Matches in SemOpenAlex for { <https://semopenalex.org/work/W4299933332> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W4299933332 abstract "We are motivated by the need for impromptu (or as-you-go) deployment of multihop wireless networks, by human agents or robots; the agent moves along a line, makes wireless link quality measurements at regular intervals, and makes on-line placement decisions using these measurements. As a first step, we have formulated such deployment along a line as a sequential decision problem. In our earlier work, we proposed two possible deployment approaches: (i) the pure as-you-go approach where the deployment agent can only move forward, and (ii) the explore-forward approach where the deployment agent explores a few successive steps and then selects the best relay placement location. The latter was shown to provide better performance but at the expense of more measurements and deployment time, which makes explore-forward impractical for quick deployment by an energy constrained agent such as a UAV. Further, the deployment algorithm should not require prior knowledge of the parameters of the wireless propagation model. In [1] we, therefore, developed learning algorithms for the explore-forward approach. The current paper provides deploy-and-learn algorithms for the pure as-you-go approach. We formulate the sequential relay deployment problem as an average cost Markov decision process (MDP), which trades off among power consumption, link outage probabilities, and the number of deployed relay nodes. First we show structural results for the optimal policy. Next, by exploiting the special structure of the optimality equation and by using the theory of asynchronous stochastic approximation, we develop two learning algorithms that asymptotically converge to the set of optimal policies as deployment progresses. Numerical results show reasonably fast speed of convergence, and hence the model-free algorithms can be useful for practical, fast deployment of emergency wireless networks." @default.
- W4299933332 created "2022-10-03" @default.
- W4299933332 creator A5032924598 @default.
- W4299933332 creator A5066528816 @default.
- W4299933332 creator A5086971942 @default.
- W4299933332 date "2017-09-05" @default.
- W4299933332 modified "2023-10-16" @default.
- W4299933332 title "Asynchronous Stochastic Approximation Based Learning Algorithms for As-You-Go Deployment of Wireless Relay Networks along a Line" @default.
- W4299933332 doi "https://doi.org/10.48550/arxiv.1709.01566" @default.
- W4299933332 hasPublicationYear "2017" @default.
- W4299933332 type Work @default.
- W4299933332 citedByCount "0" @default.
- W4299933332 crossrefType "posted-content" @default.
- W4299933332 hasAuthorship W4299933332A5032924598 @default.
- W4299933332 hasAuthorship W4299933332A5066528816 @default.
- W4299933332 hasAuthorship W4299933332A5086971942 @default.
- W4299933332 hasBestOaLocation W42999333321 @default.
- W4299933332 hasConcept C105339364 @default.
- W4299933332 hasConcept C105795698 @default.
- W4299933332 hasConcept C106189395 @default.
- W4299933332 hasConcept C108037233 @default.
- W4299933332 hasConcept C111919701 @default.
- W4299933332 hasConcept C11413529 @default.
- W4299933332 hasConcept C120314980 @default.
- W4299933332 hasConcept C121332964 @default.
- W4299933332 hasConcept C126255220 @default.
- W4299933332 hasConcept C151319957 @default.
- W4299933332 hasConcept C159886148 @default.
- W4299933332 hasConcept C163258240 @default.
- W4299933332 hasConcept C2778156585 @default.
- W4299933332 hasConcept C31258907 @default.
- W4299933332 hasConcept C33923547 @default.
- W4299933332 hasConcept C41008148 @default.
- W4299933332 hasConcept C555944384 @default.
- W4299933332 hasConcept C62520636 @default.
- W4299933332 hasConcept C76155785 @default.
- W4299933332 hasConceptScore W4299933332C105339364 @default.
- W4299933332 hasConceptScore W4299933332C105795698 @default.
- W4299933332 hasConceptScore W4299933332C106189395 @default.
- W4299933332 hasConceptScore W4299933332C108037233 @default.
- W4299933332 hasConceptScore W4299933332C111919701 @default.
- W4299933332 hasConceptScore W4299933332C11413529 @default.
- W4299933332 hasConceptScore W4299933332C120314980 @default.
- W4299933332 hasConceptScore W4299933332C121332964 @default.
- W4299933332 hasConceptScore W4299933332C126255220 @default.
- W4299933332 hasConceptScore W4299933332C151319957 @default.
- W4299933332 hasConceptScore W4299933332C159886148 @default.
- W4299933332 hasConceptScore W4299933332C163258240 @default.
- W4299933332 hasConceptScore W4299933332C2778156585 @default.
- W4299933332 hasConceptScore W4299933332C31258907 @default.
- W4299933332 hasConceptScore W4299933332C33923547 @default.
- W4299933332 hasConceptScore W4299933332C41008148 @default.
- W4299933332 hasConceptScore W4299933332C555944384 @default.
- W4299933332 hasConceptScore W4299933332C62520636 @default.
- W4299933332 hasConceptScore W4299933332C76155785 @default.
- W4299933332 hasLocation W42999333321 @default.
- W4299933332 hasLocation W42999333322 @default.
- W4299933332 hasOpenAccess W4299933332 @default.
- W4299933332 hasPrimaryLocation W42999333321 @default.
- W4299933332 hasRelatedWork W1996924724 @default.
- W4299933332 hasRelatedWork W2084050328 @default.
- W4299933332 hasRelatedWork W2093073854 @default.
- W4299933332 hasRelatedWork W2156992384 @default.
- W4299933332 hasRelatedWork W2161367706 @default.
- W4299933332 hasRelatedWork W2288717737 @default.
- W4299933332 hasRelatedWork W2364921833 @default.
- W4299933332 hasRelatedWork W3013781205 @default.
- W4299933332 hasRelatedWork W3113137637 @default.
- W4299933332 hasRelatedWork W4382935469 @default.
- W4299933332 isParatext "false" @default.
- W4299933332 isRetracted "false" @default.
- W4299933332 workType "article" @default.