[show abstract][hide abstract] ABSTRACT: Wireless links are often unreliable and prone to transmission error due to varying channel conditions. These can degrade the performance in wireless networks, particularly for applications with tight quality-of-service requirements. A common remedy is to use channel coding where the transmitter node adds redundant bits to the transmitted packets in order to reduce the error probability at the receiver. However, this per-link solution can compromise the link data rate, leading to undesired end-to-end performance. In this paper, we show that this latter shortcoming can be mitigated if the end-to-end transmission rates and channel code rates are selected properly over multiple routing paths. We formulate the joint channel coding and end-to-end data rate allocation problem in multipath wireless networks as a network throughput maximization problem, which is non-convex. We tackle the non-convexity by using function approximation and iterative techniques from signomial programming. Simulation results confirm that by using channel coding jointly with multi-path routing, the end-to-end network performance can be improved significantly.
Communications (ICC), 2011 IEEE International Conference on; 07/2011
[show abstract][hide abstract] ABSTRACT: Multipath routing and adaptive channel coding are two well-known approaches that have been separately applied to wireless networks to improve the effective throughput. However, it is usually expected that achieving a high throughput would be at a noticeable cost of increasing the average end-to-end delay and causes major degradation in the overall network performance. In this paper, we show that a combination of multipath routing and adaptive channel coding can improve throughput and reduce delay and that it is possible to trade off delay for throughput and vice versa. This is in contrast to the general expectation that higher throughput can only be achieved with noticeable degradations in the end-to-end network delay. In this regard, we jointly formulate the end-to-end data rate allocation and adaptive channel coding (at the physical layer) within the general framework of network utility maximization (NUM). Depending on the choice of the objective function, we formulate two NUM problems: one aiming to maximize the aggregate network utility and another one aiming to maximize the minimum utility among the end-to-end flows to achieve fairness, which is of interest in certain vehicular network applications. Simulation results confirm that we can significantly decrease the average delay at the cost of a small decrease in throughput. This is achieved by maximizing the aggregate utility in the network when fairness is not the dominant concern. Furthermore, we also show that, even when resource allocation is performed to provide fairness, we can still decrease the maximum end-to-end delay of the network at the cost of a slight decrease in the minimum throughput.
IEEE Transactions on Vehicular Technology 04/2011; · 2.06 Impact Factor
[show abstract][hide abstract] ABSTRACT: Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.
[show abstract][hide abstract] ABSTRACT: The emergence of cloud computing has established a trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. However, data centers and cloud computing also provide opportunities to help the grid with respect to robustness and load balancing. To gain insights into these opportunities, we formulate the service request routing problem in cloud computing jointly with the power flow analysis in smart grid and explain how these problems can be related. Simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that a grid-aware service request routing design in cloud computing can significantly help in load balancing in the electric grid and making the grid more reliable and more robust with respect to link breakage and load demand variations.
Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on; 11/2010
[show abstract][hide abstract] ABSTRACT: In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.
Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on; 11/2010
[show abstract][hide abstract] ABSTRACT: In this paper, we consider the problem of random access in wireless local area networks (WLANs) with each station generating either elastic or inelastic traffic. Elastic traffic is usually non-real-time, while inelastic traffic is usually coming from real-time applications. We formulate a network utility maximization (NUM) problem, where the optimization variables are the persistent probabilities of the stations and the utilities are either concave or sigmoidal functions. Sigmoidal utility functions can better represent inelastic traffic sources compared to concave utility functions commonly used in the existing random access literature. However, they lead to non-convex NUM problems which are not easy to solve in general. By applying the dual decomposition method, we propose a subgradient algorithm to solve the formulated NUM problem. We also develop closed-form solutions for the dual subproblems involving sigmoidal functions that have to be solved in each iteration of the proposed algorithm. Furthermore, we obtain a sufficient condition on the link capacities which guarantees achieving the global optimal solution when our proposed algorithm is being used. If this condition is not satisfied, then we can still guarantee that the optimal value of the objective function is within some lower and upper bounds. We perform various simulations to validate our analytical models when the available link capacities meet or do not meet the sufficient optimality condition.
