Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

Abstract

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.

Introduction

Nowadays, Internet of Things (IoT) has been considered as an important part of future Internet and has received much attention from both academia and industry due to its great potential to connect millions of intelligent terminals to the Internet by using ubiquitous sensing and computing abilities [1], [2], [3], [4]. However, the energy supply of sensor nodes becomes a huge challenge for the application of massive connections and dangerous environments, such as nuclear power station, chemical plants, etc. In these places, it is inconvenient to replace the battery of devices frequently. Moreover, how to deploy massive IoT devices is also challenging for wireless communications due to the scarce spectrum [5].

To achieve green IoT and the requirements of sensor nodes’ energy supply, wireless-powered backscatter communication has been proposed [6], [7], [8]. In wireless-powered backscatter communication systems, wireless nodes can achieve backscatter communication (i.e., passive communication) by using the surrounding radio frequency (RF) signals (e.g., TV towers, cellular base stations) without requiring active RF transmission (e.g., relay transmission) in a low-power communication way. In particular, the backscatter transmitter can transmit data to the backscatter receiver by modulating and reflecting the surrounding RF signals. However, with the increasing number of IoT nodes, the limited spectrum resource may cannot support the massive sensor nodes. Thus, it is urgent to introduce new techniques for this problem. Currently, cognitive radio [9], [10], [11], [12], as an intelligent radio technology, has been proposed to achieve spectrum sharing between the primary system (e.g., the licensed system). In cognitive radio networks, secondary users can access the spectrum owned by primary users in a spectrum sharing way. This method can improve spectrum utilization and system capacity, but it increases the difficulty of resource allocation and interference control.

Based on the aforementioned discussion, it is reasonable to combine cognitive radio with wireless-powered backscatter communication for improving spectrum efficiency and achieving green communication in future IoT systems. In cognitive wireless-powered backscatter communication systems, secondary transmitters (STs) can harvest RF energy from the signals of primary users based on the RF energy harvesting (EH) technique to improve spectrum- and -energy utilization. Meanwhile, they cause a tolerable interference to primary users [13]. However, the conventional STs are usually designed as active devices, including oscillators and power amplifiers, which results in high power consumption [14], [15], [16]. In specific, secondary users with the backscattering function can transmit information via exploiting ambient RF signals from the legal systems (e.g., TV, cellular, or Wi-Fi systems) and achieve data transmission without requiring any active components [17]. Specifically, the authors in [18] studied the coverage probability and the achievable rates of primary users and secondary users for a cognitive wireless-powered backscatter communication network. In [19], the authors studied the physical layer security of cognitive ambient backscatter communications by deriving the outage probability and the intercept probability. The asymptotic behaviors are conducted for the outage probability under the high signal-to-noise ratio regime and intercept probability under the high main-to-eavesdropper ratio regime. In [20], the authors proposed a novel opportunistic ambient backscatter communication framework, where opportunistic spectrum sensing integrated with ambient backscatter communication and harvest-then-transmit (HTT) operation strategies were considered. In [21], the sum throughput maximization problem of in-band full-duplex cognitive wireless powered backscatter communication networks was studied to achieve improved spectral and time efficiency, where closed-form expressions for the optimal allocated time and energy to secondary base stations were derived and solved via a low complexity and efficient algorithm called joint optimal time and energy allocation. In [22], [23], the authors investigated the sum-rate maximization resource allocation problems of all secondary users by jointly optimizing reflection coefficients (RCs) and the transmission time. Considering the interference constraints of the primary users, in [24], the authors proposed a throughput-maximization resource allocation algorithm by dynamically adjusting the transmit power of STs between the backscattering transmission (BT) mode and the active transmission (AT) mode.

Although most of the above works [18], [19], [20], [21], [22], [23], [24] have made outstanding contributions to improve system performance, there is no work considering imperfect channel state information. Moreover, most of the overlay spectrum sharing modes cannot fully utilize the spectrum of primary users. The quality of service requirements of primary users and the joint optimization of transmission time, reflection coefficients, and transmit power have not been fully developed, which caused that the radio resource and user’s performance were not fully exploited. Besides, the above works assume that the primary channel has an idle state, which means that the performance of the cognitive wireless-powered backscatter communication network depends largely on the primary channel activity.

In order to improve system throughput and robustness, in this paper, we study an chance-constrained robust resource allocation problem for a cognitive wireless-powered backscatter communication network to maximize the system throughput and against channel uncertainties. The main contributions of this paper are given as follows.

• A total throughput maximization problem of secondary users is formulated with the throughput outage probability of secondary receivers (SRs) constraint and the rate outage probability constraint of the primary receiver (PR) by jointly optimizing transmission time of the BT mode and the AT mode, the transmit power and reflection coefficients of the STs. The formulated problem is non-convex and difficult to solve.

• To tackle this problem, we first transform the probability constraints into the deterministic ones via probability theory, and then use the variable substitution method to convert the deterministic problem into a convex one. A robust resource allocation algorithm is further proposed to obtain the closed-form solutions via Lagrange dual theory.

• Simulation results show that the proposed algorithm can effectively reduce outage probabilities and improve the total throughput of secondary users compared to various benchmark schemes.

The rest of this paper is organized as follows. In Section 2, the system model and problem formulation are given. In Section 3, a robust resource allocation algorithm is designed. In Section 4, the simulation results are given to show the effectiveness of the proposed algorithm. Section 5 concludes this paper. All abbreviations used in this paper are summarized in Table 1.