i.e., they implicitly assume that users are willing to share their private information and to take actions such that the network operates efficiently. However, a self-interested and strategic user might exploit such schemes to obtain an individual gain at the expenses of the other users, misrepresenting its private information and overusing the resources. We first quantify the inefficiency of the network in the presence of selfish users for two different scenario: in the complete information case - in which the users have no private information - and in the incomplete information case - in which the users have private information. Then, we ask whether the congestion control scheme can be designed to be robust to self-interested strategic users. To reach this objective, we use an intervention scheme. For the complete information scenario we describe a scheme that is able to give the users an incentive to optimally use the resources. For the incomplete information scenario we describe two schemes that provide the users with an incentive to report truthfully and to use the resources efficiently, although not always optimally. Illustrative results show that the considered schemes can considerably improve the efficiency of the network.

We develop a novel design framework for dynamic distributed spectrum sharing among secondary users (SUs), who adjust their power levels to compete for spectrum opportunities while satisfying the interference temperature (IT) constraints imposed by primary users. The considered interaction among the SUs is characterized by the following three unique features. First, the SUs are interacting with each other repeatedly and they can coexist in the system for a long time. Second, the SUs have limited and imperfect monitoring ability: they only observe whether the IT constraints are violated, and their observation is imperfect due to the erroneous measurements. Third, since the SUs are decentralized, they are selfish and aim to maximize their own long-term payoffs from utilizing the network rather than obeying the prescribed allocation of a centralized controller. To capture these unique features, we model the interaction of the SUs as a repeated game with imperfect monitoring. We first characterize the set of Pareto optimal operating points that can be achieved by deviation-proof spectrum sharing policies, which are policies that the selfish users find it in their interest to comply with. Next, for any given operating point in this set, we show how to construct a deviation-proof policy to achieve it. The constructed deviation-proof policy is amenable to distributed implementation, and allows users to transmit in a time-division multiple-access (TDMA) fashion. In the presence of strong multi-user interference, our policy outperforms existing spectrum sharing policies that dictate users to transmit at constant power levels simultaneously. Moreover, our policy can achieve Pareto optimality even when the SUs have limited and imperfect monitoring ability, as opposed to existing solutions based on repeated game models, which require perfect monitoring abilities. Simulation results validate our analytical results and quantify the performance gains enabled by the proposed spectrum sharing policies.

In communication systems where users share common resources, selfish behavior usually results in suboptimal resource utilization. There have been extensive works that model communication systems with selfish users as one-shot games and propose incentive schemes to achieve Pareto-optimal outcomes. However, in many communication systems, due to strong negative externalities among users, the sets of feasible payoffs in one-shot games are nonconvex. Thus, it is possible to expand the set of feasible payoffs by having users choose different action profiles in an alternating manner. In this paper, we formulate a model of repeated games with intervention. First, by using repeated games we can convexify the set of feasible payoffs in one-shot games. Second, by using intervention in repeated games we can achieve a larger set of equilibrium payoffs and loosen requirements for users' patience to achieve a target payoff. We study the problem of maximizing a welfare function defined on users' payoffs. We characterize the limit set of equilibrium payoffs. Given the optimal equilibrium payoff, we derive the sufficient condition on the discount factor and the intervention capability to achieve it, and design corresponding equilibrium strategies. We illustrate our analytical results with power control and flow control.

We study the power control problem in wireless ad hoc networks with selfish users. Without incentive mechanisms, selfish users transmit at their maximum power levels at the Nash equilibrium (NE), causing significant interference to each other. In order to induce users to transmit at desired power levels, existing works have proposed pricing and auctions as incentive mechanisms. With pricing or auctions, it is explicitly stated or implicitly assumed that the users are obedient, in that they adopt the utility functions designed by the system and accept the prices as control signals. In this paper, we use the intervention mechanism to incentivize selfish users to achieve efficient outcomes as the (unique) NE. In the intervention mechanism, a system designer prescribes a intervention rule and uses a intervention device to execute it. Depending on the monitoring technology and intervention capability of the intervention device, we propose two types of intervention rules with different performance and complexity tradeoffs. We study the performance achievable by the proposed intervention rules, as well as the design principles for different intervention rules. We prove that all the Pareto boundary can be achieved as the NE or even the unique NE of the game with intervention. Simulation results demonstrate the performance improvement achieved when using different intervention rules and illustrate performance analysis on different intervention rules.

In this paper, we study the joint power and channel resource allocation problem for a multiuser F/TDMA decode-and-forward (DF) relay network under per-node power constraints and a total channel resource constraint. Our goal is to maximize the total throughput achieved by the systems. To that end, we formulate a joint power and channel resource allocation problem. We develop an iterative optimization algorithm to solve this problem, whose convergence and optimality are guaranteed. Due to the per-node power constraints, more than one relay node may be needed for a single data stream. Our solution also provides a way of finding the optimal relays among the assisting relay nodes.

Block diagonalization (BD) is a low-complexity linear preceding scheme for multiuser MIMO (MU-MIMO) downlink, which can completely eliminate multi-user interference with perfect channel state information (CSI) at the transmitter. Under the assumption of block fading channels, BD with fixed amount of feedback is interference-limited. In this paper, we first introduce a low-complexity CSI feedback scheme, by exploiting the temporal correlation of practical channels, to improve the efficiency of feedback. Then, a modified BD algorithm is proposed to further utilize the channel correlation in time domain. Combined with the CSI feedback scheme, the modified BD algorithm handles the interference-limiting problem in a wide range of SNR with a fixed, small number of feedback bits.

This paper addresses the problem of joint linear transceiver design in the downlink of multiuser MIMO systems. We define the performance criterion as minimizing the maximal mean square error among all the data streams (max-MSE) under a total power constraint. The proposed algorithm is based on a dual decomposition technique and it iterates between close-formed precoder/decoder designs. The proposed algorithm outperforms most transceiver optimization algorithms in terms of BER performance. Compared to the algorithm which can achieve the same performance, it has a lower computational complexity.