Signal Detection for Ambient Backscatter Communication with OFDM Carriers

sensors Article Signal Detection for Ambient Backscatter Communication with OFDM Carriers Thu L. N. Nguyen 1 , Yoan Shin 1,∗, Jin Young Kim 2 and Dong In Kim 3 1 School of Electronic Engineering, Soongsil University, Seoul 06978, Korea; [email protected] 2 Department of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, Korea; [email protected] 3 College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea; [email protected] * Correspondence: [email protected] Received: 18 December 2018; Accepted: 23 January 2019; Published: 26 January 2019 Abstract: Ambient backscatter communication (AmBC) is considered as a promising future emerging technology. Several works on AmBC have been proposed thanks to its convenience and low cost property. This paper focuses on finding the optimal energy detector at the receiver side and estimating the corresponding bit error rate for the communication system utilizing the AmBC. Through theoretical and numerical analyses, we present two important results. First, we improve the existing energy detector by calculating the optimal averaging power orders. Second, we take advantage of the early work on orthogonal frequency division multiplexing (OFDM), where the repeating structure of ambient OFDM signals is exploited to cancel out the direct-link interference by using a cyclic prefix, then provide a test statistic in which optimal detection threshold and optimal power order are derived accordingly. The study reveals the inherent limitation of AmBC energy detectors and provides a guidance for achieving optimal power order for a given significance level. Keywords: ambient backscatter communication; energy detector; OFDM; test statistic 1. Introduction Ambient backscatter communication (AmBC) is a new mechanism in which a device can communicate with others by backscattering ambient radio-frequency (RF) signals (e.g, WiFi, TV signals) without any additional power suppliers [1,2]. In traditional backscatter communication such as radio-frequency identification (RFID system), a device conveys the data by modulating its reflections of an incident RF signal, which takes an expensive process for generating radio waves. For instance, a reader generates a continuous carrier wave then broadcasts it. A tag receives the signal and modulates it, and backscatters to the reader. Thus, the backscattered signal has a long delay and additional path loss. Moreover, as communicating and computing devices become smaller and abundant, powering them becomes more difficult because they require more batteries, cost, and recharging/replacement that is impractical at large scales. The AmBC solves this problem by utilizing existing RF signals, rather than generating their own radio waves. Since the RF signals are reused, the AmBC is more power-efficient and much cheaper than the traditional radio communication [3–5]. Therefore, the AmBC is the key building block that enables internet-of-things (IoT) and ubiquitous communication among devices with cheap and nearly zero maintenance. Basically, RF-power devices employing the AmBC must face three main challenging issues [5,6]. First, since the backscatter signals are weak, the problem of signal detection with small changes needs to be investigated. Second, traditional backscatter receivers are constructed from powered components (e.g., oscillators), while the AmBC ones use already available RF sources, thereby reducing Sensors 2019, 19, 517; doi:10.3390/s19030517 www.mdpi.com/journal/sensors Sensors 2019, 19, 517 2 of 14 extra hardware cost and power consumption. In terms of spectrum sensing, the spectrum resource utilization can be much more improved since it does not need to allocate a new frequency spectrum [7]. For instance, by taking the advantage of existing RF signals in the air, it does not required any additional deployment like the RFID reader that suffers more installation and maintenance costs. The question is either how to build a network that enables ambient backscattering or build new complex digital signal processing techniques. Third, how to operate a distributed multiple access protocol and supporting the functionalities required for the AmBC should be considered. In this paper, we attempt to solve the first problem, which is the design of reader detector to recover the tag bits. There are several related works on this topic such as [5,8,9]. In [5], the authors first performed energy detection without the ability to directly measure the energy on the medium. The key insight is that if the transmitter backscatter information at a lower rate than the ambient signals, then one is able to design a receiver that can separate the two signals by leveraging the difference in communication rates. Thus, the