Massive Multiple Input Multiple Output (MIMO) is a cornerstone technology for achieving high capacity and spectral efficiency. A key challenge in frequency division duplex (FDD) massive MIMO systems lies in obtaining accurate downlink (DL) channel state information (CSI), as the absence of uplink (UL)DL reciprocity hinders conventional estimation methods, creating a bottleneck in system performance. To address this issue, various approaches have been developed, broadly categorized into Conversion and Feedback approaches. However, partial reciprocity and quantization loss in feedback codebook design make accurate DL CSI acquisition a persistent challenge. In this paper, we propose a novel CGAN (Conditional Generative Adversarial Network)-based CSI-fusion framework that integrates both the UL channel statistics obtained from sounding reference signals (SRS) and the DL feedback from user equipments (UEs). These two sources of DL CSI-related information are fused and utilized as conditional inputs to CGAN to map the UL channel statistics to DL CSI. The proposed CGAN-based CSI-Fusion framework significantly enhances DL CSI acquisition accuracy, offering a practical solution to overcoming DL CSI acquisition challenges.

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adjust their individual learning rates and local training iterations, adapting to their instantaneous computing powers. The convergence upper bound of FLARE is established rigorously under a general setting with non-convex models in the presence of non-i.i.d. datasets and imbalanced computing powers. By minimizing the upper bound, we further optimize the scheduling of FLARE to exploit the channel heterogeneity. A nested problem structure is revealed to facilitate iteratively allocating the bandwidth with binary search and selecting devices with a new greedy method. A linear problem structure is also identified and a low-complexity linear programming scheduling policy is designed when training models have large Lipschitz constants. Experiments demonstrate that FLARE consistently outperforms the baselines in test accuracy, and converges much faster with the proposed scheduling policy.

Channel fading can have a strong impact on the convergence of over-the-air federated edge learning (OTA-FEEL). This paper develops a new and optimal power control policy to minimize the optimality gap of OTA-FEEL under any independently and identically distributed fading. Specifically, we reveal that the optimal power control policy takes a structure where the variance of the effective channel from a device to the server should be minimized when its mean is given. Following this structure, a novel nested optimization algorithm is developed to iteratively minimize the variance using the Lagrange-dual method and then optimize the mean of the effective channel using one-dimensional search. A quasi-closed-form expression of the optimal power control policy is derived. It is shown that the optimal adaptive power control for OTA-FEEL performs an integration of the “channel inverting” strategy and the opposite “channel-proportional” strategy to balance the mean and variance of the effective channel. We also generalize the new policy when the channel statistics are unknown a-priori, and show that the optimal policy can be asymptotically approached over time. Simulations confirm the superiority of the policy to its existing alternatives in the convergence speed and learning accuracy of OTA-FEEL.

Governance by policies is an important feature in service-oriented architecture (SOA). This paper introduces a collaboration policy generation technique to govern dynamic collaboration in SOA systems. Dynamic collaboration allows two parties not knowing each other establish their collaboration protocol at runtime. Traditional SOA allows parties to collaborate but with a known collaboration protocol before execution. In dynamic collaboration, as collaboration protocols will be negotiated and determined at runtime, policies need to be generated at runtime to manage the interaction. This paper presents the concepts, architecture, and illustrative examples that demonstrate the dynamic collaboration policy generation technique

Web services are emerging technologies that are changing the way we develop and use computer systems and software. Current Web services testing techniques are unable to assure the desired level of trustworthiness, which presents a barrier to WS applications in mission and business critical environments. This paper presents a framework that assures the trustworthiness of Web services. New assurance techniques are developed within the framework, including specification verification via completeness and consistency checking, specification refinement, distributed Web services development, test case generation, and automated Web services testing. Traditional test case generation methods only generate positive test cases that verify the functionality of software. The Swiss cheese test case generation method proposed in this paper is designed to perform both positive and negative testing that also reveal the vulnerability of Web services. This integrated development process is implemented in a case study. The experimental evaluation demonstrates the effectiveness of this approach. It also reveals that the Swiss cheese negative testing detects even more faults than positive testing and thus significantly reduces the vulnerability of Web services.