131 research outputs found
Computation Offloading for Edge Computing in RIS-Assisted Symbiotic Radio Systems
In the paper, we investigate the coordination process of sensing and
computation offloading in a reconfigurable intelligent surface (RIS)-aided base
station (BS)-centric symbiotic radio (SR) systems. Specifically, the
Internet-of-Things (IoT) devices first sense data from environment and then
tackle the data locally or offload the data to BS for remote computing, while
RISs are leveraged to enhance the quality of blocked channels and also act as
IoT devices to transmit its sensed data. To explore the mechanism of
cooperative sensing and computation offloading in this system, we aim at
maximizing the total completed sensed bits of all users and RISs by jointly
optimizing the time allocation parameter, the passive beamforming at each RIS,
the transmit beamforming at BS, and the energy partition parameters for all
users subject to the size of sensed data, energy supply and given time cycle.
The formulated nonconvex problem is tightly coupled by the time allocation
parameter and involves the mathematical expectations, which cannot be solved
straightly. We use Monte Carlo and fractional programming methods to transform
the nonconvex objective function and then propose an alternating
optimization-based algorithm to find an approximate solution with guaranteed
convergence. Numerical results show that the RIS-aided SR system outperforms
other benchmarks in sensing. Furthermore, with the aid of RIS, the channel and
system performance can be significantly improved.Comment: 13 pages, 7 figure
Energy-efficient non-orthogonal multiple access for wireless communication system
Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed
Backscatter-assisted data offloading in OFDMA-based wireless powered mobile edge computing for IoT networks
Mobile edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinders IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, Backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this paper investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its non-convexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two sub-problems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closedform expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two sub-problems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 second in a 50-user network, which is tailored for ultralow latency applications of IoT networks
Deep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things
Backscatter communication (BC) technology offers sustainable solutions for
next-generation Internet-of-Things (IoT) networks, where devices can transmit
data by reflecting and adjusting incident radio frequency signals. In parallel
to BC, deep reinforcement learning (DRL) has recently emerged as a promising
tool to augment intelligence and optimize low-powered IoT devices. This article
commences by elucidating the foundational principles underpinning BC systems,
subsequently delving into the diverse array of DRL techniques and their
respective practical implementations. Subsequently, it investigates potential
domains and presents recent advancements in the realm of DRL-BC systems. A use
case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is
meticulously examined to highlight its potential. Lastly, this study identifies
and investigates salient challenges and proffers prospective avenues for future
research endeavors.Comment: 7,
Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions
Technology solutions must effectively balance economic growth, social equity,
and environmental integrity to achieve a sustainable society. Notably, although
the Internet of Things (IoT) paradigm constitutes a key sustainability enabler,
critical issues such as the increasing maintenance operations, energy
consumption, and manufacturing/disposal of IoT devices have long-term negative
economic, societal, and environmental impacts and must be efficiently
addressed. This calls for self-sustainable IoT ecosystems requiring minimal
external resources and intervention, effectively utilizing renewable energy
sources, and recycling materials whenever possible, thus encompassing energy
sustainability. In this work, we focus on energy-sustainable IoT during the
operation phase, although our discussions sometimes extend to other
sustainability aspects and IoT lifecycle phases. Specifically, we provide a
fresh look at energy-sustainable IoT and identify energy provision, transfer,
and energy efficiency as the three main energy-related processes whose
harmonious coexistence pushes toward realizing self-sustainable IoT systems.
Their main related technologies, recent advances, challenges, and research
directions are also discussed. Moreover, we overview relevant performance
metrics to assess the energy-sustainability potential of a certain technique,
technology, device, or network and list some target values for the next
generation of wireless systems. Overall, this paper offers insights that are
valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the
Communications Societ
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