In 6G network with extreme MIMO a potential misalignment exists between UE-centric rank selection and the internal link adaptation state of the gNB, specifically the outer loop link adaptation offset. Since this misalignment leads to inefficient resource utilization and degraded quality-of-service, it must be avoided. We present a novel adaptive rank selection algorithm that allows the gNB to dynamically influence the rank selection of the UE to bridge this gap. Our solution incorporates a gNB-configurable rank selection margin, α, which imposes a minimum required throughput gain to justify selecting a higher rank. This margin constrains the greedy rank selection by the UE, and helps aligning it with the link adaptation state of gNB. System-level results confirm that our method outperforms the conventional greedy baseline, boosting XR capacity from 14 to 15 users per cell and delivering consistent UE throughput gains across all measured percentiles for best-effort file transfer traffic.

While extended reality (XR) applications are expected to be one of the cornerstone use cases for the 6G mobile communications system, the actual performance and capacity results for XR in 6G are yet to be rigorously established. This paper bridges this gap by developing a detailed system model for a candidate 6G network and using it for evaluating the performance of XR applications through extensive system-level simulations. We investigate three enablers for the 6G networks: new spectrum utilization, extreme multiple-input multiple-output (MIMO) methods, and advanced radio resource management (RRM) techniques. Our results demonstrate a 3 times XR capacity improvement over 5G baselines at 3.5 GHz using enhanced CSI feedback framework and advanced RRM techniques. Furthermore, by utilizing the new 7 GHz upper-mid band spectrum with extreme MIMO and a larger bandwidth, XR capacity can be further improved by approximately a factor of 10, supporting more than 50 users per cell. These findings provide the first quantitative evidence of 6G’s potential for XR and offer a crucial baseline for future 6G development.

Conventional hybrid automatic repeat request (HARQ) uses single-bit feedback to boost retransmission reliability; however, its limited granularity restricts advanced link adaptation (LA) for optimizing performance beyond reliability. This paper presents a novel LA approach for retransmissions in 6G networks, targeting spectral efficiency (SE) optimization through effective modulation and coding scheme selection within the HARQ framework. In a dense urban setting with urban macro cell deployment and file transfer protocol model 3 (FTP3) traffic, our optimal LA solution improves average downlink (DL) physical resource block utilization by 31.37 % over Chase combining (CC), a gain inherently linked to SE optimization. Following 3GPP release 18 study item technical report's recommendation to include LA information in HARQ feedback, we address feedback design limitations of the optimal solution with a quantized LA metric. This metric is incorporated into our novel enriched HARQ feedback (EHF) at the user equipment, enabling EHF to surpass single-bit conventional feedback that only conveys transport block decoding status. Evaluated under varying feedback bit counts and LA quantization levels, our design achieves significant gains-e.g., a 20.26 % increase over CC in average DL throughput per FTP3 packet with 4-bit feedback-offering flexibility in application-specific bit sizing.

This paper investigates a dynamic packet scheduling algorithm designed to enhance the eXtended Reality (XR) capacity of fifth-generation (5G)-Advanced networks with multiple cells, users, and services. The scheduler exploits the newly defined protocol data unit (PDU)-set information for XR traffic flows to enhance its quality-of-service awareness. To evaluate the performance of the proposed solution, advanced dynamic system-level simulations are conducted. The findings reveal that the proposed scheduler offers a notable improvement in increasing XR capacity up to 45%, while keeping the same enhanced mobile broadband (eMBB) cell throughput as compared to the well-known baseline schedulers.

This paper presents a dynamic inter-cell interference coordination (ICIC) technique that enhances extended reality (XR) capacity, while minimizing the impact on enhanced mobile broadband (eMBB) traffic in a multi-cell multi-user network. The network identifies victim XR users based on their experienced performance and dynamically coordinates with aggressor cells for muting in specific transmission time intervals to alleviate interference for the victim users. Extensive dynamic system-level simulations demonstrate the effectiveness of the proposed ICIC scheme, achieving a 22-32% gain in XR capacity while incurring a degradation of 12-22% in average eMBB cell throughput.

Industry 4.0 requires wireless solutions to meet the demanding reliability and latency requirements of cyber-physical systems serving many industrial sensors and actuators. The cell-free massive multiple input multiple output (MIMO) paradigm, exploiting both massive MIMO gains and cell-free properties, is expected to play a crucial role in addressing Industry 4.0 communication requirements. However, finding an optimal user association to satisfy the quality of service (QoS) requirements is critical. In this article, we propose a novel centralized probabilistic user association algorithm to identify and maximize the number of served users without relying on short-term channel state information to satisfy the QoS requirements. In larger factories, approximately 90% of the total users are satisfied. However, as the system becomes interference-limited and is expected to meet challenging requirements, the number of served users decreases. Nevertheless, the proposed solution exhibits adaptability in maximizing the number of satisfied users. Simulation results further prove the effectiveness of the proposed solution in terms of achievable spectral efficiency and maximizing the number of satisfied users across varying requirements and factory sizes.

In this paper, we address the joint performance of eXtended reality (XR) and best-effort enhanced mobile broadband (eMBB) traffic for a 5G-Advanced system. Although XR users require stringent throughput and latency performance, operators do not lose significant additional network capacity when adding XR users to an eMBB-dominated network. For instance, adding an XR service at 45 Mbps with a 10 ms packet delay budget yields close to a 45 Mbps drop in eMBB capacity. In an XR-only network layer, we show how the capacity in a number of supported XR users depends significantly on the rate but also the latency budget. We also show how the XR service capacity is significantly reduced in the mixed service setting as the system goes into full load and other-cell interference becomes significant. The presented results can be used by cellular service providers to assess their networks’ performance of XR traffic based on their current eMBB performance or as input to dimensioning to be able to serve certain XR traffic loads.

We study the design of receivers in extra-large scale MIMO (XL-MIMO) systems, i.e. systems in which the base station is equipped with an antenna array of extremely large dimensions. While XL-MIMO can significantly increase the system’s spectral efficiency, they present two important challenges. One is the increased computational cost of multi-antenna processing. The second one is the presence of spatial non-stationarities in the channel response, which imply that the mean energy of a given user’s signal varies across the array. Such non-stationarities limit the performance of the system. In this paper, we propose a distributed receiver for such an XL-MIMO system that can address both challenges. Based on variational message passing (VMP), we propose a set of receiver options providing a range of complexity-performance characteristics to adapt to different requirements. Furthermore, we distribute the processing into local processing units (LPU), that can perform most of the complex processing in parallel, before sharing their outcome with a central processing unit (CPU). Our designs are specifically tailored to exploit the spatial non-stationarities and require lower computations than linear receivers. Our simulation study, performed with a channel model accounting for the special characteristics of XL-MIMO channels, confirms the superior performance of our proposals compared to the state of the art methods.