Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accura...
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Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accuracy, speed, and robustness than numerous existing methods. decentralized GEC-SR (deGEC-SR) addresses the scalability issue in high-resolution images. However, the convergence speed and stability of these algorithms heavily rely on the settings of several handcrafted tuning factors with inefficient turning process. In this work, we propose deGEC-SR-Net by unfolding the iterative deGEC-SR algorithm into a learning network architecture with trainable parameters. The parameters of deGEC-SR-Net are determined by data-driven training. Numerical results show that deGEC-SR-Net provides substantially faster convergence than deGEC-SR and exhibits superior robustness to noise and prior mis-specifications.
The popularity of mobile cloud computing has provisioned a new paradigm for mobile devices to offload their computation to remote mobile cloud data center for task execution. However, the mobile cloud data center may ...
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ISBN:
(纸本)9781509018932
The popularity of mobile cloud computing has provisioned a new paradigm for mobile devices to offload their computation to remote mobile cloud data center for task execution. However, the mobile cloud data center may suffer from dramatic energy consumption. To solve this problem, we propose to migrate the workload from remote cloud data center to nearby cloudlets, so as to relieve the pressure of cloud data center and save the energy consumption. Specifically, we propose a lightweight and decentralized algorithm based on the Alternating Direction Method of Multipliers (ADMM) algorithm to construct the migration scheme. Simulations show that our algorithm can fast converge in tens of iterations, and decrease the overall energy consumption.
In this paper we present two new decentralized algorithms for autonomous intersection management and compare the performance of the algorithms with an established centralized solution. One of the algorithms addresses ...
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ISBN:
(纸本)9781509003006
In this paper we present two new decentralized algorithms for autonomous intersection management and compare the performance of the algorithms with an established centralized solution. One of the algorithms addresses the problem through an unstructured peer-to-peer approach and the other uses a Distributed Hash Table to distribute knowledge of intersection usage among participating vehicles. We evaluate these algorithms through simulation and by comparing average delay to the performance of a centralized reservation-based algorithm. We find that in times of light congestion the decentralized algorithms perform as well as the centralized approach. During times of moderate or heavy congestion the unstructured peer-to-peer algorithm performs better than the centralized algorithm, and the DHT-based algorithm performs worse.
This paper aims at designing a novel decentralized energy management strategy to optimally split the power in a modular fuel cell system (FCS). The FCS is composed of two fuel cells (FCs) in a parallel structure and a...
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ISBN:
(纸本)9781728112497
This paper aims at designing a novel decentralized energy management strategy to optimally split the power in a modular fuel cell system (FCS). The FCS is composed of two fuel cells (FCs) in a parallel structure and a battery pack. The proposed decentralized strategy consists of two layers. Initially, a local optimization problem is solved by means of auxiliary problem principle (APP) method at each time step for each of the fuel cell modules (FCMs). Subsequently, the obtained values are broadcasted to the near FCM neighbors. At each step, the APP utilizes the power values and Lagrange parameters of the previous step shared by the sub-problem neighbors to find the solution that is the reference power for each FC. Compared to the centralized form of the APP algorithm, besides the modularity point of view, the proposed strategy is able to converge to the optimal answer faster. The final results indicate that the performance of the proposed strategy is very close to the results achieved by dynamic programming (DP).
This paper proposes a distributed cooperative framework to improve the energy efficiency of green cellular networks. Based on the traffic load, neighboring base stations (BSs) cooperate to optimize the BS switching (s...
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ISBN:
(纸本)9781479973392
This paper proposes a distributed cooperative framework to improve the energy efficiency of green cellular networks. Based on the traffic load, neighboring base stations (BSs) cooperate to optimize the BS switching (sleeping) strategies so as to maximize the energy saving while guaranteeing users' minimal service requirements. An interaction graph is defined to capture the network impact of the BS switching operation. Then, we formulate the problem of energy saving as a constrained graphic game, where each BS acts as a game player with the constraint of traffic load. The constrained graphic game is proved to be an exact constrained potential game. Furthermore, we prove the existence of a generalized Nash equilibrium (GNE), and the best GNE coincides with the optimal solution of total energy consumption minimization. Accordingly, we design a decentralized iterative algorithm to find the best GNE (i.e., the global optimum), where only local information exchange among the neighboring BSs is needed. Simulation results finally illustrate the convergence and optimality of the proposed algorithm.
The convergence performance of distributed optimization algorithms is of significant importance to solve optimal power flow (OPF) in a distributed fashion. In this paper, we aim to provide some insights on how to part...
