Opportunistic transmission scheduling with resource-sharing constraints in wireless networks

Abstract

We present an "opportunistic" transmission scheduling policy that exploits time-varying channel conditions and maximizes the system performance stochastically under a certain resource allocation constraint. We establish the optimality of the scheduling scheme, and also that every user experiences a performance improvement over any non-opportunistic scheduling policy when users have independent performance values. We demonstrate via simulation results that the scheme is robust to estimation errors, and also works well for nonstationary scenarios, resulting in performance improvements of 20-150% compared with a scheduling scheme that does not take into account channel conditions. Last, we discuss an extension of our opportunistic scheduling scheme to improve "short-term" performance.

Optimized Channel Utilization in Multi-carrier Wireless Mobile Networks

“…From Ô´ µ, we first calculate Ô´ µ ½ Ô´ µ. 4. We assume that the Ô´ µ is inversely proportional to the "distance" between and .…”

“…Fast adaptation increases the number of mode-change messages transmitted over the channel, consuming bandwidth, and time resources [1]. While many aspects of scheduling transmissions over time-varying wireless channels have been studied (see, for example, [2][3][4] and the references therein), the penalty induced by LA has not been considered. Moreover, predicting the future channel quality may also consume significant amount of system resources (e.g., time, bandwidth and power), since it may involve transmission of training-sequences, pilot tones, or feedback messages carrying the CSI [1].…”

QoS-Aware Cooperative and Opportunistic Scheduling Exploiting Multi-user Diversity for Rate Adaptive Ad Hoc Networks

“…In practice, the λ i 's can similarly be calculated by stochastic approximation algorithm as in [19]. We give an iterative algorithm as follows:…”

“…It is proved in [6] that HDR exploits multiuser diversity while maintaining proportional fairness among users. In [19], the authors consider resource-sharing constraints and propose a water-filling algorithm that selects the link with the highest feasible data rate added by a bias value. Kulkarni and Rosenberg extended the water-filling method to a more generalized solution in [10] for multiple long-term QoS constrains.…”

“…where the C k i is the throughput achieved until timeslot k, and for the stationary case, we can set a k = 1/k; otherwise, we set a k to a small constant to track the system variation [19].…”