Antti Tolli

Amin Radbord, Italo Atzeni, Antti Tolli

We present a new analytical framework on the uplink data detection for massive multiple-input multiple-output systems with 1-bit analog-to-digital converters (ADCs). We first characterize the expected values of the soft-estimated symbols (after the linear receiver and prior to the data detection), which are affected by the 1-bit quantization during both the channel estimation and the uplink data transmission. In our analysis, we consider conventional receivers such as maximum ratio combining (MRC), zero forcing, and minimum mean squared error (MMSE), with multiple user equipments (UEs) and correlated Rayleigh fading. Additionally, we design a linear minimum mean dispersion (LMMD) receiver tailored for the data detection with 1-bit ADCs, which exploits the expected values of the soft-estimated symbols previously derived. Then, we propose a joint data detection (JD) strategy that exploits the interdependence among the soft-estimated symbols of the interfering UEs, along with its low-complexity variant. These strategies are compared with the robust maximum likelihood data detection with 1-bit ADCs. Numerical results examining the symbol error rate show that MMSE exhibits a considerable performance gain over MRC, whereas the proposed LMMD receiver significantly outperforms all the conventional receivers. Lastly, the proposed JD and its low-complexity variant provide a significant boost in comparison with the single-UE data detection.

MohammadJavad Salehi, Kari Hooli, Jari Hulkkonen et al.

The next evolutionary step in human-computer interfaces will bring forward immersive digital experiences that submerge users in a 3D world while allowing them to interact with virtual or twin objects. Accordingly, various collaborative extended reality (XR) applications are expected to emerge, imposing stringent performance requirements on the underlying wireless connectivity infrastructure. In this paper, we examine how novel multi-antenna coded caching (CC) techniques can facilitate high-rate low-latency communications and improve users' quality of experience (QoE) in our envisioned multi-user XR scenario. Specifically, we discuss how these techniques make it possible to prioritize the content relevant to wireless bottleneck areas while enabling the cumulative cache memory of the users to be utilized as an additional communication resource. In this regard, we first explore recent advancements in multi-antenna CC that facilitate the efficient use of distributed in-device memory resources. Then, we review how XR application requirements are addressed within the third-generation partnership project (3GPP) framework and how our envisioned XR scenario relates to the foreseen use cases. Finally, we identify new challenges arising from integrating CC techniques into multi-user XR scenarios and propose novel solutions to address them in practice.