We consider a space-division multiplexing elastic optical network (SDM-EON) that supports super-channels (SChs). A Sch comprises a set of contiguous frequency slots on multiple cores in a multi-core fiber. The problem of finding a lightpath using SChs involves routing, modulation, spectrum and core assignment (RMSCA). To minimize the request blocking probability (RBP), two critical issues must be addressed. First, routing and modulation assignment (RMA) should not cause hotspots, or overutilized links. Second, spectrum and core assignment (SCA) should aim at minimizing fragmentation, or small frequency slot blocks that can hardly be utilized by future requests. In this paper, a pre-computation method is first proposed for better load balancing in RMA. Then an efficient fragmentation-aware SCA is proposed based on a new fragmentation metric that measures both the spectral and spatial fragmentation. With the enhanced RMA and SCA, a joint load-balanced and fragmentation-aware algorithm called LBFA is designed to solve the RMSCA problem. As compared with the existing algorithms, simulation results show that our LBFA provides significant reduction in RBP.

The communication bottleneck severely constrains the scalability of distributed deep learning, and efficient communication scheduling accelerates distributed DNN training by overlapping computation and communication tasks. However, existing approaches based on tensor partitioning are not efficient and suffer from two challenges: 1) the fixed number of tensor blocks transferred in parallel can not necessarily minimize the communication overheads; 2) although the scheduling order that preferentially transmits tensor blocks close to the input layer can start forward propagation in the next iteration earlier, the shortest per-iteration time is not obtained. In this paper, we propose an efficient communication framework called US-Byte. It can schedule unequal-sized tensor blocks in a near-optimal order to minimize the training time. We build the mathematical model of US-Byte by two phases: 1) the overlap of gradient communication and backward propagation, and 2) the overlap of gradient communication and forward propagation. We theoretically derive the optimal solution for the second phase and efficiently solve the first phase with a low-complexity algorithm. We implement the US-Byte architecture on PyTorch framework. Extensive experiments on two different 8-node GPU clusters demonstrate that US-Byte can achieve up to 1.26x and 1.56x speedup compared to ByteScheduler and WFBP, respectively. We further exploit simulations of 128 GPUs to verify the potential scaling performance of US-Byte. Simulation results show that US-Byte can achieve up to 1.69x speedup compared to the state-of-the-art communication framework.

Extremely Large-scale Antenna Array (ELAA) is increasingly recognized as a promising solution for enhancing spectral efficiency and spatial resolution in the 6G mobile system. However, realizing these benefits necessitates the development of sophisticated interference management strategies, which typically rely on perfect Channel State Information at the Transmitter (CSIT) and involve computationally intensive operations. In real-world scenarios, perfect CSIT is typically infeasible due to inherent channel estimation errors and hardware impairments, which also lead to imperfect Successive Interference Cancellation (SIC). Additionally, the computational complexity associated with precoding schemes poses a formidable challenge. To address these issues, this study proposes a Deep Learning (DL)-assisted Rate-Splitting Multiple Access (RSMA) scheme for ELAA systems. The primary objective is to maximize the geometric mean of ergodic user-rates under imperfect CSIT and SIC, thereby optimizing both fairness and system throughput. Given the prohibitively high computational complexity of conventional optimization approaches to address this optimization problem, we introduce a DL model, named GruCN, to optimize precoder design. Simulation results demonstrate that the proposed RSMA-enabled ELAA system achieves better performance in terms of fairness and robustness under imperfect CSIT. Moreover, the GruCN model exhibits remarkable efficiency and effectiveness in precoder optimization.

This paper proposes a novel coupling-informed data-driven algorithm tailored for the concurrent estimation of frequency and angle within a uniform linear array (ULA), while addressing the complicating influence of mutual coupling. Leveraging the hybrid dynamic mode decomposition (DMD) methodology, termed as averaged DMD, we incorporate moving average techniques to achieve effective denoising. The averaged DMD further decomposes the received signal into eigenvalues and corresponding eigenvectors. The frequency information is derived from the eigenvalues and the corresponding eigenvectors represent the steering vectors of sources. Subsequently, mutual coupling is informed into the calibration of the steering vector for each source. Specifically, the calibration of corresponding eigenvectors leverage the inverse of the mutual coupling matrix, i.e., Toeplitz matrix, acquired through Schur decomposition. Then, the calibrated steering vectors facilitate the estimation of angles. The decomposition results of our proposed method reveal a significant one-to-one correspondence between eigenvectors and eigenvalues, enabling the automatic pairing of estimated frequencies and angles. Several numerical examples demonstrate the effectiveness and robust anti-noise properties of the proposed method, especially in scenarios where mutual coupling has a significant impact. Hence, our work contributes to the advancement of signal processing techniques in ULA applications, offering a promising avenue for enhanced performance in practical communication and radar systems.

In distributed training, workers collaboratively refine the global model parameters by pushing their updates to the Parameter Server and pulling fresher parameters for the next iteration. This introduces high communication costs for training at scale, and incurs unproductive waiting time for workers. To minimize the waiting time, existing approaches overlap communication and computation for deep neural networks. Yet, these techniques not only require the layer-by-layer model structures, but also need significant efforts in runtime profiling and hyperparameter tuning. To make the overlapping optimization simple and generic , in this article, we propose a new Parameter Server framework. Our solution decouples the dependency between push and pull operations, and allows workers to eagerly pull the global parameters. This way, both push and pull operations can be easily overlapped with computations. Besides, the overlapping manner offers a different way to address the straggler problem, where the stale updates greatly retard the training process. In the new framework, with adequate information available to workers, they can explicitly modulate the learning rates for their updates. Thus, the global parameters can be less compromised by stale updates. We implement a prototype system in PyTorch and demonstrate its effectiveness on both CPU/GPU clusters. Experimental results show that our prototype saves up to 54% less time for each iteration and up to 37% fewer iterations for model convergence, achieving up to 2.86× speedup over widely-used synchronization schemes.

In wireless channel sounding, accurately estimating multiple parameters within a multipath signal, such as azimuth, elevation, Doppler shift, and delay, necessitates addressing the challenges posed by the multidimensional harmonic retrieval (MHR) problem. To overcome these complexities, we propose a framework based on high-order dynamic mode decomposition (HODMD) that designed for robustly estimating frequencies of interest from high-dimensional sinusoidal signals, particularly in additive white Gaussian noise conditions. The HODMD approach, a hybrid algorithm amalgamating high-order singular value decomposition (HOSVD) and dynamic mode decomposition (DMD), operates by initially decomposing observed tensorial data into a core tensor and R mode matrices through HOSVD. Subsequently, DMD is applied to analyze each mode matrix individually, decomposing it into dynamic modes and DMD eigenvalues. The imaginary component of the DMD eigenvalues yields frequencies along the rth dimension. By uniformly applying this analysis to all mode matrices, multiple frequencies of interest are efficiently obtained. Furthermore, the integration of HOSVD, DMD, and moving average techniques in the proposed method is designed to mitigate noise interference during the MHR process. We conduct several numerical experiments and present a real-life example, i.e., the double-direction multiple-input and multiple-output (MIMO) channel sounding, to validate the effectiveness of the proposed HODMD approach. Results demonstrate that HODMD outperforms comparable approaches, particularly in scenarios characterized by high-signal-to-noise ratios. Notably, the proposed method exhibits the capability to estimate the number of tones in undamped cases during the decomposition process. Hence, our work contributes a practical and effective tensor-based solution to the MHR problem, particularly in the context of channel parameter estimation for MIMO systems.