121 research outputs found

    Multicast Multigroup Precoding and User Scheduling for Frame-Based Satellite Communications

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    The present work focuses on the forward link of a broadband multibeam satellite system that aggressively reuses the user link frequency resources. Two fundamental practical challenges, namely the need to frame multiple users per transmission and the per-antenna transmit power limitations, are addressed. To this end, the so-called frame-based precoding problem is optimally solved using the principles of physical layer multicasting to multiple co-channel groups under per-antenna constraints. In this context, a novel optimization problem that aims at maximizing the system sum rate under individual power constraints is proposed. Added to that, the formulation is further extended to include availability constraints. As a result, the high gains of the sum rate optimal design are traded off to satisfy the stringent availability requirements of satellite systems. Moreover, the throughput maximization with a granular spectral efficiency versus SINR function, is formulated and solved. Finally, a multicast-aware user scheduling policy, based on the channel state information, is developed. Thus, substantial multiuser diversity gains are gleaned. Numerical results over a realistic simulation environment exhibit as much as 30% gains over conventional systems, even for 7 users per frame, without modifying the framing structure of legacy communication standards.Comment: Accepted for publication to the IEEE Transactions on Wireless Communications, 201

    Fair Cellular Throughput Optimization with the Aid of Coordinated Drones

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    Comunicación presentada en IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (Paris, 29 April-2 May 2019)The interest on flexible air-to-ground channels from aerial base stations to enhance users access by seeking good line-of-sight connectivity from the air has increased in the past years. In this paper, we propose a deployable analytical framework for the 3-D placement of a fleet of coordinated drone relay stations to optimize network capacity according to α-fairness metrics. We formulate a mixed-integer non-convex program, which results to be intractable. Therefore, we design a near-optimal heuristic that can solve the problem in real-time applications. We assess the performance of our proposal by simulation, using a realistic urban topology, and study pros and cons of using drone relay stations in both static and dynamic scenarios, when popular events gather masses of users in limited areas

    Quasi-offline fair scheduling in third generation wireless data networks

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    In 3G wireless data networks, network operators would like to balance system throughput while serving users fairly. This is achieved through the use of fair scheduling. However, this approach provides non-Pareto optimal bandwidth allocation when considering a network as a whole. In this paper an optimal offline algorithm that is based on the decomposition result for a double stochastic matrix by Birkhoff and von Neumann is proposed. A utility max-min fairness is suggested for the derivation of a double stochastic matrix. Using a numerical experiment, new approach improves the fairness objective and is close to the optimal solution

    Fairness Time-Slot Allocation and Scheduling with QoS Guarantees in Multihop WiMAX Mesh Networks

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    The WiMAX technology has been defined to provide high throughput over long distance communications and support the quality of service (QoS) control applied on different applications. This paper studies the fairness time-slot allocation and scheduling problem for enhancing throughput and guaranteeing QoS in multihop WiMAX mesh networks. For allocating time slots to multiple subscribe stations (SSs), fairness is a key concern. The notion of max-min fairness is applied as our metric to define the QoS-based max-min fair scheduling problem for maximizing the minimum satisfaction ratio of each SS. We formulate an integer linear programming (ILP) model to provide an optimal solution on small-scale networks. For large-scale networks, several heuristic algorithms are proposed for better running time and scalability. The performance of heuristic algorithms is compared with previous methods in the literatures. Experimental results show that the proposed algorithms are better in terms of QoS satisfaction ratio and throughput

    Resource Management in Cloud-based Radio Access Networks: a Distributed Optimization Perspective

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    University of Minnesota Ph.D. dissertation. 2015. Major: Electrical Engineering. Advisor: Zhi-Quan Luo. 1 computer file (PDF); ix, 136 pages.In this dissertation, we consider the base station (BS) and the resource management problems for the cloud-based radio access network (C-RAN). The main difference of the envisioned future 5G network architecture is the adoption of multi-tier BSs to extend the coverage of the existing cellular BSs. Each of the BS is connected to the multi-hop backhaul network with limited bandwidth. For provisioning the network, the cloud centers have been proposed to serve as the control centers. These differences give rise to many practical challenges. The main focus of this dissertation is the distributed strategy across the cloud centers. First, we show that by jointly optimizing the transceivers and determining the active set of BSs, high system resource utilization can be achieved with only a small number of BSs. In particular, we provide efficient distributed algorithms for such joint optimization problem, under the following two common design criteria: i) minimization of the total power consumption at the BSs, and ii) maximization of the system spectrum efficiency. In both cases, we introduce a nonsmooth regularizer to facilitate the activation of the most appropriate BSs, and the algorithms are, respectively, developed with Alternating Direction Method of Multipliers (ADMM) and weighted minimum mean square error (WMMSE) algorithm. In the second part, we further explicitly consider the backhaul limitation issues. We propose an efficient algorithm for joint resource allocation across the wireless links and the flow control over the entire network. The algorithm, which maximizes the utility function of the rates among all the transmitted commodities, is based on a decomposition approach leverages both the ADMM and the WMMSE algorithms. This algorithm is shown to be easily parallelizable within cloud centers and converges globally to a stationary solution. Lastly, since ADMM has been popular for solving large-scale distributed convex optimization, we further consider the issues of the network synchronization across the cloud centers. We propose an ADMM-type implementation that can handle a specific form of asynchronism based on the so-called BSUM-M algorithm, a new variant of ADMM. We show that the proposed algorithm converges to the global optimal solution

    Exact Resource Allocation for Fair Wireless Relay

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    In relay-enabled cellular networks, the intertwined nature of network agents calls for complex schemes to allocate wireless resources. Resources need to be distributed among mobile users while considering how relay resources are allocated, and constrained by the traffic rate achievable by base stations and over backhaul links. In this work, we derive a resource allocation scheme that achieves max-min fairness across mobile users. Furthermore, the optimal allocation is found with linear complexity with respect to the number of mobile users and relays

    Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks

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    Understanding the optimal usage of fluctuating renewable energy in Wireless Sensor Networks (WSNs) is complex. Lexicographic Max-min (LM) rate allocation is a good solution, but is non-trivial for multi-hop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and off-line, suffering from low scalability and large computational complexity; typically solving O(N2 ) linear programming problems for N-node WSNs. This paper presents the first optimal distributed solution to this problem with much lower complexity. We apply it to Solar Powered WSNs (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MicaZ motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions
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