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A number of problems in relational Artificial Intelligence can be viewed as Stochastic Constraint Optimization Problems (SCOPs). These are constraint optimization problems that involve objectives or constraints with a stochastic component.…
Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio (CR) was introduced as a technique to attain high spectral efficiency. As the number of secondary users (SUs) connecting to the cognitive radio network is on the…
Green low power networking with the least requirement of dedicated radio resources is need of the hour which has led to the upsurge of backscatter communication (BSC) technology. However, this inherent potential of BSC is challenged by…
Symbiotic radio (SR) networks are possible solutions to the future low-power wireless communications for massive Internet of Things devices. In this paper, we investigate a multiple-input-single-output non-orthogonal multiple access (NOMA)…
Optimal queueing control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider a two-hop half-duplex relaying system with random channel connectivity. The relay is equipped with a…
In this paper, we propose a movable antenna (MA) empowered scheme for symbiotic radio (SR) communication systems. Specifically, multiple antennas at the primary transmitter (PT) can be flexibly moved to favorable locations to boost the…
Ambient Internet of Things networks use low-cost, low-power backscatter tags in various industry applications. By exploiting those tags, we introduce the integrated sensing and backscatter communication (ISABC) system, featuring multiple…
Dual-function radar-communication (DFRC), which can simultaneously perform both radar and communication functionalities using the same hardware platform, spectral resource and transmit waveform, is a promising technique for realizing…
This paper studies a cooperative cognitive radio network where two primary users (PUs) exchange information with the help of a secondary user (SU) that is equipped with multiple antennas and in return, the SU superimposes its own messages…
An integrated sensing and backscatter communication (ISABC) system is introduced herein. This system features a full-duplex (FD) base station (BS) that seamlessly merges sensing with backscatter communication and supports multiple users.…
This paper considers a general stochastic resource allocation problem that arises widely in wireless networks, cognitive radio, networks, smart-grid communications, and cross-layer design. The problem formulation involves expectations with…
Integrated sensing and communication (ISAC) has been recognized as one of the key technologies for future wireless networks, which potentially need to operate in multiple frequency bands to satisfy ever-increasing demands for both…
The application of renewable energy is a promising solution to realize the Green Communications. However, if the cellular systems are solely powered by the renewable energy, the weather dependence of the renewable energy arrival makes the…
Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The communication overhead is a key bottleneck that hinders…
Bistatic backscatter communication facilitates ubiquitous, massive connectivity of passive tags for future Internet-of-Things (IoT) networks. The tags communicate with readers by reflecting carrier emitter (CE) signals. This work addresses…
In this paper, we employ active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASRIS) to aid in establishing and enhancing communication within a commensal symbiotic radio (CSR) network. Unlike traditional…
In the future, sensor nodes or Internet of Things (IoTs) will be tasked with sampling the environment. These nodes/devices are likely to be powered by a Hybrid Access Point (HAP) wirelessly, and may be programmed by the HAP with a {\em…
Multistage stochastic programming deals with operational and planning problems that involve a sequence of decisions over time while responding to realizations that are uncertain. Algorithms designed to address multistage stochastic linear…
Gradient-based optimization methods implemented on distributed computing architectures are increasingly used to tackle large-scale machine learning applications. A key bottleneck in such distributed systems is the high communication…
Stochastic Gradient Descent (SGD) is the standard numerical method used to solve the core optimization problem for the vast majority of machine learning (ML) algorithms. In the context of large scale learning, as utilized by many Big Data…