Feng Ye

Next generation communications demand better spectrum management, lower latency, and guaranteed quality-of-service (QoS). Recently, artificial intelligence (AI) has been widely introduced to advance these aspects in next generation wireless systems. However, such AI applications suffer from limited training data, low robustness, and poor generalization capabilities. To address these issues, we introduce a model-driven deep unfolding (DU) algorithm in this paper to address the gap between traditional model-driven communication algorithms and data-driven deep learning. Focusing on the QoS-aware rate-splitting multiple access (RSMA) resource allocation problem in multi-user communications, a conventional fractional programming (FP) algorithm is first applied as a benchmark. The solution is further refined using projection gradient descent (PGD). DU is employed to further accelerate convergence, thereby improving the efficiency of PGD. Moreover, the feasibility of results is guaranteed by designing a low-complexity projection based on scale factors, and adding violation control mechanisms into the loss function that minimizes error rates. Finally, we provide a detailed analysis of the computational complexity and analysis design of the proposed DU algorithm. Extensive simulations are conducted and the results demonstrate that the proposed DU algorithm can reach the optimal communication efficiency with only 1.1% violation rate for the five-layer DU. The DU algorithm also exhibits robustness in out-of-distribution tests and can be effectively trained with as few as 50 samples.

The orthogonal time frequency space (OTFS) is one promising approach for the future wireless system with highmobility users. This paper proposed a channel estimation for a multiple-input multiple-output (MIMO) OTFS system with high delay resolution in high-mobility environment. Shifts of the path indices and path appearance/disappearance are studied in this work. Due to the high mobility of the user and high delay resolution, the studied system can be more sensitive to index shift of paths. Considering the fractional components in OTFS channel, only the significant CSI elements are processed with minimum performance loss. A Deep Learning supported 3-phase scheme is developed. An auto-encoder (AE) is first deployed for compressed channel features, followed by a recurrent neural network (RNN) based scheme that provides a rough channel prediction. The indices of significant elements in the predicted channel are then extracted using the decoder function of the AE process. Finally, the values of the significant elements are reconstructed via the Least Squares based method. Analysis is provided on erroneous path predictions, i.e., missing existing paths or detecting nonexistent paths. Simulation results demonstrate that the proposed 3-phase scheme can outperform the existing channel prediction schemes with a much better accuracy and lower bit error rate in high-mobility use cases.

Application offloading plays a crucial role in application deployment in edge-cloud computing. However, finding the optimal solution for application offloading is challenging due to the heterogeneous resources, computing dependency of tasks, and complex network. Existing research on application offloading problems often assumes that the communication delay between the edge and the cloud (inter-side) is symmetrical or that within the cloud or edge (intra-side) can be omitted. However, this assumption is not practical considering the distinct features of the clouds and the edge clusters. Therefore, we study application offloading in the heterogeneous edge-cloud environment by considering both intra-side communication delay between tasks assigned to the same side and asymmetry inter-side communication delay between edge and cloud sides. We first focus on the specific circumstances with a boundary condition that lead to an optimal offloading solution in a heterogeneous edge-cloud environment. Then we study the general case by designing an iterative algorithm with maximum gain technique to solve it. Furthermore, considering various bandwidths within each side and resource capacities of physical nodes, we develop two efficient algorithms by combining minimum cut and maximum gain approaches. Both simulations and real trace-based evaluations are conducted to validate that the proposed algorithms outperform existing solutions.

Edge artificial intelligence (AI) has emerged as a promising paradigm catering to overwhelming explosions of smart applications, by offloading the computation-intensive deep neural network (DNN) inference to an edge network for processing. The surging of edge AI brings new vigor and vitality to shape the prospect of smart transportation. However, when considering the cooperation between heterogeneous edge devices and the operation precedence between DNN tasks, it is still challenging to decompose a DNN across multiple edge devices in an edge network with a general topology to minimize DNN inference delay. In this paper, we devise a polynomial-time optimal solution to the DNN inference offloading problem for smart roadside applications, in which the roadside edge network is usually organized with chain topology. Specifically, the DNN inference offloading problem for the roadside edge network with chain topology is transformed into an equivalent graph optimization problem. Theoretical analysis and extensive evaluations validate the performance of the proposed solution in minimizing the total inference delay.