Optimal joint probing and transmission strategy for maximizing throughput in wireless systems

Abstract

In broadcast fading channel, channel variations can be exploited through what is referred to as multiuser diversity and opportunistic scheduling for improving system performance. To achieve the gains promised by this kind of diversity, the transmitter has to accurately track the channel variations of the various receivers, which consumes resources (time, energy, bandwidth), and thus reduces the resources remaining for effective data transmissions. The transmitter may decide not to acquire or probe the channel conditions of certain receivers, either because these receivers are presumably experiencing severe fading, or because the transmitter wishes to spare resources for data transmissions. It may also decide to transmit to a receiver without probing its channel; in such cases, the transmitter guesses the channel state, which often results in a reduction of the transmission rate compared to when the transmitter knows the channel state. Ultimately, the transmitter has to decide to which receiver it should transmit. In this paper, we identifying the joint probing and transmission strategies realizing the optimal trade-off between the channel state acquisition and the effective data transmission. The objective is to maximize the system throughput. Finally, we propose several extensions of the proposed strategy, including a scheme to maximize the system utility and a scheme to ensure the system stability.

Scheduling with limited information in wireless systems

“…To our knowledge, the present work provides the first analysis of the joint CSI acquisition and scheduling problem with system stability as a performance objective. As we demonstrate later, accounting for the stochastic queueing behavior of the system greatly increases the problem complexity, and our results are nontrivial extensions of those presented in [8].…”

“…Thus, the complete characterization of the optimal policy through Theorems 3 and 4 is available only in limited cases. We note that the problem of determining which user to probe is difficult in general (see [11]) even when all the weights are equal; the solutions are known only in special cases [8]. In the following section, we consider certain special cases that are relevant in practice, and obtain scheduling policies with provable stability guarantees.…”

“…Identifying an optimal channel state acquisition and scheduling strategy has been addressed in the literature only recently [14,20,11,12,7,8]. These papers study the exploration vs. exploitation trade-off in the saturated case, i.e., all users always have packets in their corresponding buffers.…”

“…In other words, when, after acquiring the CSI of p users, a user is scheduled and the corresponding transmission rate is R, then the reward or throughput is equal to R − pβ. This cost structure simplifies the problem as explained in [7,8], but proves difficult to justify. Here we consider a more natural cost structure: time is slotted and acquiring the CSI of one user takes a fraction β of the slot, so that the throughput (in a slot) is equal to R × (1 − pβ).…”

“…Here we consider a more natural cost structure: time is slotted and acquiring the CSI of one user takes a fraction β of the slot, so that the throughput (in a slot) is equal to R × (1 − pβ). This cost model is referred to as a logarithmic cost in [8] (since the log of the throughput is just log(R) + log(1 − pβ)).…”