3,273 research outputs found
On the Spectral Efficiency and Fairness in Full-Duplex Cellular Networks
To increase the spectral efficiency of wireless networks without requiring
full-duplex capability of user devices, a potential solution is the recently
proposed three-node full-duplex mode. To realize this potential, networks
employing three-node full-duplex transmissions must deal with self-interference
and user-to-user interference, which can be managed by frequency channel and
power allocation techniques. Whereas previous works investigated either
spectral efficient or fair mechanisms, a scheme that balances these two metrics
among users is investigated in this paper. This balancing scheme is based on a
new solution method of the multi-objective optimization problem to maximize the
weighted sum of the per-user spectral efficiency and the minimum spectral
efficiency among users. The mixed integer non-linear nature of this problem is
dealt by Lagrangian duality. Based on the proposed solution approach, a
low-complexity centralized algorithm is developed, which relies on large scale
fading measurements that can be advantageously implemented at the base station.
Numerical results indicate that the proposed algorithm increases the spectral
efficiency and fairness among users without the need of weighting the spectral
efficiency. An important conclusion is that managing user-to-user interference
by resource assignment and power control is crucial for ensuring spectral
efficient and fair operation of full-duplex networks.Comment: 6 pages, 4 figures, accepted in IEEE ICC 2017. arXiv admin note: text
overlap with arXiv:1603.0067
Spatial Resources Optimization in Distributed MIMO Networks with Limited Data Sharing
Wireless access through a large distributed network of low-complexity
infrastructure nodes empowered with cooperation and coordination capabilities,
is an emerging radio architecture, candidate to deal with the mobile data
capacity crunch. In the 3GPP evolutionary path, this is known as the Cloud-RAN
paradigm for future radio. In such a complex network, distributed MIMO
resources optimization is of paramount importance, in order to achieve capacity
scaling. In this paper, we investigate efficient strategies towards optimizing
the pairing of access nodes with users as well as linear precoding designs for
providing fair QoS experience across the whole network, when data sharing is
limited due to complexity and overhead constraints. We propose a method for
obtaining the exact optimal spatial resources allocation solution which can be
applied in networks of limited scale, as well as an approximation algorithm
with bounded polynomial complexity which can be used in larger networks. The
particular algorithm outperforms existing user-oriented clustering techniques
and achieves quite high quality-of-service levels with reasonable complexity.Comment: submitted to Globecom 2013 - Wireless Communications Symposiu
Stability and fairness in models with a multiple membership
This article studies a model of coalition formation for the joint production (and finance) of public projects, in which agents may belong to multiple coalitions. We show that, if projects are divisible, there always exists a stable (secession-proof) structure, i.e., a structure in which no coalition would reject a proposed arrangement. When projects are in- divisible, stable allocations may fail to exist and, for those cases, we resort to the least core in order to estimate the degree of instability. We also examine the compatibility of stability and fairness on metric environments with indivisible projects. To do so, we explore, among other things, the performance of several well-known solutions (such as the Shapley value, the nucleolus, or the Dutta-Ray value) in these environments.stability, fairness, membership, coalition formation
MODELS AND SOLUTION ALGORITHMS FOR EQUITABLE RESOURCE ALLOCATION IN AIR TRAFFIC FLOW MANAGEMENT
Population growth and economic development lead to increasing demand for travel and pose mobility challenges on capacity-limited air traffic networks. The U.S. National Airspace System (NAS) has been operated near the capacity, and air traffic congestion is expected to remain as a top concern for the related system operators, passengers and airlines. This dissertation develops a number of model reformulations and efficient solution algorithms to address resource allocation problems in air traffic flow management, while explicitly accounting for equitable objectives in order to encourage further collaborations by different stakeholders.
This dissertation first develops a bi-criteria optimization model to offload excess demand from different competing airlines in the congested airspace when the predicted traffic demand is higher than available capacity. Computationally efficient network flow models with side constraints are developed and extensively tested using datasets obtained from the Enhanced Traffic Management System (ETMS) database (now known as the Traffic Flow Management System). Representative Pareto-optimal tradeoff frontiers are consequently generated to allow decision-makers to identify best-compromising solutions based on relative weights and systematical considerations of both efficiency and equity.
This dissertation further models and solves an integrated flight re-routing problem on an airspace network. Given a network of airspace sectors with a set of waypoint entries and a set of flights belonging to different air carriers, the optimization model aims to minimize the total flight travel time subject to a set of flight routing equity, operational and safety requirements. A time-dependent network flow programming formulation is proposed with stochastic sector capacities and rerouting equity for each air carrier as side constraints. A Lagrangian relaxation based method is used to dualize these constraints and decompose the original complex problem into a sequence of single flight rerouting/scheduling problems.
Finally, within a multi-objective utility maximization framework, the dissertation proposes several practically useful heuristic algorithms for the long-term airport slot assignment problem. Alternative models are constructed to decompose the complex model into a series of hourly assignment sub-problems. A new paired assignment heuristic algorithm is developed to adapt the round robin scheduling principle for improving fairness measures across different airlines. Computational results are presented to show the strength of each proposed modeling approach
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