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Uplink Interference Mitigation for OFDMA Femtocell Networks

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2012年第2期11卷 614-625页

作者： Sun, Yanzan Jover, Roger Piqueras Wang, Xiaodong Tongji Univ BW&M Inst Shanghai 200092 Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA

Femtocell networks, consisting of a conventional macro cellular deployment and overlaying femtocells, forming a hierarchical cell structure, constitute an attractive solution to improving the macrocell capacity and coverage. However, the inter-and intra-tier interferences in such systems can significantly reduce the capacity and cause an unacceptably high level of outage. This paper treats the uplink interference problem in orthogonal frequency-division multiple-access (OFDMA)-based femtocell networks with partial cochannel deployment. We first propose an inter-tier interference mitigation strategy without the femtocell users power control by forcing the femto-interfering macrocell users to use only some dedicated subcarriers. The non-interfering macrocell users, on the other hand, can use either the dedicated subcarriers, or the shared subcarriers which are also used by the femtocell users. We then propose subcarrier allocation schemes based on the auction algorithm for macrocell users and femtocell users, respectively, to independently mitigate the intra-tier interference. The proposed interference mitigation scheme for femtocell networks offers significant performance improvement over the existing methods by substantially reducing the inter-and intra-tier inferences in the system.

关键词： Femtocell OFDMA interference mitigation auction algorithm

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Provably-Good Distributed algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks

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IEEE TRANSACTIONS ON ROBOTICS 2015年第1期31卷 19-30页

作者： Luo, Lingzhi Chakraborty, Nilanjan Sycara, Katia Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA SUNY Stony Brook Dept Mech Engn Stony Brook NY 11794 USA

In this paper, we present provably-good distributed task assignment algorithms for a heterogeneous multi-robot system, in which the tasks form disjoint groups and there are constraints on the number of tasks a robot can do (both within the overall mission and within each task group). Each robot obtains a payoff (or incurs a cost) for each task and the overall objective for task allocation is to maximize (minimize) the total payoff (cost) of the robots. In general, existing algorithms for task allocation either assume that tasks are independent or do not provide performance guarantee for the situation, in which task constraints exist. We present a distributed algorithm to provide an almost optimal solution for our problem. The key aspect of our distributed algorithm is that the overall objective is (almost) maximized by each robot maximizing its own objective iteratively (using a modified payoff function based on an auxiliary variable, called price of a task). Our distributed algorithm is polynomial in the number of tasks, as well as the number of robots.

关键词： auction algorithm distributed algorithm multirobot task assignment task allocation

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Max-product for maximum weight matching: Convergence, correctness, and LP duality

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IEEE TRANSACTIONS ON INFORMATION THEORY 2008年第3期54卷 1241-1251页

作者： Bayati, Mobsen Shah, Devavrat Sharma, Mayank Microsoft Res Redmond WA 98052 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA

Max-product "belief propagation" (BP) is an iterative, message-passing algorithm for finding the maximum a posteriori (MAP) assignment of a discrete probability distribution specified by a graphical model. Despite the spectacular success of the algorithm in many application areas such as iterative decoding and combinatorial optimization, which involve graphs with many cycles, theoretical results about both the correctness and convergence of the algorithm are known in only a few cases (see Section I for references). In this paper, we will prove the correctness and convergence of max-product for finding the maximum weight matching (MWM) in bipartite graphs. Even though the underlying graph of the MWM problem has many cycles, somewhat surprisingly we show that the max-product algorithm converges to the correct MWM as long as the MWM is unique. We provide a bound on the number of iterations required and show that for a graph of size n, the computational cost of the algorithm scales as O(n(3)), which is the same as the computational cost of the best known algorithms for finding the MWM. We also provide an interesting relation between the dynamics of the max-product algorithm and the auction algorithm, which is a well-known distributed algorithm for solving the MWM problem.

