Sporadic ambient radio frequency signals can offer opportunistic spectrum and energy sources for backscatter communications, but they also induce unpredictable transmission interruptions in ambient backscatter wireless networks (AmBWNs). Integrating self-carrier-generative active transmissions with backscatter communications could significantly enhance transmission stability but require frequent mode switching to accommodate the ever-changing ambient radio frequency signals. However, this will result in frequent changes in network topology and link capacity, posing significant challenges in solving the network maximum flow problem in hybrid AmBWNs. To address this problem, we design a link-aware online maximum flow (OnMAXFlow) scheme to tackle agile and adaptive flow scheduling and communication mode selection. Specifically, we first employ an online learning framework to dynamically track changes in ambient signal strength and channel states, enabling real-time evaluation of link capacity. We then model the network maximum flow problem as a stochastic multi-armed bandit (MAB) problem and solve it with a Kullback-Leibler upper confidence bound (KL-UCB) algorithm. Our experimental evaluation results reveal that our OnMAXFlow scheme exhibits rapid convergence and superior adaptability against the varying network states, while maintaining spectrum efficiency and latency performance comparable to the Oracle scheme, which always selects the optimal transmission modes and paths.

Timely channel conditions are essential for vehicles to determine which base station (BS) to connect to, but acquiring them in mmWave vehicular networks is costly. Without additional channel estimations, the proposed asynchronous contextual kernelized upper confidence bound (ACK-UCB) algorithm estimates the current instantaneous transmission rates based on the historical transmission rates and contexts, such as the vehicle’s historical locations, velocities, and numbers of concurrent transmissions at the BS. ACK-UCB captures the nonlinear relationship between context and transmission rate, mapping the context into a reproducing kernel Hilbert space (RKHS), where a linear relationship becomes observable. To enhance estimation accuracy, a novel kernel function incorporating mmWave signal propagation characteristics is introduced in RKHS, allowing for a more precise evaluation of context similarity in relation to transmission rates. Furthermore, ACK-UCB encourages vehicles to share only reward distribution features after sufficient explorations, accelerating the learning process while keeping communication costs manageable. Numerical results show that ACK-UCB achieves 99.5%–100.5% network throughput and reduces 89%–91% communication cost of a benchmark algorithm that directly shares all local historical contexts and transmission rates, demonstrating the sharing efficiency of the ACK-UCB algorithm.

Vehicles require timely channel conditions to determine the base station (BS) to communicate with, but it is costly to estimate the fast-fading mmWave channels frequently. Without additional channel estimations, the proposed Distributed Kernelized Upper Confidence Bound (DK-UCB) algorithm estimates the current instantaneous transmission rates utilizing past contexts, such as the vehicle's location and velocity, along with past instantaneous transmission rates. To capture the nonlinear mapping from a context to the instantaneous transmission rate, DK-UCB maps a context into the reproducing kernel Hilbert space (RKHS) where a linear mapping becomes observable. To improve estimation accuracy, we propose a novel kernel function in RKHS which incorporates the propagation characteristics of the mmWave signals. Moreover, DK-UCB encourages a vehicle to share necessary information when it has conducted significant explorations, which speeds up the learning process while maintaining affordable communication costs.

Millimeter wave (mmWave) communication has emerged as a key technology enabling ultra-low latency and high throughput in vehicular communication. Usually, an appropriate decision on user association requires timely channel information between vehicles and base stations (BSs), which is challenging given a fast-fading mmWave vehicular channel. In this paper, we propose a low-complexity semi-distributed contextual correlated upper confidence bound (SD-CC-UCB) algorithm to establish an up-to-date user association between vehicles and BSs without explicit measurement of channel state information (CSI). Under a contextual multi-arm bandits framework, SD-CC-UCB learns and predicts the transmission rate given the location and velocity of the vehicle, which can adequately capture the intricate channel condition for a prompt decision on user association. Further, SD-CC-UCB efficiently identifies the set of candidate BSs which probably support supreme transmission rates by leveraging the correlated distributions of transmission rates on different locations. To further refine the learning transmission rate to candidate BSs, each vehicle deploys the Thompson Sampling algorithm by taking the interference among vehicles and handover into consideration. Numerical results show that our proposed algorithm achieves the network throughput within 100%–103% of a benchmark algorithm which requires perfect instantaneous CSI, demonstrating the effectiveness of SD-CC-UCB in vehicular communications.

A lot of knowledge has been accumulated and documented in the form of process models, standards, best practices, etc. The knowledge tells how a high quality software process should look like, in other words, which constrains should be fulfilled by a software process to assure high quality software products. Compliance checking for a predefined process against proper constrains is helpful to quality assurance. Checking the compliance of an actual performed process against some constrains is also helpful to process improvement. Manual compliance checking is time-consuming and error-prone, especially for large and complex processes. In this paper, we record the process knowledge by means of process pattern. We provide an automatic compliance checking approach for process models against constrains defined in process patterns. Checking results indicate where and which constrains are violated, and therefore suggests the focuses of future process improvement. We have applied this approach in three real projects and the experimental results are also presented.