results are very low signal-to-noise ratio (SNR) decoding and low data rate. In [8,9], the authors focused on the uplink signal detection of the communication systems adopting the AmBC, where the detectors exploit maximum a posteriori probability (MAP) and maximum-likelihood (ML) estimators at the receiver side. However, the solutions do not perform well when the difference between the backscatter channel and the direct-link channel is small. Other approaches [10,11] make use of WiFi backscatter to decode the tag bits by detecting the changes in the received signal strength which highly depend on channel and multi-path effects. Recently, a new AmBC over orthogonal frequency division multiplexing (OFDM) signals was proposed in [12], where the system model for such AmBC system from spread-spectrum perspective was established. By inhibiting the effects of cyclic prefix (CP) on the ambient OFDM signals, the authors developed a test statistic that is able to invalidate the inter-symbol interference among them. An extension of [12] to the case of multi-antenna receiver was presented in [13], where the test statistic was built from a linear combination of the per-antenna test statistics. Inspired by [12], our approach is to focus on the uplink signal detection and the performance analysis for a communication system that utilizes AmBC over ambient OFDM signals. Our main ideas and contributions are highlighted as follows. First, we introduce the system model for the AmBC over ambient OFDM carriers in the air and the test statistic for tag signal detection, which are established in [12]. Second, we design an improved energy detector by proposing an arbitrary positive power operation on the signal amplitude instead of the squaring operation given as the previous work. Numerical results demonstrate that the proposed detector with optimum power order can achieve lower bit error rate (BER) and higher data rate than those in [12]. The remainder of this paper is organized as follows. Section2 presents the system model and the problem formulation. Section3 analyzes the optimal energy detector design for the proposed scheme. Section4 gives the numerical results, followed by the conclusion in Section5. 2. System Model and Problem Formulation The aim of this section is first to introduce the existing AmBC system that utilizes OFDM carriers, then to present those factors that affect the detector performance. In order to understand how to make use of both energy harvesting and backscattering, we investigate the AmBC system model where an energy harvesting tag uses the existing radio signals from ambient sources to operate itself (e.g., RF source). It produces a modulated reflection of those signals to a nearby receiver (e.g., reader). Legacy receivers employ OFDM structure, where data is transmitted in parallel on a certain number of sub-carriers of different frequencies. Thus, it leads to low power consumption on data transmissions which is very suitable for low-powered hardware or batteryless IoT devices. Moreover, in terms of efficient spectrum sensing, it has been shown that energy detection approach has low computational complexity and ability to identify the spectrum holes without a priori knowledge of primary characteristic [8,14,15]. Once we develop an appropriate test statistic for energy detector, it can guarantee a desired detection performance. Sensors 2019, 19, 517 3 of 14 2.1. Notations The following notations are used throughout the paper. E( ) and Var( ) denote the expectation and variance operators, respectively. • · · (m, s2) and (m, s2) denote the Gaussian and the circularly-symmetric Gaussian distributions • N CN with mean m and variance s2, respectively. Re is the real part of a complex number. • f·g A random variable X that is gamma-distributed with shape k and rate q is denoted as X G(k, q). • ∼ The corresponding probability density function (PDF) in the shape-rate parametrization is f (x; k, q) = 1 xk 1 exp x/q , where G(k) = R ¥ xk 1exdx, k (0, ¥) is the gamma function qkG(k) − f− g 0 − 2 evaluated at k. 2.2. Overall System Architecture The overall system architecture utilizing the AmBC over OFDM carriers is illustrated in Figure1. In this system, we consider two communication components coexist: the legacy OFDM system and the AmBC system. In the legacy OFDM system, the RF source transmits OFDM signals to its legacy users, while in the AmBC system a backscatter tag transmits its modulated signals to the reader over ambient OFDM carriers from the RF source. Note that this tag is equipped with a switch that can split the received signal into two parts: information decoder (ID) and energy harvester (EH). Assume that they are all connected to a single antenna and use the same RF signals. The RF-powered passive

Load more