Channel probing in communication systems: Myopic policies are not always optimal

We consider a multi-channel communication system in which a transmitter has access to a large number of channels, but does not know the state of these channels. We model channel state using an ON/OFF Markovian model, and allow the transmitter to probe one of the channels at predetermined probing intervals to decide over which channel to transmit. For models in which the transmitter must send over the probed channel, it has been shown that a myopic policy that probes the channel most likely to be ON is optimal. In this work, we allow the transmitter to select a channel over which to transmit that is not necessarily the one it probed. We show that the myopic policy is not optimal, and propose a simple alternative probing policy, which achieves a higher per-slot expected throughput. Finally, we consider the case where there is a fixed cost associated with probing and derive optimal probing intervals.National Science Foundation (U.S.) (Grant CNS1217048)National Science Foundation (U.S.) (CNS-0915988)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238

Optimal channel probing in communication systems: The two-channel case

We consider a multi-channel communication system in which a transmitter has access to two channels, but does not know the state of either channel. We model the channel state using an ON/OFF Markovian model, and allow the transmitter to probe one of the channels at predetermined probing intervals to decide over which channel to transmit. For models in which the transmitter must transmit over the probed channel, it has been shown that a myopic policy that probes the channel most likely to be ON is optimal. In this work, we allow the transmitter to select a channel over which to transmit that is not necessarily the one it probed. We show that in the case where the two channels are i.i.d, all probing policies yield equal reward. We extend this problem to dynamically choose when to probe based on the results of previous probes, and characterize the optimal policy, as well as provide a LP in terms of state action frequencies to find the optimal policy.National Science Foundation (U.S.) (Grant CNS-0915988)National Science Foundation (U.S.) (Grant CNS-1217048)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238

Joint-optimal probing and scheduling in wireless systems

Abstract-Consider a wireless system where a sender can transmit data to various users with independent and varying channel conditions. To maximize its long-term transmission rate, the sender should always transmit to the user with the best channel. To discover which user has the best channel, it has to spend time to probe channels, and this reduces the time available for effective transmission. This paper aims at identifying optimal joint probing and scheduling strategies. These strategies realize the best trade-off between the channel state acquisition and effective transmission. We first provide general structural properties of optimal strategies, and then exactly characterize these strategies in particular but relevant cases. Finally we propose extensions of this problem, e.g., to impose fairness among the users, we investigate how to maximize system utility rather than throughput

Throughput-efficient sequential channel sensing and probing in cognitive radio networks under sensing errors

In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic trans-mission. The difficulty of the problem comes from the fact that it is practically infeasible for a CR to first scan all chan-nels and then pick the best among them, due to the poten-tially large number of channels open to OSA and the limited power/hardware capability of a CR. As a result, the CR can only sense and probe channels sequentially. To avoid colli-sions with other CRs, after sensing and probing a channel, the CR needs to make a decision on whether to terminate the scan and use the underlying channel or to skip it and scan the next one. The optimal use-or-skip decision strategy that maximizes the CR’s average throughput is one of our primary concerns in this study. This problem is further complicated by practical considerations, such as sensing/probing overhead and sensing errors. An optimal decision strategy that ad-dresses all the above considerations is derived by formulat-ing the sequential sensing/probing process as a rate-of-return problem, which we solve using optimal stopping theory. We further explore the special structure of this strategy to con-duct a “second-round ” optimization over the operational pa-rameters, such as the sensing and probing times. We show through simulations that significant throughput gains (e.g., about 100%) are achieved using our joint sensing/probing scheme over the conventional one that uses sensing alone