Efficient Resource Allocation for Underlay Device-to-device Communication Networks with Limited Channel State Information

Abstract

Device-to-device (D2D) communication is finding applications in future wireless networks such as vehicular networks and internet-of-things. It offloads traffic from the base station (BS), improves energy efficiency, and reduces latency by enabling direct communication between the users. In underlay D2D, the D2D users share subchannels with the cellular users (CUs). While this improves spatial reuse, it causes interference between the D2D users and CUs. Hence, interference-aware resource allocation is an important research problem for underlay D2D networks.

In this thesis, we consider a practically feasible partial CSI model in which the BS only knows the channel state information (CSI) of the CU-to-BS and D2D Receiver (DRx)-to-BS links. The D2D pair knows the CSI of the D2D transmitter (DTx)-to-DRx and CU-to-DRx links, and the statistics of inter-D2D and inter-cell interference powers. We propose a feedback model in which the DRx computes the signal-to-interference-plus-noise ratio (SINR) estimate and feeds a quantized version of it back to the BS. The SINR estimate is such that the corresponding rate has an outage probability within a pre-specified value.

We first consider a subchannel allocation problem in which at most K D2D pairs are allowed to share a subchannel and a minimum rate with a pre-specified probability of outage is guaranteed for the CUs. We propose a polynomial-time algorithm called cardinality-constrained subchannel assignment algorithm (CCSAA) based on a submodular maximization approach. We prove that it gives a D2D sum rate that is at least one-third of the optimal D2D sum rate. We also propose a lower-complexity locally greedy algorithm (LGA) that provides the same theoretical guarantee but is applicable when K is equal to the number of D2D pairs. We then propose a modification of LGA called cardinality-constrained LGA (CCLGA) that applies to all values of K. We propose a rate upgradation scheme employed at the D2D pair to improve the D2D rate after subchannel allocation by exploiting the asymmetry in the rate information at the BS and the D2D pairs.

Next, we consider a statistical CSI model in which the DRx computes and feeds back the SINR estimate by knowing only the statistics of the CSI of DTx-to-DRx, CU-to-DRx links, and the inter-cell and inter-D2D interference powers. We propose a relaxation-pruning algorithm (RPA) based on a linear program relaxation and rounding approach. It provides a D2D sum rate that is at least half of the optimal D2D sum rate. We present numerical results to investigate the interplay between the CSI model and resource allocation algorithm design by considering partial and statistical CSI models, and RPA and CCSAA. RPA outperforms CCSAA for the partial CSI model, while CCSAA outperforms RPA for the statistical CSI model even though it has a lower theoretical sum rate guarantee than RPA. We connect this to the different sensitivities of the algorithms to the variation of rates across subchannels for the considered CSI models. We find that the optimal value of K depends on the CSI model, algorithm, and feedback resolution. We also propose a statistical rate upgradation scheme in which the D2D pair exploits the broadcast subchannel allocation information to upgrade its rate.

In the last part of our work, we study the subchannel allocation problem with a disjunctivity constraint between the D2D pairs. It prevents two D2D pairs from sharing a subchannel if they cause significant interference to each other. This approach is naturally applicable for dense D2D networks, where D2D pairs are closely spaced and avoids the conservative rate estimates generated by our earlier approaches. We address the subchannel allocation problem for two cases. In the first case, a D2D pair is allowed to transmit on multiple subchannels. We propose a branch-and-bound algorithm to assign subchannels to the D2D pairs. In the second case, a D2D pair is allowed to transmit on only one subchannel. We propose a submodular maximization-based approach in which, for each subchannel, we apply the branch-and-bound algorithm to assign subchannels to the D2D pairs. This approach provides at least half of the optimal D2D sum rate. We look at the disjunctivity constraint and the SINR computation based on path-loss and fading-averaged interference power between the D2D pairs. Our results show that considering fading-averaged interference power that includes the path-loss and shadowing leads to improved system performance than considering only path-loss, which is often considered in the literature.