Throughput Optimal Scheduling with Dynamic Channel Feedback

Abstract: It is well known that opportunistic scheduling algorithms are throughput optimal under full knowledge of channel and network conditions. However, these algorithms achieve a hypothetical achievable rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. We adopt a channel probing model where $\beta$ fraction of time slot is consumed for acquiring the channel state information (CSI) of a single channel. In this work, we design a joint scheduling and channel probing algorithm named SDF by considering the overhead of obtaining the channel state information. We first analytically prove SDF algorithm can support $1+\epsilon$ fraction of of the full rate region achieved when all users are probed where $\epsilon$ depends on the expected number of users which are not probed. Then, for homogenous channel, we show that when the number of users in the network is greater than 3, $\epsilon > 0$, i.e., we guarantee to expand the rate region. In addition, for heterogenous channels, we prove the conditions under which SDF guarantees to increase the rate region. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by probing only less than 50% of all channels in a CDMA based cellular network utilizing high data rate protocol under normal channel conditions.