Noor Biswas

Swatantra Kafle, Jithin Jagannath, Zackary Kane et al.

We tackle the problem of joint frequency and power allocation while emphasizing the generalization capability of a deep reinforcement learning model. Most of the existing methods solve reinforcement learning-based wireless problems for a specific pre-determined wireless network scenario. The performance of a trained agent tends to be very specific to the network and deteriorates when used in a different network operating scenario (e.g., different in size, neighborhood, and mobility, among others). We demonstrate our approach to enhance training to enable a higher generalization capability during inference of the deployed model in a distributed multi-agent setting in a hostile jamming environment. With all these, we show the improved training and inference performance of the proposed methods when tested on previously unseen simulated wireless networks of different sizes and architectures. More importantly, to prove practical impact, the end-to-end solution was implemented on the embedded software-defined radio and validated using over-the-air evaluation.

Jithin Jagannath, Anu Jagannath, Justin Henney et al.

The proliferation of wireless devices and their ever increasing influence on our day-to-day life is very evident and seems irreplaceable. This exponential growth in demand, both in terms of the number of devices and Quality of Service (QoS) had spawned the concept of cross-layer optimization several years ago. The primary goal of the cross-layer approach was to liberate the strict boundary between the layers of the traditional Open Systems Interconnection (OSI) protocol stack. The initial decade focused on establishing the theoretical feasibility of this revolutionary concept and gauging the effectiveness and limits of this idea. During the next phase, the advent of software defined radios (SDR) accelerated the growth of this domain due to its added flexibility. Yet, there has been a gaping abyss between solutions designed in theory and ones deployed in practice. To establish this, we first present an elaborate survey of the cross-layer protocol stack literature. Next, we briefly discuss how a commercial off-the-shelf (COTS), low SWaP (Size, Weight, and Power) embedded SDR (e-SDR) was transformed into a standalone, fieldable transceiver. Thereafter, we provide the software design ethos that focuses on efficiency and flexibility such that the optimization objectives and cross-layer interactions can be reconfigured rapidly. To demonstrate our claims, we provide results from extensive outdoor over-the-air experiments in various settings with up to 10-node network topologies. The results from the field trials demonstrate high reliability, throughput, and dynamic routing capability. To the best of our knowledge, this is the first time in literature, a COTS e-SDR has been leveraged to successfully design a cross-layer optimized transceiver that is capable of forming an ad hoc network that provides high throughput and high reliability in a ruggedized, weatherized, and fieldable form factor.