The challenging issue of managing human eye irradiance from a light source poses a critical hurdle in uplink data transmission within visible light communication (VLC) systems. Eye irradiance and the data rate are influenced by the transmit power. Therefore, the transmit power must be optimized to achieve the maximum data rate while preventing the irradiance of the eye. To tackle this, multiple transmitters and receivers with partitioned intelligent reflecting surface (IRS) units cooperative VLC system is proposed in this work. Further, the formulation of an optimization problem is done in order to minimize the total transmit power requirements by putting constraints on achievable data rates. Furthermore, the optimization problem is addressed analytically and verified through simulations by employing the proposed evolutionary power minimization algorithm (EPMA). Our study shows that optimization and IRS make the system energy efficient. In addition, extensive numerical findings indicate that an IRS with optimization substantially reduces the transmit power requirements and improves the data rate.

Multiarmed bandits (MABs) are online machine learning algorithms that aim to identify the optimal arm without prior statistical knowledge via the exploration-exploitation tradeoff. The performance metric, regret, and computational complexity of the MAB algorithms degrade with the increase in the number of arms,K. In applications such as wireless communication, radar systems, and sensor networks,K, i.e., the number of antennas, beams, bands, etc., is expected to be large. In this work, we consider focused exploration-based MAB, which outperforms conventional MAB for largeK, and its mapping on various edge processors and multiprocessor system on a chip (MPSoC) via hardware-software co-design (HSCD) and fixed point (FP) analysis. The proposed architecture offers 67% reduction in average cumulative regret, 84% reduction in execution time on edge processor, 97% reduction in execution time using FPGA-based accelerator, and 10% savings in resources over state-of-the-art MABs for large$K=100$.

The opportunistic spectrum access (OSA) algorithms allow secondary users (SUs) to exploit vacant channels with an aim to maximize overall spectrum utilization/throughput. The design of OSA algorithm is challenging for ad hoc networks due to lack of coordination among SUs and unknown channel statistics. It becomes even more challenging for the dynamic networks where the SUs can enter or leave the network any time without prior agreement. Most of the existing algorithms assume either prior knowledge of the number of SUs or need wideband sensing to sense all channels simultaneously to guarantee optimal channel allocation among SUs. Our goal in this paper is to develop distributed OSA algorithm for dynamic ad hoc networks that offers higher throughput without compromising on the number of SUs collisions. The proposed distributed algorithm is based on multi-player multi-arm bandit framework and they allow SUs to independently estimate the number of other SUs and channel statistics. We derive the upper bounds on the throughput loss (regret) and number of collisions. Exhaustive synthetic results and experimental results on universal software radio peripherals (USRP) based testbed validate our claims and superiority of the proposed algorithm.

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiment.

The dynamic spectrum access (DSA) algorithms aim to maximize network throughput by ensuring orthogonal channel allocation among secondary users (SUs) in cognitive radio network (CRN). The DSA is challenging in the infrastructure-less CRN (ICRN) due to lack of coordination among SUs. Most of the existing algorithms assume prior knowledge of the number of active SUs to guarantee orthogonalization. The musical chair (MC) algorithm is the state-of-the-art algorithm where the number of SUs are unknown and estimated independently at each SU. However, MC algorithm incurs a significant number of SU collisions and hence, the aggregate throughput is poor compared to centralized algorithms. In this paper, we setup DSA in ICRN as a multi-player multi-armed Bandit problem and develop distributed algorithm which allows SUs to orthogonalize in optimal channels with a negligible number of collisions. We also develop realistic USRP based testbed to validate the performance of the DSA algorithms in the real radio environment. Experimental results validate the superiority of the proposed algorithm over existing algorithms in terms of network throughput and fairness in channel access. Furthermore, a fewer number of collisions and hence, fewer retransmissions lead to efficient use of battery power, spectrum and time.

An opportunistic spectrum access (OSA) in the infrastructure-less network has received significant attention in last few years due to their ability to improve spectrum utilization as well as usefulness in the infrastructure-less networks established for disaster relief and military applications. The main research problem for feasible implementation of such network is to achieve coordination among secondary users (SUs) (i.e. unlicensed users). Existing algorithms incur a significant number of collisions which in turn require retransmissions and hence, lead to inefficient use of battery power, spectrum and time. In this paper, we set-up the problem as a multi-player Bandit and develop a new distributed algorithm which allows SUs to select one of the top channels with a significantly fewer number of collisions. We show that the proposed algorithm has constant regret with high confidence. We validate our claims and the superiority of the proposed algorithm over existing state-of-the-art algorithms through the exhaustive simulated experiments as well as a realistic USRP based experiments in the real radio environment.

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. Moreover, they are vulnerable to Denial of Service attacks. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider jammers launching coordinated attack where they select non-overlapping channels in each time slot and can lead to significantly higher number of collisions for SUs than uncoordinated attack. We setup the problem as a multiplayer bandit and develop distributed learning algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiments.