Boosting Quantum Battery-Based IoT Gadgets via RF-Enabled Energy Harvesting

in Sensors (2022), 22(14), 1-19

The search for a highly portable and efficient supply of energy to run small-scale wireless gadgets has captivated the human race for the past few years. As a part of this quest, the idea of realizing a ... [more ▼]

The search for a highly portable and efficient supply of energy to run small-scale wireless gadgets has captivated the human race for the past few years. As a part of this quest, the idea of realizing a Quantum battery (QB) seems promising. Like any other practically tractable system, the design of QBs also involve several critical challenges. The main problem in this context is to ensure a lossless environment pertaining to the closed-system design of the QB, which is extremely difficult to realize in practice. Herein, we model and optimize various aspects of a Radio-Frequency (RF) Energy Harvesting (EH)-assisted, QB-enabled Internet-of-Things (IoT) system. Several RF-EH modules (in the form of micro- or nano-meter-sized integrated circuits (ICs)) are placed in parallel at the IoT receiver device, and the overall correspondingly harvested energy helps the involved Quantum sources achieve the so-called quasi-stable state. Concretely, the Quantum sources absorb the energy of photons that are emitted by a photon-emitting device controlled by a micro-controller, which also manages the overall harvested energy from the RF-EH ICs. To investigate the considered framework, we first minimize the total transmit power under the constraints on overall harvested energy and the number of RF-EH ICs at the QB-enabled wireless IoT device. Next, we optimize the number of RF-EH ICs, subject to the constraints on total transmit power and overall harvested energy. Correspondingly, we obtain suitable analytical solutions to the above-mentioned problems, respectively, and also cross-validate them using a non-linear program solver. The effectiveness of the proposed technique is reported in the form of numerical results, which are both theoretical and simulations based, by taking a range of operating system parameters into account. [less ▲]

Learning-Based Multiplexing of Grant-Based and Grant-Free Heterogeneous Services with Short Packets

in Proceedings of 2021 IEEE Global Communications Conference (GLOBECOM) (2021, December)

In this paper, we investigate the multiplexing of grant-based (GB) and grant-free (GF) device transmissions in an uplink heterogeneous network (HetNet), namely GB-GF HetNet, where the devices transmit ... [more ▼]

In this paper, we investigate the multiplexing of grant-based (GB) and grant-free (GF) device transmissions in an uplink heterogeneous network (HetNet), namely GB-GF HetNet, where the devices transmit their information using low-rate short data packets. Specifically, GB devices are granted unique time-slots for their transmissions. In contrast, GF devices can randomly select time-slots to transmit their messages utilizing the GF non-orthogonal multiple access (NOMA), which has emerged as a promising enabler for massive access and reducing access latency. However, random access (RA) in the GF NOMA can cause collisions and severe interference, leading to system performance degradation. To overcome this issue, we propose a multiple access (MA) protocol based on reinforcement learning for effective RA slots allocation. The proposed learning method aims to guarantee that the GF devices do not cause any collisions to the GB devices and the number of GF devices choosing the same time-slot does not exceed a predetermined threshold to reduce the interference. In addition, based on the results of the RA slots allocation using the proposed method, we derive the approximate closed-form expressions of the average decoding error probability (ADEP) for all devices to characterize the system performance. Our results presented in terms of access efficiency (AE), collision probability (CP), and overall ADEP (OADEP), show that our proposed method can ensure a smooth operation of the GB and GF devices within the same network while significantly minimizing the collision and interference among the device transmissions in the GB-GF HetNet. [less ▲]

Q-Learning-Based SCMA for Efficient Random Access in mMTC Networks With Short Packets

in Proceedings of 2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2021) (2021, September)

In massive machine-type communications (mMTC) networks, the ever-growing number of MTC devices and the limited radio resources have caused a severe problem of random access channel (RACH) congestion. To ... [more ▼]

In massive machine-type communications (mMTC) networks, the ever-growing number of MTC devices and the limited radio resources have caused a severe problem of random access channel (RACH) congestion. To mitigate this issue, several potential multiple access (MA) mechanisms including sparse code MA (SCMA) have been proposed. Besides, the short-packet transmission feature of MTC devices requires the design of new transmission and congestion avoidance techniques as the existing techniques based on the assumption of infinite data-packet length may not be suitable for mMTC networks. Therefore, it is important to find novel solutions to address RACH congestion in mMTC networks while considering SCMA and short-packet communications (SPC). In this paper, we propose an SCMA-based random access (RA) method, in which Q-learning is utilized to dynamically allocate the SCMA codebooks and time-slot groups to MTC devices with the aim of minimizing the RACH congestion in SPC-based mMTC networks. To clarify the benefits of our proposed method, we compare its performance with those of the conventional RA methods with/without Q-learning in terms of RA efficiency and evaluate its convergence. Our simulation results show that the proposed method outperforms the existing methods in overloaded systems, i.e., the number of devices is higher than the number of available RA slots. Moreover, we illustrate the sum rate comparison between SPC and long-packet communications (LPC) when applying the proposed method to achieve more insights on SPC. [less ▲]

Spectral coexistence for next generation wireless backhaul networks

in Access, Fronthaul and Backhaul Networks for 5G and Beyond (2017)

Cognitive approaches to enhance spectrum availability for satellite systems

in International Journal of Satellite Communications and Networking (2016)

Cognitive radio technologies have achieved in the recent years an increasing interest for the possible gain in terms of spectrum usage with respect to unshared approaches. While most of the attention has ... [more ▼]

Cognitive radio technologies have achieved in the recent years an increasing interest for the possible gain in terms of spectrum usage with respect to unshared approaches. While most of the attention has been devoted to the cognitive coexistence between terrestrial systems, the coexistence between terrestrial and satellite communications is also seen as a viable option. Cognitive radio for satellite communications (CoRaSat) has been a European Commission seventh Framework Program project funded under the ICT Call 8. CoRaSat aimed at investigating, developing, and demonstrating cognitive radio techniques in satellite communication systems for flexible and dynamic spectrum access. In this paper, the CoRaSat cognitive approaches and techniques, investigated, developed, and demonstrated as most relevant to satellite communications, are described. In particular, the focus is on spectrum awareness, that is, database and spectrum sensing approaches, and on spectrum exploitation algorithms, that is, resource allocation and beamforming algorithms, to enable the use of spectrum for satellite communications using shared bands. [less ▲]