IEEE Transactions on Wireless Communications 07/2010; · 2.42 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this paper, we propose a novel optimization-based pre-equalization filter (PEF) design for direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining. The key feature in our design is that we explicitly take into account the spectral mask constraints that are usually imposed by the telecommunication regulation bodies. This avoids the need for an inefficient power back-off, which is necessary for transmit structures designed solely based on average transmit power constraints. Simulation results confirm that the proposed PEF design leads to significant performance gains over UWB PEF structures without explicit spectral mask considerations.
Communications (ICC), 2010 IEEE International Conference on; 06/2010
[show abstract][hide abstract] ABSTRACT: Radio frequency identification (RFID) is an emerging wireless communication technology which allows objects to be identified automatically. An RFID system consists of a set of readers and several objects, equipped with small and inexpensive computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. High probability of collision impairs the benefit of multiple reader deployment and results in misreading. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not achieve optimal system performance. In this paper, we systematically design two optimization-based distributed channel selection and randomized interrogation algorithms for dense RFID systems. For this purpose, we develop elaborate models for the reader-to-tag and reader-to-reader collision problems. The first algorithm is fully distributed and is guaranteed to find a local optimum of a max-min fair resource allocation problem for RFID systems. The second algorithm is semi-distributed and achieves the global optimal system performance. Max-min fair optimality balances the performance and the processing load among readers. Simulation results show that our algorithms have significantly better performance than the previous heuristic algorithms.
IEEE Transactions on Wireless Communications 05/2010; · 2.42 Impact Factor
[show abstract][hide abstract] ABSTRACT: Most of the previous work on network coding has assumed that the users are not selfish and always follow the designed coding schemes. However, recent results have shown that selfish users do not have the incentive to participate in inter-session network coding in a static non-cooperative game setting. As a result, the worst-case network efficiency (i.e., the price-of-anarchy) can be as low as 22%. In this paper, we show that if the same game is played repeatedly, then the price-of-anarchy can be significantly improved to 48%. We propose a grim-trigger strategy that encourages users to cooperate and participate in the inter-session network coding. A key challenge is to determine a common cooperative coding rate that the users should mutually agree on. We propose to resolve the conflict of interest among the users through a bargaining process. We derive a tight upper bound for the price-of-anarchy which is valid for any bargaining scheme. Moreover, we propose a simple and efficient min-max bargaining solution that can achieve this upper bound. Our results represent one of the first steps towards designing practical inter-session network coding schemes that can achieve reasonable performance for selfish users.
[show abstract][hide abstract] ABSTRACT: Random access protocols, such as Aloha, are commonly modeled in wireless ad-hoc networks by using the protocol model. However, it is well-known that the protocol model is not accurate and particularly it cannot account for aggregate interference from multiple interference sources. In this paper, we use the more accurate physical model, which is based on the signal-to-interference-plus-noise-ratio (SINR), to study optimization-based design in wireless random access systems, where the optimization variables are the transmission probabilities of the users. We focus on throughput maximization, fair resource allocation, and network utility maximization, and show that they entail non-convex optimization problems if the physical model is adopted. We propose two schemes to solve these problems. The first design is centralized and leads to the global optimal solution using a sum-of-squares technique. However, due to its complexity, this approach is only applicable to small-scale networks. The second design is distributed and leads to a close-to-optimal solution using the coordinate ascent method. This approach is applicable to medium-size and large-scale networks. Based on various simulations, we show that it is highly preferable to use the physical model for optimization-based random access design. In this regard, even a sub-optimal design based on the physical model can achieve a significantly better performance than an optimal design based on the inaccurate protocol model.
[show abstract][hide abstract] ABSTRACT: In this paper, we propose a novel optimization-based pre-equalization filter (PEF) design for multiple-input single-output (MISO) direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining. The key feature in our design is that we explicitly take into account spectral mask constraints which are usually imposed by telecommunications regulation and standardization bodies. This avoids the need for an inefficient power back-off, which is necessary for existing pre-equalizer and pre-Rake designs that are designed solely based on average transmit power constraints. Simulation results confirm that the proposed PEF design leads to significant performance gains over UWB PEF structures without any explicit spectral mask considerations. Furthermore, the use of multiple transmit antennas is shown to provide substantial combining gains compared to single-antenna transmitter structures. We also investigate the impact of certain system and optimization parameters on the performance of the proposed PEF design.
