Xingwei Wang’s research while affiliated with Northeastern University and other places

Energy efficiency (EE) has become a key performance indicator in designing future wireless networks. In this regard, we propose a novel two reconfigurable intelligent surfaces (RISs) assisted coordinated multipoint (CoMP) network, where two RISs cooperate to reduce energy consumption by constructing an intelligent radio environment. Aiming to maximize this system’s energy efficiency (EE), the joint optimization problem on the cooperative beamforming among base stations (BSs) and passive beamforming among RISs, subject to each user’s data rate requirement, is formulated. An alternative optimization algorithm is proposed by decoupling it into a cooperative beamforming subproblem among BSs and a cooperative reflective coefficients design subproblem among RISs. Dinkelbach’s transform and complex fractional programming algorithms are also invoked to transform these two subproblems into convex optimization problems and obtain the optimal solution for each sub-problem. Numerical results validate the proposed algorithm’s feasibility, fast convergence, and flexibility. It also shows that the proposed scheme with cooperative beamforming design for both BSs and RISs outperforms that without RIS and that with randomly initialized RISs.

In this article, we explore the opportunities and benefits of integrating federated learning (FL) and blockchain technologies to build an adaptable and secure Knowledge-Defined Networking (KDN) system. Our aim is to enhance network performance by ensuring self-learning, self-adapting, and self-adjustment capabilities in dynamic and decentralized network environments. The proposed conceptual architecture, KDN-FLB, also strategically addresses critical challenges in knowledge sharing and privacy preservation within network environments. We discuss the constituents, architecture, processes, and use cases of KDN-FLB in contemporary networking applications. Additionally , we analyze the benefits, challenges, and future prospects associated with KDN-FLB, making it more intelligent for large-scale, dynamic, and decentralized network environments.

Medication recommender is to suggest appropriate medication combinations based on a patient's health history, e.g., diagnoses and procedures. Existing works represent different diagnoses/procedures well separated by one-hot encodings. However, they ignore the latent hierarchical structures of these medical terms, undermining the generalization performance of the model. For example, "Respiratory Diseases", "Chronic Respiratory Diseases" and "Chronic Bronchiti" have a hierarchical relationship, progressing from general to specific. To address this issue, we propose a novel hierarchical encoder named HIER to hierarchically represent diagnoses and procedures, which is based on standard medical codes and compatible with any existing methods. Specifically, the proposed method learns relation embedding with a self-supervised objective for incorporating the neighbor hierarchical structure. Additionally, we develop the position encoding to explicitly introduce global hierarchical position. Extensive experiments demonstrate significant and consistent improvements in recommendation accuracy across four baselines and two real-world clinical datasets.

Recently, computation offloading methods have greatly improved the Quality of Experience (QoE) in Multi-access Edge Computing (MEC) by offloading tasks to the edge servers. Since well-coordinated actions of Terminal Devices (TDs) are critical to improving the performance of the entire individual system, many practical MEC-based applications, i.e., firefighting robots and unmanned aerial vehicles, require great teamwork among TDs. However, real-world scenarios are usually bound by resource conditions. For instance, network connectivity may weaken or experience interruptions during emergency situations. In cases where the communication medium is utilized by multiple TDs, achieving effective coordination poses a significant challenge. In this paper, we propose a computation offloading scheme based on Scheduled Multi-agent Deep Reinforcement Learning (SMDRL) to make the most efficient decision in a resource-constrained scenario. First, we design a virtual energy queue based on the MEC system and maximize the QoE (related to service delay and energy consumption) in a real-time manner. Subsequently, we propose a scheduled multi-agent deep reinforcement learning algorithm to support each TD in learning how to encode messages, select actions, and schedule itself based on the received messages. Furthermore, a TopK mechanism is introduced. This mechanism chooses the most crucial TDs to broadcast their messages, and then the computation offloading problem in a communication-constrained MEC environment can be solved in a low-communication manner. Also, we prove that even under limited communication conditions, our proposed methods can still lead to the close-to-optimal performance. The final performance analysis shows that the developed scheme has significant advantages over other representative schemes.

Existing H.265/HEVC selective encryption (SE) schemes do not take into account the semantic features of input videos, nor do they adjust the encryption syntax elements according to the sensitivity of video content, which greatly limits their applicability. In this paper, we propose a chaos-based tunable H.265/HEVC SE scheme with semantic understanding. First, a deep hashing network is employed to identify content-sensitive videos by analyzing the semantic features of video sequences. Then, the non-sensitive videos and the retrieved sensitive ones are encrypted with different encryption strengths, respectively. Specifically, for non-sensitive videos, seven syntax elements with bypass-coded bins are selected for encryption at a constant bit rate. Hence, the encrypted bitstream keeps exactly the same compression ratio. To provide heavier visual distortion for content-sensitive videos, the regular-coded bins of four syntax elements and the intra prediction mode (IPM) are encrypted based on their corresponding encoding characteristics as well. Additionally, the selected syntax elements are all masked using a keystream generated by a chaotic system to ensure real-time constraints. Experimental results demonstrate that our suggested scheme offers format compatibility and is secure against all common attacks. Meanwhile, it outperforms state-of-the-art SE schemes in terms of security strength. Furthermore, the proposed scheme can be flexibly used in a wide range of applications according to the user’s requirements for encryption strength and bit rate.

Model merging-based multitask learning (MTL) offers a promising approach for performing MTL by merging multiple expert models without requiring access to raw training data. However, in this paper, we examine the merged model's representation distribution and uncover a critical issue of "representation bias". This bias arises from a significant distribution gap between the representations of the merged and expert models, leading to the suboptimal performance of the merged MTL model. To address this challenge, we first propose a representation surgery solution called Surgery. Surgery is a lightweight, task-specific module that aligns the final layer representations of the merged model with those of the expert models, effectively alleviating bias and improving the merged model's performance. Despite these improvements, a performance gap remains compared to the traditional MTL method. Further analysis reveals that representation bias phenomena exist at each layer of the merged model, and aligning representations only in the last layer is insufficient for fully reducing systemic bias because biases introduced at each layer can accumulate and interact in complex ways. To tackle this, we then propose a more comprehensive solution, deep representation surgery (also called SurgeryV2), which mitigates representation bias across all layers, and thus bridges the performance gap between model merging-based MTL and traditional MTL. Finally, we design an unsupervised optimization objective to optimize both the Surgery and SurgeryV2 modules. Our experimental results show that incorporating these modules into state-of-the-art (SOTA) model merging schemes leads to significant performance gains. Notably, our SurgeryV2 scheme reaches almost the same level as individual expert models or the traditional MTL model. The code is available at \url{https://github.com/EnnengYang/SurgeryV2}.