Simultaneous Routing and Resource Allocation Via Dual Decomposition

Xiao, Lin; Johansson, Mikael; Boyd, Stephen

doi:10.1109/tcomm.2004.831346

Cited by 497 publications

(452 citation statements)

References 23 publications

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“…Following straightforward algebra from [22], [23], it can be shown that the optimal solution of (7) can be obtained by using the following iterative procedure. First of all, we broadcast an initial value λ 0 of the dual variable to all zones.…”

Section: Bargain-based Airflow Allocation Strategymentioning

confidence: 99%

Distributed flexibility characterization and resource allocation for multi-zone commercial buildings in the smart grid

Lian

Kalsi

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-The HVAC (Heating, Ventilation, and AirConditioning) system of commercial buildings is a complex system with a large number of dynamically interacting components. In particular, the thermal dynamics of each zone are coupled with those of its neighboring zones. In this paper, we study an agent-based approach to model and control commercial building HVAC system for providing ancillary services to the power grid. In the multi-agent-building-system (MABS), individual zones are modeled as agents that can communicate, interact, and negotiate with one another to achieve a common objective. We first propose a distributed characterization method on the aggregate airflow (and thus fan power) flexibility that the HVAC system can provide to the ancillary service market. A Nash-bargaining-based airflow allocation strategy is then proposed to track a dispatch signal while respecting the preference and flexibility of individual zones. Moreover, we devise a distributed algorithm to obtain the Nash bargaining solution via dual decomposition. Numerical simulations illustrate that the proposed distributed protocols are much more scalable than centralized approaches especially when the system becomes larger and more complex.

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Section: Bargain-based Airflow Allocation Strategymentioning

confidence: 99%

Distributed flexibility characterization and resource allocation for multi-zone commercial buildings in the smart grid

Lian

Kalsi

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

show abstract

“…First, we calculate the value of client coverage as dictated by specific AP locations and their configuration, ground terrain, and environmental data. We then calculate the value of network flow according to the Simultaneous Routing and Resource Allocation (SRRA) techniques of Xiao et al (2004). We combine these two subproblems to formulate SRRA1C, a nondifferentiable, nonconvex, nonlinear optimization problem.…”

Section: Description Of Problemmentioning

confidence: 99%

“…We build on this general idea to calculate the value of client coverage, and use the DIRECT algorithm similarly for outdoor environments. Xiao et al (2004) solve the SRRA problem via dual decomposition to identify optimal traffic routes and allocation of AP transmission power within a wireless network. As noted above, we use SRRA to calculate the value of network flow.…”

Section: Description Of Problemmentioning

confidence: 99%

“…In general, the wireless nature of this network means that transmission along any of the backhaul arcs is possible, although distance, terrain, and background interference will dictate which of these arcs has the transmission capacity to be useful. Following Xiao et al (2004), we assume APs are not subject to self-jamming or interference from other APs.…”

Section: Fast Effective Transmitter Placement In Wireless Mesh Networkmentioning

confidence: 99%

“…This formula establishes a theoretical upper bound on transmission capacity in bits per second (bps). Following Xiao et al (2004), the capacity from node i to node j is:…”

Section: Calculating Network Flowmentioning

confidence: 99%

See 2 more Smart Citations

Fast, Effective Transmitter Placement in Wireless Mesh Networks

Nicholas¹,

Alderson²

2012

Military Oper Res

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W ireless mesh networks are systems of interconnected wireless access points that provide digital services to client devices via radio transmission. We consider the challenges of a communications planner who must quickly design a wireless mesh network, as might be expected during combat operations or in support of humanitarian assistance and disaster relief operations. We seek a network that maximizes client coverage area subject to constraints on network service, the technical characteristics of the available access points, and radio propagation over terrain. We create a nondifferentiable, nonconvex, nonlinear optimization problem and use a sampling algorithm to quickly find good solutions. We validate our formulation and solutions via numerical experiments and several field tests, and we demonstrate that our technique can generate network topologies capable of functioning in real-world scenarios.We construct a corresponding decision support tool that allows a communications planner to design working wireless mesh network topologies quickly, with no guesswork, and requiring very little expertise. The tool runs on a laptop, supports virtually any type of access point, uses terrain information freely downloadable from the Internet, and does not require any additional software or solver licenses.

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Space‐Time Processing for MIMO Communications

Gershman

Sidiropoulos²

2005

222

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Simultaneous Routing and Resource Allocation Via Dual Decomposition

Cited by 497 publications

References 23 publications

Distributed flexibility characterization and resource allocation for multi-zone commercial buildings in the smart grid

Distributed flexibility characterization and resource allocation for multi-zone commercial buildings in the smart grid

Fast, Effective Transmitter Placement in Wireless Mesh Networks

Space‐Time Processing for MIMO Communications

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