Optimal routing, link scheduling and power control in multihop wireless networks

By Rene L. Cruz, Arvind Santhanam. IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), 2003

676 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4a4592a90a55633c9cf318147a7cb0f4cfcdbba9

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Citations in the corpus

On the Complexity of Scheduling in Wireless Networks (2006, background): “This has spurred recent interest in developing cross-layer optimization algorithms (see, for example, [34, 23, 22, 29, 7]).”
A tutorial on cross-layer optimization in wireless networks (2006, background): “Note that the perfect schedules computed by the scheduling component (8) and (12) are, in fact, the throughput-optimal schedules that achieve the largest capacity region (see Section II-B, and [15], [37], and [ 38 ]).”
A survey of multimedia streaming in wireless sensor networks (2008, background): “Similar combinations of joint power, scheduling, routing, and physical layer optimizations were studied in [121]–[123], [129].”
The impact of imperfect scheduling on cross-Layer congestion control in wireless networks (2006, methodology): “The extension to the case with channel variations is treated in Section V-B. maximum capacity [4], [5], [ 7 ].”
The impact of imperfect scheduling on cross-layer rate control in wireless networks (2005, methodology): “This assumption is suitable for CDMA systems with a moderate processing gain [ 6 ].”
Ad hoc networks beyond unit disk graphs (2003, background): “While some work tries to characterize interference by static UDG-based models [11, 20, 57, 70], other researchers focus on the interplay of scheduling and transmission power based on a realistic physical signal propagation model [13, 15, 16, 24, 27, 34, 53, 58, 59].”
Dynamic power allocation and routing for time varying wireless networks (2003, background): “Optimization approaches to network resource allocation are developed in [10]–[14] and [29]–[32].”
Minimum-cost multicast over coded packet networks (2005, background): “The rate vectorz, consisting ofziJ , (i, J) ∈ A, is called a subgraph, and we assume that it must lie within a constraint setZ for, if not, the packet queues associated with one or more hyperarcs becomes unstable (for examples of constraint set s Z that pertain specifically to multi-hop wireless net”
The Complexity of Connectivity in Wireless Networks (2006, methodology): “In [4], [3], the impact of power assignments to nodes on the achievable throughput capacity is studied, whereas [ 7 ], [9] study the problem of finding a schedule and power control policy that minimizes the total average transmission power in the wireless multi-hop network.”
Joint rate control and scheduling in multihop wireless networks (2004, methodology): “The capacity region is determined as in [4]: The Route-Dependent Case: The capacity region Λ is the set of user rates x such that there exists xsv for each s, v and the vector [xsv, s = 1, .”
Complexity in geometric SINR (2007, methodology): “Other aspects of scheduling and power control using an arbitrary gain matrix are studied for instance in [3, 4, 6 , 28, 29, 30].”
Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks (2006, methodology): “This is in contrast to [ 4 ], where the authors assume a system with very low bandwidth efficiency.”
A Unified Framework for Max-Min and Min-Max Fairness With Applications (2007, background): “The maximum rate of informationd can achieve is then [25], [6]”
Understanding the Capacity Region of the Greedy Maximal Scheduling Algorithm in Multihop Wireless Networks (2008, background): “On the other hand, if no vector φ ∈ Co(ME) exists such that λ φ, then one can show that the system is unstable under any scheduling algorithms [1], [24], [25].”
Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures (2007, background): “• Jointly congestion control and adaptive coding or power control [18], [21], [76] • Jointly congestion and contention control [16], [61], [74], [144], [167], [168], [169] • Jointly congestion control and scheduling [1], [12], [35], [92], [129] • Jointly routing and power control [58], [100], [159] ”