Rate-Splitting Multiple Access for Non-Orthogonal Unicast Multicast: An Experimental Study

Non-orthogonal unicast multicast (NOUM) is a variant of multi-antenna multi-user communications where the users desire a shared message (multicast) in addition to their respective unique messages (unicast). The multicast rate is capped in many emerging NOUM applications, such as live-event broadcasting, location-based services and vehicular communications. Given this constraint, we experimentally show that when the user channels are highly correlated, Rate-Splitting Multiple Access (RSMA)-based NOUM can meet the multicast rate while supporting a larger unicast sum rate than conventional multi-user linear precoding (MULP)-based NOUM.

A Prototype Implementation of Rate Splitting Multiple Access using Software-Defined Radios

For interference-limited multi-user communications, many papers have demonstrated, in theory, the effectiveness of Rate Splitting Multiple Access (RSMA) in suppressing the interference and achieving better outcomes (w.r.t spectral efficiency, fairness etc.) than the conventional Space Division Multiple Access (SDMA) used in present-day standards. However, an experimental demonstration of RSMA's benefits is missing in the literature. In this paper, we address this gap by realizing an RSMA prototype using software-defined radios. For the two-user multiple-input single-output (MISO) scenario, we measure the throughput performance of SDMA and RSMA in nine different scenarios that vary in terms of the channel pathloss and spatial correlation experienced by the users. Emulating perfect channel state information (CSI) at the transmitter through unquantized CSI feedback, we observe that RSMA achieves a higher sum throughput (upto 57%) and more fairness than SDMA when the user channels have high spatial correlation. Similarly, emulating imperfect CSI through quantized feedback, RSMA - along with the above trend - also experiences a smaller sum throughput loss, relative to the unquantized case (37%, on average, as opposed to 44% for SDMA). These outcomes are consistent with theoretical predictions, and demonstrate the feasibility and potential of RSMA for next generation wireless networks (e.g., 6G).

Rule-Guided Graph Neural Networks for Recommender Systems

To alleviate the cold start problem caused by collaborative filtering in recommender systems, knowledge graphs (KGs) are increasingly employed by many methods as auxiliary resources. However, existing work incorporated with KGs cannot capture the explicit long-range semantics between users and items meanwhile consider various connectivity between items. In this paper, we propose RGRec, which combines rule learning and graph neural networks (GNNs) for recommendation. RGRec first maps items to corresponding entities in KGs and adds users as new entities. Then, it automatically learns rules to model the explicit long-range semantics, and captures the connectivity between entities by aggregation to better encode various information. We show the effectiveness of RGRec on three real-world datasets. Particularly, the combination of rule learning and GNNs achieves substantial improvement compared to methods only using either of them.