3,724 research outputs found
Optimality of Treating Interference as Noise: A Combinatorial Perspective
For single-antenna Gaussian interference channels, we re-formulate the
problem of determining the Generalized Degrees of Freedom (GDoF) region
achievable by treating interference as Gaussian noise (TIN) derived in [3] from
a combinatorial perspective. We show that the TIN power control problem can be
cast into an assignment problem, such that the globally optimal power
allocation variables can be obtained by well-known polynomial time algorithms.
Furthermore, the expression of the TIN-Achievable GDoF region (TINA region) can
be substantially simplified with the aid of maximum weighted matchings. We also
provide conditions under which the TINA region is a convex polytope that relax
those in [3]. For these new conditions, together with a channel connectivity
(i.e., interference topology) condition, we show TIN optimality for a new class
of interference networks that is not included, nor includes, the class found in
[3].
Building on the above insights, we consider the problem of joint link
scheduling and power control in wireless networks, which has been widely
studied as a basic physical layer mechanism for device-to-device (D2D)
communications. Inspired by the relaxed TIN channel strength condition as well
as the assignment-based power allocation, we propose a low-complexity
GDoF-based distributed link scheduling and power control mechanism (ITLinQ+)
that improves upon the ITLinQ scheme proposed in [4] and further improves over
the heuristic approach known as FlashLinQ. It is demonstrated by simulation
that ITLinQ+ provides significant average network throughput gains over both
ITLinQ and FlashLinQ, and yet still maintains the same level of implementation
complexity. More notably, the energy efficiency of the newly proposed ITLinQ+
is substantially larger than that of ITLinQ and FlashLinQ, which is desirable
for D2D networks formed by battery-powered devices.Comment: A short version has been presented at IEEE International Symposium on
Information Theory (ISIT 2015), Hong Kon
Green Networking in Cellular HetNets: A Unified Radio Resource Management Framework with Base Station ON/OFF Switching
In this paper, the problem of energy efficiency in cellular heterogeneous
networks (HetNets) is investigated using radio resource and power management
combined with the base station (BS) ON/OFF switching. The objective is to
minimize the total power consumption of the network while satisfying the
quality of service (QoS) requirements of each connected user. We consider the
case of co-existing macrocell BS, small cell BSs, and private femtocell access
points (FAPs). Three different network scenarios are investigated, depending on
the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs,
and HetNets with semi-closed FAPs. A unified framework is proposed to
simultaneously allocate spectrum resources to users in an energy efficient
manner and switch off redundant small cell BSs. The high complexity dual
decomposition technique is employed to achieve optimal solutions for the
problem. A low complexity iterative algorithm is also proposed and its
performances are compared to those of the optimal technique. The particularly
interesting case of semi-closed FAPs, in which the FAPs accept to serve
external users, achieves the highest energy efficiency due to increased degrees
of freedom. In this paper, a cooperation scheme between FAPs and mobile
operator is also investigated. The incentives for FAPs, e.g., renewable energy
sharing and roaming prices, enabling cooperation are discussed to be considered
as a useful guideline for inter-operator agreements.Comment: 15 pages, 9 Figures, IEEE Transactions on Vehicular Technology 201
Precoding by Pairing Subchannels to Increase MIMO Capacity with Discrete Input Alphabets
We consider Gaussian multiple-input multiple-output (MIMO) channels with
discrete input alphabets. We propose a non-diagonal precoder based on the
X-Codes in \cite{Xcodes_paper} to increase the mutual information. The MIMO
channel is transformed into a set of parallel subchannels using Singular Value
Decomposition (SVD) and X-Codes are then used to pair the subchannels. X-Codes
are fully characterized by the pairings and a real rotation matrix
for each pair (parameterized with a single angle). This precoding structure
enables us to express the total mutual information as a sum of the mutual
information of all the pairs. The problem of finding the optimal precoder with
the above structure, which maximizes the total mutual information, is solved by
{\em i}) optimizing the rotation angle and the power allocation within each
pair and {\em ii}) finding the optimal pairing and power allocation among the
pairs. It is shown that the mutual information achieved with the proposed
pairing scheme is very close to that achieved with the optimal precoder by Cruz
{\em et al.}, and is significantly better than Mercury/waterfilling strategy by
Lozano {\em et al.}. Our approach greatly simplifies both the precoder
optimization and the detection complexity, making it suitable for practical
applications.Comment: submitted to IEEE Transactions on Information Theor
Decentralized algorithm of dynamic task allocation for a swarm of homogeneous robots
The current trends in the robotics field have led to the development of large-scale swarm robot systems, which are deployed for complex missions. The robots in these systems must communicate and interact with each other and with their environment for complex task processing. A major problem for this trend is the poor task planning mechanism, which includes both task decomposition and task allocation. Task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize the cost while satisfying operational constraints. Task allocation mechanism must be run by each robot, which integrates the swarm whenever it senses a change in the environment to make sure the robot is assigned to the most appropriate task, if not, the robot should reassign itself to its nearest task. The main contribution in this thesis is to maximize the overall efficiency of the system by minimizing the total time needed to accomplish the dynamic task allocation problem. The near-optimal allocation schemes are found using a novel hybrid decentralized algorithm for a dynamic task allocation in a swarm of homogeneous robots, where the number of the tasks is more than the robots present in the system. This hybrid approach is based on both the Simulated Annealing (SA) optimization technique combined with the Discrete Particle Swarm Optimization (DPSO) technique. Also, another major contribution in this thesis is the formulation of the dynamic task allocation equations for the homogeneous swarm robotics using integer linear programming and the cost function and constraints are introduced for the given problem. Then, the DPSO and SA algorithms are developed to accomplish the task in a minimal time. Simulation is implemented using only two test cases via MATLAB. Simulation results show that PSO exhibits a smaller and more stable convergence characteristics and SA technique owns a better quality solution. Then, after developing the hybrid algorithm, which combines SA with PSO, simulation instances are extended to include fifteen more test cases with different swarm dimensions to ensure the robustness and scalability of the proposed algorithm over the traditional PSO and SA optimization techniques. Based on the simulation results, the hybrid DPSO/SA approach proves to have a higher efficiency in both small and large swarm sizes than the other traditional algorithms such as Particle Swarm Optimization technique and Simulated Annealing technique. The simulation results also demonstrate that the proposed approach can dislodge a state from a local minimum and guide it to the global minimum. Thus, the contributions of the proposed hybrid DPSO/SA algorithm involve possessing both the pros of high quality solution in SA and the fast convergence time capability in PSO. Also, a parameters\u27 selection process for the hybrid algorithm is proposed as a further contribution in an attempt to enhance the algorithm efficiency because the heuristic optimization techniques are very sensitive to any parameter changes. In addition, Verification is performed to ensure the effectiveness of the proposed algorithm by comparing it with results of an exact solver in terms of computational time, number of iterations and quality of solution. The exact solver that is used in this research is the Hungarian algorithm. This comparison shows that the proposed algorithm gives a superior performance in almost all swarm sizes with both stable and small execution time. However, it also shows that the proposed hybrid algorithm\u27s cost values which is the distance traveled by the robots to perform the tasks are larger than the cost values of the Hungarian algorithm but the execution time of the hybrid algorithm is much better. Finally, one last contribution in this thesis is that the proposed algorithm is implemented and extensively tested in a real experiment using a swarm of 4 robots. The robots that are used in the real experiment called Elisa-III robots
A Two-Step Optimisation Method for Dynamic Weapon Target Assignment Problem
International audienceno abstrac
A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks
This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability
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