We consider the problem of multi-flow transmission in wireless networks, where data signals from different flows can interfere with each other due to mutual interference between links along their routes, resulting in reduced link capacities. The objective is to develop a multi-flow transmission strategy that routes flows across the wireless interference network to maximize the network utility. However, obtaining an optimal solution is computationally expensive due to the large state and action spaces involved. To tackle this challenge, we introduce a novel algorithm called Dual-stage Interference-Aware Multi-flow Optimization of Network Data-signals (DIAMOND). The design of DIAMOND allows for a hybrid centralized-distributed implementation, which is a characteristic of 5G and beyond technologies with centralized unit deployments. A centralized stage computes the multi-flow transmission strategy using a novel design of graph neural network (GNN) reinforcement learning (RL) routing agent. Then, a distributed stage improves the performance based on a novel design of distributed learning updates. We provide a theoretical analysis of DIAMOND and prove that it converges to the optimal multi-flow transmission strategy as time increases. We also present extensive simulation results over various network topologies (random deployment, NSFNET, GEANT2), demonstrating the superior performance of DIAMOND compared to existing methods.

The objective of our work is to address the challenge of delivering time-sensitive data across a multi-hop wireless network. We aim to maximize the number of packets that reach their destination before the strict deadline. To achieve this, we introduce a deep reinforcement learning (DRL) approach that determines the optimal route, scheduling, and power allocation for each flow while complying with strict time constraints.

Multi-core processors are increasingly popular because they yield higher performance, but they also present new challenges for hard real-time systems in that they make it much more difficult to estimate a task's worst-case execution time (WCET). Partitioned cache architecture is being used to ease the problem by providing an isolated execution environment for each thread. Although simple to implement and use, this method may be sub-optimal with respect to both energy consumption and performance since it prevents taking advantage of information shared across threads for both instructions and data. This work presents a new cache architecture termed SPACE (Semi-Partitioned CachE) that makes it possible to leverage information sharing, yielding in turn a tighter WCET. The SPACE architecture together with our new WCET algorithm can be used to maintain the predictability of the execution time of the parallel threads while reducing the overall energy consumption of the system. The new proposed cache architecture was implemented using Verilog and deployed on a Xilinx MicroBlaze multi-core design for testing, validation and measurements. The application level experiments were conducted using the Chronos tool for estimation and the Wattch/SimpleScalar simulator for execution. Using three real-time programs-a radar tracker, a DES encryption algorithm, and an FM radio-we showed that SPACE together with the enhanced WCET algorithm reduce the average system WCET of these applications by 31 percent and reduce the actual energy consumption by 18 percent in comparison with other cache architectures.