spectral-efficiency analysis

Existing spectral efficiency (SE) evaluations of candidate 6G waveforms neglect computational complexity, leading to significant SE distortion under compute-constrained scenarios. Method: This work explicitly incorporates signal processing time complexity as a core constraint in SE analysis, establishing a joint computation-resource–SE evaluation framework. Leveraging measured baseband processor runtime, effective data rate derivation under symbol duration constraints, and comparative case studies involving IEEE 802.11a and representative 6G waveforms, it reveals how real-time execution bottlenecks degrade practical SE for high-complexity waveforms. Results: Ignoring computational overhead overestimates SE by over 20%; conversely, certain low-nominal-SE waveforms outperform high-complexity alternatives under sufficient compute resources. This study provides the first quantitative benchmark for 6G waveform selection that jointly optimizes computational cost and communication performance.

To address inaccurate SINR modeling in downlink multi-user MIMO systems caused by imperfect (particularly outdated) channel state information at the transmitter (CSIT), this paper proposes a high-accuracy, analytically tractable statistical SINR approximation tailored for the integrated Rate-Splitting Multiple Access (RSMA) and Space-Division Multiple Access (SDMA) architecture. Departing from conventional Gamma-based approximations—which systematically underestimate SINR variance—the proposed model preserves closed-form solvability while significantly improving fidelity to practical channel mismatch. Theoretical analysis and extensive simulations across diverse antenna configurations, user numbers, and CSIT distortion levels confirm its superior accuracy: average modeling error is reduced by over 50% compared to classical approaches. This enables rigorous analytical performance evaluation and resource optimization of RSMA systems under realistic, non-ideal CSI conditions.

This paper addresses the degradation of user spectral efficiency (SE) in full-duplex integrated access and backhaul (IAB) networks under high-load conditions. We propose a joint optimization framework for uplink/downlink power allocation and beamforming. To this end, we first formulate a novel uplink-downlink coordinated power control model tailored to IAB architectures and analyze the fundamental capacity limitation imposed by the rank-one nature of line-of-sight (LOS) channels. We then design two convex optimization objectives—max-sum SE and max-min fairness—and enable dynamic resource adaptation driven by channel state information (CSI). Experimental results demonstrate that the proposed method significantly enhances system performance in multi-user IAB scenarios, achieving up to a 37% improvement in SE over baseline schemes while maintaining service fairness. The results validate the critical role of joint power optimization in beyond-5G IAB networks.

This work addresses channel modeling and performance evaluation of distributed MIMO (D-MIMO) in industrial environments. We systematically compare, for the first time, deterministic ray-tracing models against stochastic Rayleigh fading models in predicting downlink/uplink single-user capacity. Leveraging a real-world 3D factory map, we construct multiple deployment scenarios to quantify how network densification affects user equipment (UE) multi-access point (AP) connectivity and coverage gain. Results show that densification significantly enhances D-MIMO capacity. Ray tracing more accurately captures spatial correlation and realistic propagation characteristics, whereas the Rayleigh model offers superior computational efficiency and maintains acceptable prediction error (<15%) in typical factory settings. The study establishes fundamental trade-offs among modeling accuracy, spatial correlation fidelity, and computational overhead, providing both theoretical guidance and empirical evidence for selecting appropriate D-MIMO channel models in industrial wireless systems.

Ultra-massive MIMO (XL-MIMO) systems operating in the mid-band face a severe energy-efficiency bottleneck due to power consumption scaling with array size, limiting their practical deployment. Method: We develop the first end-to-end, hardware- and signal-processing-aware power consumption model for XL-MIMO, integrated with near-field channel modeling and closed-form throughput analysis to establish a system-level energy efficiency (EE) analytical framework. Contribution/Results: We derive, for the first time, the analytical EE scaling law under near-field conditions. Theoretical and numerical validation confirms <3% throughput estimation error and excellent agreement between analytical EE predictions and simulations. At equal spectral efficiency, mid-band XL-MIMO achieves 18–35% lower power consumption than conventional multi-antenna systems, clearly demonstrating its superior energy efficiency.

This work addresses the lack of theoretical understanding regarding the degradation of rate-splitting multiple access (RSMA) to space-division multiple access (SDMA) in the presence of transceiver hardware impairments and imperfect successive interference cancellation (SIC). By constructing a unified system model that jointly accounts for hardware distortions and residual SIC interference, the study rigorously proves—using optimization theory—that as the residual interference coefficient approaches unity, the optimal beamformer for the common stream in RSMA converges to zero. Consequently, the RSMA transmission structure naturally collapses into SDMA. This result provides the first optimality-based explanation for the empirically observed performance convergence between RSMA and SDMA under severe SIC failure, offering crucial theoretical guidance for selecting appropriate multiple access schemes in SIC-constrained systems.

This work addresses the limitations of the 3GPP-standardized tapped delay line (TDL) channel model in accurately capturing the spatial propagation characteristics of MIMO systems, which can lead to biased performance evaluations. To overcome this, the authors propose and validate a reduced cluster delay line (rCDL) model. Through comparative analysis against real-world channel measurements in representative commercial scenarios, the study evaluates the spatial modeling accuracy of rCDL relative to TDL and further assesses their discriminative capability via CSI reporting performance simulations. Results demonstrate that rCDL significantly improves the fidelity of spatial channel characterization while maintaining reasonable computational complexity. It outperforms TDL in both measurement-to-model alignment and evaluation of CSI feedback schemes, thereby offering strong support for future 3GPP standardization efforts.

This work addresses a critical limitation in conventional performance analyses of non-orthogonal multiple access (NOMA) systems, which typically neglect the statistical dependence between successive interference cancellation (SIC) residual noise and channel fading, leading to inaccurate outage probability and ergodic capacity evaluations. Focusing on a two-user downlink NOMA scenario, the study derives for the first time the joint probability density function of SIC-induced noise and Rayleigh fading channels. By leveraging random variable transformation and closed-form integration techniques, it obtains an exact closed-form expression for the near user’s outage probability and a single-integral representation for its ergodic capacity. The proposed parameter-free model exposes the fundamental inadequacy of treating the residual interference factor as statistically independent. Simulations confirm that conventional models—assuming Gaussian or fixed residual interference—exhibit significant deviations from actual system performance, particularly at medium to low signal-to-noise ratios.

This work addresses the trade-off between energy efficiency and integrated sensing and communication (ISAC) performance in satellite–UAV MIMO systems by proposing a distributed MIMO ISAC framework under a hybrid high- and low-altitude channel model that accounts for both line-of-sight (LoS) and non-LoS components. The framework jointly optimizes beamforming and power allocation, incorporating—for the first time—a constraint on sensing beampattern gain into the energy efficiency maximization problem to ensure multi-user quality-of-service requirements and multi-target radar sensing accuracy. Leveraging a uniform planar array and a probabilistic LoS channel model, the resulting non-convex problem is solved via an alternating optimization algorithm. Simulation results demonstrate that the proposed method significantly enhances energy efficiency in multi-user, multi-target scenarios while simultaneously meeting communication throughput and sensing precision requirements.