Smart Antennas (WSA), 2010 International ITG Workshop on; 03/2010
[show abstract][hide abstract] ABSTRACT: In this paper, we consider deployment of energy consumption scheduling (ECS) devices in smart meters for autonomous demand side management within a neighborhood, where several buildings share an energy source. The ECS devices are assumed to be built inside smart meters and to be connected to not only the power grid, but also to a local area network which is essential for handling two-way communications in a smart grid infrastructure. They interact automatically by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber, with an aim at reducing the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Incentives are also provided for the subscribers to actually use the ECS devices via a novel pricing model, derived from a game-theoretic analysis. Simulation results confirm that our proposed distributed algorithm significantly reduces the PAR and the total cost in the system.
[show abstract][hide abstract] ABSTRACT: Network coding has recently received increasing attention to improve performance and increase capacity in both wired and wireless communication networks. In this paper, we focus on inter-session network coding, where multiple unicast sessions jointly participate in network coding. Wireless links are often unreliable because of varying channel conditions. We consider multi-hop unicast sessions over unreliable links and propose a distributed end-to-end transmission rate adjustment mechanism to maximize the aggregate network utility by taking into account the wireless link reliability information. This includes an elaborate modeling of end-to-end reliability. Simulation results show that by taking into account the reliability information, we can increase the network throughput by up to 100% for some network topologies. We can also increase the aggregate network utility significantly for various choices of utility functions.
Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE; 01/2010
[show abstract][hide abstract] ABSTRACT: A radio frequency identification (RFID) system consists of a set of readers and several objects, equipped with small computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not fully utilize the achievable system performance. In this paper, we develop an optimization-based distributed randomized multi-channel interrogation algorithm, called FDFA, for large-scale RFID systems. For this purpose, we develop elaborate models for reader-to-tag and reader-to-reader collision problems. FDFA algorithm is guaranteed to find a local optimum of a max-min fair resource allocation problem to balance the processing load among readers. Simulation results show that FDFA has a significantly better performance than the existing heuristic algorithms in terms of the number of successful interrogations. It also better utilizes the frequency spectrum.
Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE; 01/2010
[show abstract][hide abstract] ABSTRACT: In a multi-hop wireless access network, where each node is an independent self-interested commercial entity, pricing is helpful not only to encourage collaboration but also to utilize the network resources efficiently. In this paper, we propose a market-based model with two-fold pricing (TFP) for wireless access networks. In our model, the relay-pricing is used to encourage nodes to forward packets for other nodes. Each node receives a payment for the relay service that it provides. We also consider interference-pricing to leverage optimal resource allocation. Together, the relay and interference prices incorporate both cooperative and competitive interactions among the nodes. We prove that TFP guarantees positive profit for each individual wireless node for a wide range of pricing functions. The profit increases as the node forwards more packets. Thus, the cooperative nodes are well rewarded. We then determine the relay and interference pricing functions such that the network social welfare and the aggregate network utility are maximized. Simulation results show that, compared to two recently proposed single-fold pricing models, where only the relay or only the interference prices are considered, our proposed TFP scheme significantly increases the total network profit as well as the aggregate network throughput. TFP also leads to more fair revenue sharing among the wireless relay nodes.
IEEE Transactions on Wireless Communications 09/2009; · 2.42 Impact Factor
[show abstract][hide abstract] ABSTRACT: A common assumption in the network coding literature is that the users are cooperative and will not pursue their own interests. However, this assumption can be violated in practice. In this paper, we analyze inter-session network coding in a wired network, assuming that the users are selfish and act as strategic players to maximize their own utility. We prove the existence of Nash equilibria for a wide range of utility functions. The number of Nash equilibria can be large (even infinite) under certain conditions, which is in sharp contrast to a similar game setting with traditional packet forwarding. We then characterize the worst-case efficiency bounds, i.e., the price-of-anarchy (PoA), compared to an optimal and cooperative network design. We show that by using a novel discriminatory pricing scheme that charges encoded and forwarded packets differently, we can improve PoA in comparison with the case where a single pricing scheme is being used. However, PoA is still worse than the case when network coding is not applied. This implies that inter-session network coding is more sensitive to strategic behavior. For example, for the case where only two network coding flows share a single bottleneck link, the efficiency at certain Nash equilibria can be as low as 48%. These results generalize the well-known result of guaranteed 67% efficiency bounds shown by Johari and Tsitsiklis for traditional packet forwarding networks.