关键词： auction algorithm belief propagation (BP) distributed optimization linear programming Markov random fields maximum weight matching (MWM) max-product algorithm message-passing algorithms min-sum algorithm

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A new efficient algorithm for optimal assignment of smart weapons to targets

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第10期58卷 1965-1969页

作者： Bogdanowicz, Zbigniew R. Armament Res Dev & Engn Ctr Picatinny Arsenal NJ 07806 USA

In the modem battlefields smart weapons inherently rely on the sensors. The benefit of assigning a given weapon to a target often depends on the pre-assigned sensor. In this paper we present an efficient algorithm to optimally assign sensors and weapons to targets. This algorithm is derived from the well-known auction algorithm, and it is named as Swt-opt. We prove that Swt-opt converges to an optimal solution. Published by Elsevier Ltd

关键词： Optimal assignment Weapon-target pairing Sensor-target pairing auction algorithm Discrete optimization

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THE INVISIBLE HAND algorithm - SOLVING THE ASSIGNMENT PROBLEM WITH STATISTICAL PHYSICS

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NEURAL NETWORKS 1994年第3期7卷 477-490页

作者： KOSOWSKY, JJ YUILLE, AL Harvard University USA

We propose a novel method for solving the assignment problem using techniques adapted from statistical physics. We derive a convex effective energy function whose unique minimum corresponds to the optimal assignment. Steepest descent results in a continuous-time dynamical system that is guaranteed to converge arbitrarily close to the optimal solution. Our algorithm has an appealing economic interpretation and has very interesting connections to the discrete auction algorithm proposed by Bertsekas. We also derive an alternative discrete algorithm for minimizing the effective energy based on a theorem by Sinkhorn.

关键词： ASSIGNMENT PROBLEM STATISTICAL PHYSICS auction algorithm INTERIOR POINT METHOD OPTIMIZATION NEURAL NETWORKS

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The vehicle rescheduling problem: Model and algorithms

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NETWORKS 2007年第3期50卷 211-229页

作者： Li, Jing-Quan Mirchandani, Pitu B. Borenstein, Denis Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Univ Fed Rio Grande do Sul Sch Management BR-90010460 Porto Alegre RS Brazil

When a vehicle on a scheduled trip breaks down, one or more vehicles need to be rescheduled to serve the customers on that trip with minimum operating and delay costs. The problem of reassigning vehicles in real-time to this cut trip as well as to other scheduled trips with given starting and ending times, is referred to as the vehicle rescheduling problem (VRSP). This paper considers modeling, algorithmic, and computational aspects of the single-depot VRSP. The paper formulates a model for this problem and develops several fast algorithms to solve it, including parallel synchronous auction algorithms. The concept of the common feasible network (CFN) is introduced to find a good set of initial "prices" for speeding up the auction algorithm. Computational experiments on randomly generated problems are described. Computational results show that, for small problems, all of the developed algorithms demonstrate very good computational performances. For large problems, parallel CFN-based auction algorithms provide the optimal solution with much smaller computation times. (c) 2007 Wiley Periodicals, Inc.

关键词： vehicle scheduling rescheduling auction algorithm parallel processing

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Joint Power Allocation and User Association Optimization for IRS-Assisted mmWave Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第1期21卷 577-590页

作者： Zhao, Dan Lu, Hancheng Wang, Yazheng Sun, Huan Gui, Yongqiang Chinese Acad Sci Univ Sci & Technol China Key Lab Wireless Opt Commun Hefei 230027 Peoples R China Huawei Technol Co Ltd Wireless Technol Lab Shanghai 200120 Peoples R China

Intelligent reflecting surface (IRS) is a potential technology to build programmable wireless environment in future communication systems. In this paper, we consider a multi-IRS-assisted multi-base station (multi-BS) multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to extend mmWave signal coverage to blind spots. Considering the impact of IRS on user association in multi-BS mmWave systems, we formulate a sum rate maximization problem by jointly optimizing passive beamforming at IRS, power allocation and user association. This leads to an intractable non-convex problem, for which to tackle we propose a computationally affordable iterative algorithm, capitalizing on alternating optimization, sequential fractional programming (SFP) and forward-reverse auction (FRA). In particular, passive beamforming at IRS is optimized by utilizing the SFP method, power allocation is solved through means of standard convex optimization method, and user association is handled by the network optimization based FRA algorithm. Simulation results demonstrate that the proposed algorithm can achieve significant performance gains, e.g., it can provide up to 147% higher sum rate compared with the benchmark and 116% higher energy efficiency compared with amplify-and-forward relay.

