Nonconcave Utility Maximization in Locally Coupled Systems, With Applications to Wireless and Wireline Networks

Borst, Sem; Markakis, Mihalis G.; Saniee, Iraj

doi:10.1109/tnet.2013.2257181

Cited by 29 publications

(35 citation statements)

References 21 publications

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“…More relevant to our work, [12] considered distributed power control in OFDMA-based systems using Gibbs sampling. A similar radio access system model as [12] is adopted in our work whereas in addition to power control, we also consider BS activation and user scheduling which changes the problem.…”

Section: B Related Workmentioning

confidence: 99%

“…While include user association in the model is straightforward as shown in [12], for the sake of clarity, we make some practical assumptions regarding user association that simplifies the model. First, we consider a standard OFDMA system without carrier aggregation capability presented in LTE-Advanced [14].…”

Section: User Associationmentioning

confidence: 99%

“…In addition, we would like to minimize the long-term energy cost of the system as defined in (12). Hence, following the drift-plus-penalty approach [15], we add the expected energy cost of the system to (14), which results in the following driftplus-penalty expression,…”

Section: Online Controller Designmentioning

confidence: 99%

“…Similar to [12], we assume that the transmission power of a BS can vary from 0 to P max in small discrete steps of size δ, i.e., 0, δ, 2δ, . .…”

Section: A Power Allocationmentioning

confidence: 99%

“…This way, the neighborhood of each node forms a clique [12]. In addition, the net utility (17) can be expressed as the summation of a net utilities over the set of cliques Q(G), That is, for f ∈ F we have…”

Section: A Power Allocationmentioning

confidence: 99%

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Online algorithms for energy cost minimization in cellular networks

Abbasi

Ghaderi

2014

2014 IEEE 22nd International Symposium of Quality of Service (IWQoS)

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Abstract-Dynamic base station activation and transmission power control are the key mechanisms to reduce energy consumption in cellular networks. In this work, we consider employing these methods for the purpose of minimizing long-term energy cost in cellular networks. Based on the two-timescale Lyapunov optimization technique, we formulate an online control problem to ensure achieving minimal energy cost while stabilizing use queues. While the control problem can be solved in a centralized manner, we limit our attention to distributed solutions which are highly attractive in the design of next generation mobile networks. Due to the combinatorial nature of the problem and the complex relation of achievable rates to interfering signals, the problem is non-convex. Consequently, conventional duality methods cannot be employed to achieve the distributed solution. Thus, we design a distributed solution for the problem based on Gibbs sampling method. The proposed algorithm can be implemented in a fully distributed manner, does not depend on the convexity or continuity of the energy cost functions, and guarantees solution optimality. Numerical results are provided to demonstrate the behavior of the solution in some example network scenarios.

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Section: B Related Workmentioning

confidence: 99%

Section: User Associationmentioning

confidence: 99%

Section: Online Controller Designmentioning

confidence: 99%

“…Similar to [12], we assume that the transmission power of a BS can vary from 0 to P max in small discrete steps of size δ, i.e., 0, δ, 2δ, . .…”

Section: A Power Allocationmentioning

confidence: 99%

Section: A Power Allocationmentioning

confidence: 99%

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Online algorithms for energy cost minimization in cellular networks

Abbasi

Ghaderi

2014

2014 IEEE 22nd International Symposium of Quality of Service (IWQoS)

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A Fully Distributed Learning Algorithm for Power Allocation in Heterogeneous Networks

2015

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A fully distributed learning algorithm for power allocation in heterogeneous networks

2019

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In this work, we present a fully distributed Learning algorithm for power allocation in HetNets, referred to as the FLAPH, that reaches the global optimum given by the total social welfare. Using a mix of macro and femto base stations, we discuss opportunities to maximize users global throughput. We prove the convergence of the algorithm and compare its performance with the well-established Gibbs and Max-logit algorithms which ensure convergence to the global optimum. Algorithms are compared in terms of computational complexity, memory space, and time convergence.

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Nonconcave Utility Maximization in Locally Coupled Systems, With Applications to Wireless and Wireline Networks

Cited by 29 publications

References 21 publications

Online algorithms for energy cost minimization in cellular networks

Online algorithms for energy cost minimization in cellular networks

A Fully Distributed Learning Algorithm for Power Allocation in Heterogeneous Networks

A fully distributed learning algorithm for power allocation in heterogeneous networks

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