Distributed link scheduling with constant overhead

Abstract

This paper proposes a new class of simple, distributed algorithms for scheduling in wireless networks. The algorithms generate new schedules in a distributed manner via simple local changes to existing schedules. The class is parameterized by integers k ≥ 1. We show that algorithm k of our class achieves k/(k + 2) of the capacity region, for every k ≥ 1.The algorithms have small and constant worst-case overheads: in particular, algorithm k generates a new schedule using (a) time less than 4k + 2 round-trip times between neighboring nodes in the network, and (b) at most three control transmissions by any given node, for any k. The control signals are explicitly specified, and face the same interference effects as normal data transmissions.Our class of distributed wireless scheduling algorithms are the first ones guaranteed to achieve any fixed fraction of the capacity region while using small and constant overheads that do not scale with network size. The parameter k explicitly captures the tradeoff between control overhead and scheduler throughput performance and provides a tuning knob protocol designers can use to harness this trade-off in practice.

On throughput optimality with delayed network-state information

“…as demonstrated in equality (17), where the last equality holds due to equation (16). Now, give Q P (0 : τ ) = q(0 : τ ), C(τ : τ ) = c , there exist T(c, q(0 : τ )) and p(c, q(0 : τ )) such that equality (18) holds for all (m, n) ∈ L. In other words, there exist T(c, q(0 : τ )) and p(c, q(0 : τ )) such that, given the queue lengths and delayed channel states, the probability the channel state of (m, n) is greater than or equal to the threshold is the same as the probability that link (m, n) is active under the time-sharing policy.…”

“…A key component in engineering such networks is the routing/scheduling algorithm. So far, most research focuses on developing routing/scheduling algorithms with complete Network State Information (NSI -the channel and queue state information of the entire network) by assuming that instantaneous NSI is available [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], or channel states are constant [12], [13], [14], [15], [16], [17]. We refer to [18], [19] for comprehensive surveys.…”

Stability-Based Topology Control in Wireless Mesh Networks

“…Therefore, the optimal scheduling policy is not convenient for implementation due to its high complexity. As a consequence, less complex scheduling policies that achieve only a fraction of the optimal stability region for general network topologies have been developed [12,[15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Past queue length based low-overhead link scheduling in multi-beam wireless mesh networks

“…Gupta et al in [15], and Joo and Shroff in [16] built on this result in [14] and achieved close to 1/2 the capacity region with constant overhead. In [17], it has been shown that full throughput can be achieved with constant overhead when we consider a node exclusive spectrum sharing model for interference. All these works do not consider multiple beams at the network nodes.…”

“…14) The feasible set, Γ, can differ in omni-beam and multibeam scenarios due to spatial reuse. For e.g., in a 3-node linear network with one flow, assuming node exclusive spectrum sharing for omni-beam case [17] and assuming non-directional links with R jl ∈ {0, 1}, the feasible set of rates on links 1 and 2 for the case of omni-beam is Γ = {(0, 0), (0, 1), (1, 0)}. For the case of 2 beams per node, Γ = {(0, 0), (0, 1), (1, 0), (1, 1)}.…”