499 research outputs found
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
This paper investigates the use of deep reinforcement learning (DRL) in a MAC
protocol for heterogeneous wireless networking referred to as
Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is
partially inspired by the vision of DARPA SC2, a 3-year competition whereby
competitors are to come up with a clean-slate design that "best share spectrum
with any network(s), in any environment, without prior knowledge, leveraging on
machine-learning technique". Specifically, this paper considers the problem of
sharing time slots among a multiple of time-slotted networks that adopt
different MAC protocols. One of the MAC protocols is DLMA. The other two are
TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC
protocols are TDMA and ALOHA. Yet, by a series of observations of the
environment, its own actions, and the resulting rewards, a DLMA node can learn
an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes
according to a specified objective (e.g., the objective could be the sum
throughput of all networks, or a general alpha-fairness objective)
Use of Q-Learning Approaches for Practical Medium Access Control in Wireless Sensor Networks
This paper studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks. Q-Learning is employed as an intelligent transmission strategy. We review the existing MAC protocols in the context of Q-learning. A recently-proposed, ALOHA and Q-Learning based MAC scheme, ALOHA-Q, is considered which improves the channel performance significantly with a key benefit of simplicity. Practical implementation issues of ALOHA-Q are studied. We demonstrate the performance of the ALOHA-Q through extensive simulations and evaluations in various testbeds. A new exploration/exploitation method is proposed to strengthen the merits of the ALOHA-Q against dynamic the channel and environment conditions. © 2016 Elsevier Lt
Q-learning Channel Access Methods for Wireless Powered Internet of Things Networks
The Internet of Things (IoT) is becoming critical in our daily life. A key technology of interest in this thesis is Radio Frequency (RF) charging. The ability to charge devices wirelessly creates so called RF-energy harvesting IoT networks. In particular, there is a hybrid access point (HAP) that provides energy in an on-demand manner to RF-energy harvesting devices. These devices then collect data and transmit it to the HAP. In this respect, a key issue is ensuring devices have a high number of successful transmissions.
There are a number of issues to consider when scheduling the transmissions of devices in the said network. First, the channel gain to/from devices varies over time. This means the efficiency to deliver energy to devices and to transmit the same amount of data is different over time. Second, during channel access, devices are not aware of the energy level of other devices nor whether they will transmit data. Third, devices have non-causal knowledge of their energy arrivals and channel gain information. Consequently, they do not know whether they should delay their transmissions in hope of better channel conditions or less contention in future time slots or doing so would result in energy overflow
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An Adaptive Tree Algorithm to Approach Collision-Free Transmission in Slo ed ALOHA
Tag anti-collision algorithms in RFID systems - a new trend
RFID is a wireless communication technology that provides automatic identification or tracking and data collection from any tagged object. Due to the shared communication channel between the reader and the tags during the identification process in RFID systems, many tags may communicate with the reader at the same time, which causes collisions. The problem of tag collision has to be addressed to have fast multiple tag identification process. There are two main approaches to the tag collision problem: ALOHA based algorithms and tree based algorithms. Although these methods reduce the collision and solve the problem to some extent, they are not fast and efficient enough in real applications. A new trend emerged recently which takes the advantages of both ALOHA and tree based approaches. This paper describes the process and performance of the tag anti-collision algorithms of the tree-ALOHA trend
Practical Evaluation of Low-complexity Medium Access Control Protocols for Wireless Sensor Networks
This thesis studies the potential of a novel approach to ensure more efficient and
intelligent assignment of capacity through medium access control (MAC) in
practical wireless sensor networks (WSNs), whereby Reinforcement Learning
(RL) is employed as an intelligent transmission strategy. RL is applied to framed
slotted-ALOHA to provide perfect scheduling. The system converges to a steady
state of a unique transmission slot assigned per node in single-hop and multi-hop
communication if there is sufficient number of slots available in the network,
thereby achieving the optimum performance.
The stability of the system against possible changes in the environment and
changing channel conditions is studied. A Markov model is provided to represent
the learning behaviour, which is also used to predict how the system loses its
operation after convergence. Novel schemes are proposed to protect the lifetime
of the system when the environment and channel conditions are insufficient to
maintain the operation of the system.
Taking real sensor platform architectures into consideration, the practicality of
MAC protocols for WSNs must be considered based on hardware
limitations/constraints. Therefore, the performance of the schemes developed is
demonstrated through extensive simulations and evaluations in various test-beds.
Practical evaluations show that RL-based schemes provide a high level of
flexibility for hardware implementation
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