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Ambient backscatter communication is a newly emerged paradigm, which utilizes the ambient radio frequency (RF) signal as the carrier to reduce the system battery requirement, and is regarded as a promising solution for enabling large scale deployment of future Internet of Things (IoT) networks.The key issue of ambient backscatter communication systems is how to perform reliable detection. In this paper, we propose novel encoding methods at the information tag, and devise the corresponding symbol detection methods at the reader. In particular, Manchester coding and differential Manchester coding are adopted at the information tag, and the corresponding semicoherent Manchester (SeCoMC) and non-coherent Manchester (NoCoMC) detectors are developed. In addition, analytical bit error rate (BER) expressions are characterized for both detectors assuming either complex Gaussian or unknown deterministic ambient signal. Simulation results show that the BER performance of unknown deterministic ambient signal is better, and the SeCoMC detector outperforms the NoCoMC detector. Finally, compared with the prior detectors for ambient backscatter communications, the proposed detectors have the advantages of achieving superior BER performance with lower communication delay.

In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent reflecting surface (IRS) equipped with N passive reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI requires substantial training overhead that increases with N . In contrast, we propose in this letter a novel random passive beamforming scheme, where the IRS performs independent random reflection for Q ≥ 1 times in each channel coherence interval without the need of CSI acquisition. For the proposed scheme, we first derive a closed-form approximation of the outage probability, based on which the optimal Q with best outage performance can be efficiently obtained. Then, for the purpose of comparison, we derive a lower bound of the outage probability with traditional CSI-based passive beamforming. Numerical results show that a small Q is preferred in the highoutage regime (or with high rate target) and the optimal Q becomes larger as the outage probability decreases (or as the rate target decreases). Moreover, the proposed scheme significantly outperforms the CSI-based passive beamforming scheme with training overhead taken into consideration when N and/or the number of users are large, thus offering a promising CSI-free alternative to existing CSI-based schemes.Index terms-Intelligent reflecting surface, multicast, random passive beamforming, outage probability I. INTRODUCTION Intelligent reflecting surface (IRS) is a new emerging paradigm for the fifth-generation (5G) and beyond wireless communication networks [1]-[3]. Specifically, an IRS is able to alter the wireless channel by shifting the phases of the impinging signals via a large number of passive reflecting elements, for enhancing desired signal power or suppressing undesired interference. Being also cost-effective and energyefficient, IRS has received fast-growing research attention recently. To maximize the performance gains brought by IRS, it is of paramount importance to properly design the IRS reflection coefficients, which is also termed as passive beamforming. In the literature, passive beamforming design has been studied under various system setups with different performance metrics [4]- [12]. Particularly, the existing works on IRS have mostly considered a "fully intelligent" reflecting surface with the availability of full channel state information (CSI) for all the IRS-associated links (see, e.g., [4]-[11]). In practice, such CSI needs to be acquired in each channel coherence interval by the transmitter/receiver at the cost of channel training overhead and/or feedback complexity that increases with the number of IRS reflecting elements, N , Q. Tao and C. Zhong are with the

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