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ISBN:
(纸本)9781538677032
The convergence performance of distributed optimization algorithms is of significant importance to solve optimal power flow (OPF) in a distributed fashion. In this paper, we aim to provide some insights on how to partition a power system to achieve a high convergence rate of distributed algorithms for the solution of an OPF problem. We analyzed several features of the power network to find a set of suitable partitions with the aim of convergence performance improvement. We model the grid as a graph and decompose it based on the edge betweenness graph clustering. This technique provides several partitions. To find an effective partitioning, we merge the partitions obtained by clustering technique and analyze them based on characteristics of tie-lines connecting neighboring partitions. The main goal is to find the best set of partitions with respect to the convergence speed. We deploy analytical target cascading (ATC) method to distributedly solve optimization subproblems. We test the proposed algorithm on the IEEE 118-bus system. The results show that the algorithm converges faster with a proper partitioning, whereas improper partitioning leads to a large number of iterations.
Dynamic spectrum sharing can improve the efficiency of spectrum utilization. Spectrum trading between primary users (PUs) and secondary users (SUs) is a popular and efficient way to fulfill this kind of spectrum shari...
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ISBN:
(纸本)9783319218373;9783319218366
Dynamic spectrum sharing can improve the efficiency of spectrum utilization. Spectrum trading between primary users (PUs) and secondary users (SUs) is a popular and efficient way to fulfill this kind of spectrum sharing. In this paper we present a novel spectrum trading mechanism which operate among secondary users. More specifically, some secondary users which has leased spectrum from PUs can sublet it to other SUs to reduce their own leasing cost. Then all of the SUs can share these spectrum bands to conduct data transmission respectively. This leads to a new multi-leader multi-follower (MLMF) game which is different from existing works. The existence of Nash equilibrium of this formulated game is proven by redefining it as a shared MLMF constraint game. A decentralized algorithm is then proposed to find Nash equilibrium of this two tiers game with only local information. Simulations are provided to illustrate the convergence and effectiveness of the proposed algorithm.
The jettisoned communication buoy can provide submarine with effective covert sending capability. This paper studies the delay parameters of communication buoy and the use of escape avoidance to maintain concealment, ...
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ISBN:
(纸本)9798350373141;9798350373158
The jettisoned communication buoy can provide submarine with effective covert sending capability. This paper studies the delay parameters of communication buoy and the use of escape avoidance to maintain concealment, in order to analyze the problems existing in the use of abandoned communication buoy. By studying the relationship between the parameter coefficient of the sending delay and the submarine escape speed, we can master the method of reducing the probability of submarine detection when the communication buoy sends the signal, and improve the real-time performance of the communication. The research results provide a reference for the formulation of submarine evasion scheme when the communication buoy sends signals, and are of great significance to improve the concealment efficiency of submarine.
The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set ...
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ISBN:
(纸本)9781728105154
The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set of influential nodes for fast content dissemination in a social network. In this paper, we propose a novel approach to solve the problem, where opinion maximization is achieved through efficient information spreading. In our model, multiple sources inject information continuously into the network, while the regular nodes with heterogeneous social learning abilities spread the information to their acquaintances through gossip mechanism. One of the sources employs smart information spreading and the rest spread information randomly. We model the social interactions and evolution of opinions as a dynamic Bayesian network (DBN), using which the opinion maximization is formulated as a sequential decision problem. Since the problem is intractable, we develop multiple variants of centralized and decentralized algorithms to obtain approximate solutions. Through simulations in synthetic and real-world networks, we demonstrate two key results: 1) the proposed methods perform better than random spreading by a large margin, and 2) even though the smart source (that spreads the desired content) is unfavorably located in the network, it can outperform the contending random sources located at favorable positions.
As a basic requirement of live peer-to-peer multimedia streaming sessions, the streaming playback rate needs to be strictly enforced at each of the peers. In real-world peer-to-peer streaming sessions with very large ...
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ISBN:
(纸本)9780819469908
As a basic requirement of live peer-to-peer multimedia streaming sessions, the streaming playback rate needs to be strictly enforced at each of the peers. In real-world peer-to-peer streaming sessions with very large scales, the number of streaming servers for each session may not be easily increased, leading to a limited supply of bandwidth. To scale to a large number of peers, one prefers to regulate the bandwidth usage on each of the overlay links in an optimal fashion, such that limited supplies of bandwidth may be maximally utilized. In this paper, we propose a decentralized bandwidth allocation algorithm that can be practically implemented in peer-to-peer streaming sessions. Given a mesh P2P topology, our algorithm explicitly reorganizes the bandwidth of data transmission on each overlay link, such that the streaming bandwidth demand is always guaranteed to be met at any peer in the session, without depending on any a priori knowledge of available peer upload or overlay link bandwidth. Our algorithm is especially useful when there exists no or little surplus bandwidth supply from servers or other peers. It adapts well to time-varying availability of bandwidth, and guarantees bandwidth supply for the existing peers during volatile peer dynamics. We demonstrate the effectiveness of our algorithm with in-depth simulation studies.
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