Communications, 2009. ICC '09. IEEE International Conference on; 07/2009
[show abstract][hide abstract] ABSTRACT: In wireless local area networks (WLANs), quality of service (QoS) can be provided by mapping applications with different requirements (e.g., delay and throughput) into one of the available access categories (ACs), as is done in the IEEE 802.11e standard. With the increasing programmability of network adapters, a malicious user can strategically declare a higher AC for its application to gain an unfair share of resources. This can drastically degrade the network performance and avoid adequate service distinction among different ACs. In this paper, we use the technique of mechanism design in game theory to tackle this problem in WLANs with random access. We propose to use the Vickrey-Clarke-Groves (VCG) mechanism in order to motivate each station to inform the access point (AP) truthfully, about the required AC of its application. The AP will then inform each station about its persistent probability and the price it needs to pay for the offered service. The result of the allocation of the persistent probabilities can be used for admission control. Simulation results show that the use of mechanism design can lead to a higher aggregate utility and prevents malicious users from gaining an unfair share of the network bandwidth.
Communications, 2009. ICC '09. IEEE International Conference on; 07/2009
[show abstract][hide abstract] ABSTRACT: In wireless sensor networks (WSNs), the field information (e.g., temperature, humidity, airflow) is acquired via several battery-equipped wireless sensors and is relayed toward a sink node. As the size of the WSNs increases, it becomes inefficient (in terms of power consumption) when gathering all information in a single sink. To tackle this problem, one can increase the number of sinks. The set of sensor nodes that are sending data to sink k is called commodity k . In this paper, we formulate the lexicographically optimal commodity lifetime (LOCL) routing problem. A stepwise centralized algorithm called the LOCL algorithm is proposed, which can obtain the optimal routing solution and lead to lexicographical fairness among commodity lifetimes. We then show that, under certain assumptions, the lexicographical optimality among commodity lifetimes can be achieved by providing lexicographical optimality among node lifetimes. This motivates us to propose our second algorithm, which is called the lexicographically optimal node lifetime (LONL) algorithm, which is suitable for practical implementation. Simulation results show that our proposed LOCL and LONL algorithms increase the normalized commodity and node lifetimes compared with the maximum lifetime with multiple sinks (MLMS) and lexicographical max-min fair (LMM) routing algorithms.
IEEE Transactions on Vehicular Technology 04/2009; · 2.06 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this paper, we propose two distributed contention-based medium access control (MAC) algorithms for solving a network utility maximization (NUM) problem in wireless ad hoc networks. Most of the previous NUM-based random access algorithms have one or more of the following performance bottlenecks: (1) extensive signaling among the nodes to achieve semi-distributed implementations, (2) synchronous updates of contention probabilities, (3) small update stepsizes to ensure convergence but with typically slow speed, and (4) supporting a limited range of utility functions under which the NUM is shown to be convex. Our proposed algorithms overcome the bottlenecks in all four aspects. First, only limited amount of message passing among nodes is required. Second, fully asynchronous updates of contention probabilities are allowed. Furthermore, our algorithms are robust to arbitrary large message passing delay and message loss. Third, we do not utilize any stepsize during updates, thus our algorithms can achieve faster convergence. Finally, our proposed algorithms have provable convergence, optimality, and robustness properties under a wider range of utility functions, even if the NUM problem is non-convex. Simulation results show the optimality and fast convergence of our algorithms, performance improvements compared with the subgradient-based MAC, and better efficiency-fairness tradeoff compared with the IEEE 802.11 distributed coordination function.
IEEE Transactions on Wireless Communications 03/2009; · 2.42 Impact Factor
[show abstract][hide abstract] ABSTRACT: The emerging high-rate wireless personal area network (WPAN) technology is capable of supporting high-speed and high-quality real-time multimedia applications. In particular, video streams are deemed to be a dominant traffic type, and require quality of service (QoS) support. However, in the current IEEE 802.15.3 standard for MAC (media access control) of high-rate WPANs, the implementation details of some key issues such as scheduling and QoS provisioning have not been addressed. In this paper, we first propose a Markov decision process (MDP) model for optimal scheduling for video flows in high-rate WPANs. Using this model, we also propose a scheduler that incorporates compact state space representation, function approximation, and reinforcement learning (RL). Simulation results show that our proposed RL scheduler achieves nearly optimal performance and performs better than F-SRPT, EDD + SRPT, and PAP scheduling algorithms in terms of a lower decoding failure rate.