关键词： Optimization Resource management Wireless communication Relays MIMO communication Array signal processing Channel estimation Intelligent reflecting surface millimeter wave user association beamforming power allocation auction algorithm

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Homotopic Approach for Robot Allocation Optimization Coupled With Path Constraints

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IEEE ROBOTICS AND AUTOMATION LETTERS 2020年第1期5卷 88-95页

作者： Yao, Weiran Qi, Naiming Liu, Yongbei Xu, Song Du, Desong Harbin Inst Technol Sch Astronaut Harbin 150001 Heilongjiang Peoples R China Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada

This letter investigates a special task allocation problem with constraints of path planning. The path planning process involved is defined as the follow-up step of task allocation. The allocation problem is coupled with path constraints, which will affect the utility of allocation solution. A homotopic approach featuring two homotopy primitives is proposed to resolve the complexity caused by the coupling between task allocation and path planning. Reference paths generated by the homotopic approach are transformed to desired task bundles for robots via transformation functions. A distributed task allocation architecture with market mechanism is employed to make consensus on task allocation solution in multi-robot. Simulation results validate the performance of the proposed methods comparing with conventional allocation methods.

关键词： Task analysis Resource management Robot kinematics Path planning Schedules Optimization Robot task allocation path constraints homotopic approach auction algorithm

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Distributed optimization of P2P live streaming overlays

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COMPUTING 2012年第8-10期94卷 621-647页

作者： Payberah, Amir H. Dowling, Jim Rahimain, Fatemeh Haridi, Seif SICS S-16429 Kista Sweden

Peer-to-peer live media streaming over the Internet is becoming increasingly more popular, though it is still a challenging problem. Nodes should receive the stream with respect to intrinsic timing constraints, while the overlay should adapt to the changes in the network and the nodes should be incentivized to contribute their resources. In this work, we meet these contradictory requirements simultaneously, by introducing a distributed market model to build an efficient overlay for live media streaming. Using our market model, we construct two different overlay topologies, tree-based and mesh-based, which are the two dominant approaches to the media distribution. First, we build an approximately minimal height multiple-tree data dissemination overlay, called Sepidar. Next, we extend our model, in GLive, to make it more robust in dynamic networks by replacing the tree structure with a mesh. We show in simulation that the mesh-based overlay outperforms the multiple-tree overlay. We compare the performance of our two systems with the state-of-the-art NewCoolstreaming, and observe that they provide better playback continuity and lower playback latency than that of NewCoolstreaming under a variety of experimental scenarios. Although our distributed market model can be run against a random sample of nodes, we improve its convergence time by executing it against a sample of nodes taken from the Gradient overlay. The evaluations show that the streaming overlays converge faster when our market model works on top of the Gradient overlay.

关键词： P2P live streaming The Gradient overlay Distributed algorithms Market-based algorithms auction algorithm

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Optimization of Weapon-Target Pairings Based on Kill Probabilities

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IEEE TRANSACTIONS ON CYBERNETICS 2013年第6期43卷 1835-1844页

作者： Bogdanowicz, Zbigniew R. Tolano, Antony Patel, Ketula Coleman, Norman P. ARDEC Picatinny Arsenal NJ 07806 USA

In this paper, we present a novel optimization algorithm for assigning weapons to targets based on desired kill probabilities. For the given weapons, targets, and desired kill probabilities, our optimization algorithm assigns weapons to targets that satisfy the desired kill probabilities and minimize the overkill. The minimization of overkill assures that any proper subset of the weapons assigned to a target results in a kill probability that is less than the desired kill probability on such a target. Computational results for up to 120 weapons and 120 targets indicate that the performance of this algorithm yields an average improvement in quality of solutions of 26.8% over the greedy algorithms, whereas execution times remained on the order of milliseconds.

关键词： auction algorithm collaborative engagement decision trees discrete optimization kill probability parallel algorithms scalable lethality weapon-target assignment

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