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Register a new userOptimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning
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Nguyen Van Huynh
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this article, we propose an optimal and low-complexity dynamic spectrum access framework for RF-powered ambient backscatter system. In this system, the secondary transmitter not only harvests energy from ambient signals (from incumbent users), but also backscatters these signals to its receiver for data transmission. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to learn from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment, but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods.
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For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSUs decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior- and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods.
RF-powered backscatter communication is a promising new technology that can be deployed for battery-free applications such as internet of things (IoT) and wireless sensor networks (WSN). However, since this kind of communication is based on the ambient RF signals and battery-free devices, they are vulnerable to interference and jamming. In this paper, we model the interaction between the user and a smart interferer in an ambient backscatter communication network as a game. We design the utility functions of both the user and interferer in which the backscattering time is taken into the account. The convexity of both sub-game optimization problems is proved and the closed-form expression for the equilibrium of the Stackelberg game is obtained. Due to lack of information about the system SNR and transmission strategy of the interferer, the optimal strategy is obtained using the Q-learning algorithm in a dynamic iterative manner. We further introduce hotbooting Q-learning as an effective approach to expedite the convergence of the traditional Q-learning. Simulation results show that our approach can obtain considerable performance improvement in comparison to random and fixed backscattering time transmission strategies and improves the convergence speed of Q-Learning by about 31%.
With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this paper, we investigate the distributed DSA problem for multi-user in a typical multi-channel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we proposed a centralized off-line training and distributed on-line execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent Q-network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of cognitive radio network in distributed fashion without coordination information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. From the simulation results, we can observe that the proposed algorithm can converge fast and achieve almost the optimal performance.
We consider an ambient backscatter communication (AmBC) system aided by an intelligent reflecting surface (IRS). The optimization of the IRS to assist AmBC is extremely difficult when there is no prior channel knowledge, for which no design solutions are currently available. We utilize a deep reinforcement learning-based framework to jointly optimize the IRS and reader beamforming, with no knowledge of the channels or ambient signal. We show that the proposed framework can facilitate effective AmBC communication with a detection performance comparable to several benchmarks under full channel knowledge.
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational resources (processor usage) are allocated to stochastic arrivals of network slice requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements, and if feasible, it is served with available communication and computational resources allocated over its requested duration. As each decision of resource allocation makes some of the resources temporarily unavailable for future, the myopic solution that can optimize only the current resource allocation becomes ineffective for network slicing. Therefore, a Q-learning solution is presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon subject to communication and computational constraints. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions. While reinforcement learning sustains scalable performance as the number of served users increases, it can also be effectively used to assign resources to network slices when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks.
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