說明 
1 electronic text (x, 144 p. : ill.) : digital file 
系列 
Synthesis lectures on communication networks, 19354193 ; # 6


Synthesis digital library of engineering and computer science


Synthesis lectures on communication networks, 19354193 ; # 6

附註 
Part of: Synthesis digital library of engineering and computer science 

Series from website 

Includes bibliographical references (p. 133139) and index 

1. Introduction  

2. Overview  A small wireless network  Feasible rates  Maximum weighted matching  CSMA  Entropy maximization  Discussion  Admission control  Randomized backpressure  Appendix  Summary  

3. Scheduling in wireless networks  Model and scheduling problem  CSMA algorithm  Idealized algorithm  CSMA can achieve maximal throughput  An idealized distributed algorithm  Distributed algorithms  Throughputoptimal algorithm  Variation: constant update intervals  Timeinvariant ACSMA  Maximalentropy interpretation  Reducing delays: algorithm 1(b)  Simulations  Timeinvariant ACSMA  Timevarying ACSMA  Proof sketch of theorem 3.10(i)  Further proof details of theorem 3.10(i)  Property 3.21  Property 3.22: noise  Property 3.22: bias  Proof of theorem 3.10(ii)  Proof of theorem 3.13  General transmission times  Appendices  Proof of the fact that C is the interior of C  Proof the proposition 3.7  Summary  Related works  Maximalweight scheduling  Lowcomplexity but suboptimal algorithms  Throughputoptimum algorithms for restrictive interference models  Random access algorithms  

4. Utility maximization in wireless networks  Joint scheduling and congestion control  Formulation of optimization problem  Derivation of algorithm  Approaching the maximal utility  Extensions  Anycast  Multicast with network coding  Simulations  Properties of algorithm  Bound on backpressure  Total utility  Queue lengths  Summary  Related works  

5. Distributed CSMA scheduling with collisions  Introduction  CSMA/CAbased scheduling with collisions  Model  Notation  Computation of the service rates  A distributed algorithm to approach throughputoptimality  CSMA scheduling with collisions  Reducing delays  Numerical examples  Proofs of theorems  Proof of theorem 5.1  Proof of theorem 5.2  Proof of Theorem 5.4  Summary  Related works  

6. Stochastic processing networks  Introduction  Examples  Basic model  DMW scheduling  Arrivals that are smooth enough  More random arrivals  Utility maximization  Extensions  Simulations  DMW scheduling  Utility maximization  Summary  Skipped proofs  Proof of theorem 6.5  Proof of theorem refthm:ratestable  Proof of theorem 6.8  Proof of theorem 6.9  

A. Stochastic approximation  A.1. Gradient algorithm  A.2. Stochastic approximation  A.3. Summary  A.4. References  Bibliography  Authors' biographies  Index 

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In this book, we consider the problem of achieving the maximum throughput and utility in a class of networks with resourcesharing constraints. This is a classical problem of great importance. In the context of wireless networks, we first propose a fully distributed scheduling algorithm that achieves the maximum throughput. Inspired by CSMA (Carrier Sense Multiple Access), which is widely deployed in today's wireless networks, our algorithm is simple, asynchronous, and easy to implement. Second, using a novel maximalentropy technique, we combine the CSMA scheduling algorithm with congestion control to approach the maximum utility. Also, we further show that CSMA scheduling is a modular MAClayer algorithm that can work with other protocols in the transport layer and network layer. Third, for wireless networks where packet collisions are unavoidable, we establish a general analytical model and extend the above algorithms to that case. Stochastic Processing Networks (SPNs) model manufacturing, communication, and service systems. In manufacturing networks, for example, tasks require parts and resources to produce other parts. SPNs are more general than queueing networks and pose novel challenges to throughput optimum scheduling. We proposes a "deficit maximum weight" (DMW) algorithm to achieve throughput optimality and maximize the net utility of the production in SPNs 

Also available in print 

MorganIISLIB 
主題 
Packet switching (Data transmission)


Wireless communication systems  Design and construction


scheduling


congestion control


wireless networks


stochastic processing networks


carrier sensemultiple access


convex optimization


Markov chain


stochastic approximation

Alt Author 
Walrand, Jean

