Future Internet-of-Things (IoT) is expected to wirelessly connect billions of low-complexity devices. For wireless information transfer (WIT) in IoT, high density of IoT devices and their ad hoc communication result in strong interference which acts as a bottleneck on WIT. Furthermore, battery replacement for the massive number of IoT devices is difficult if not infeasible, making wireless energy transfer (WET) desirable. This motivates: (i) the design of full-duplex WIT to reduce latency and enable efficient spectrum utilization, and (ii) the implementation of passive IoT devices using backscatter antennas that enable WET from one device (reader) to another (tag). However, the resultant increase in the density of simultaneous links exacerbates the interference issue. This issue is addressed in this paper by proposing the design of full-duplex backscatter communication (BackCom) networks, where a novel multiple-access scheme based on time-hopping spread-spectrum (TH-SS) is designed to enable both one-way WET and two-way WIT in coexisting backscatter reader-tag links. Comprehensive performance analysis of BackCom networks is presented in this paper, including forward backward bit-error rates and WET efficiency and outage probabilities, which accounts for energy harvesting at tags, non-coherent and coherent detection at tags and readers, respectively, and the effects of asynchronous transmissions.
For this reason, active research has been conducted on designing techniques for various types of BackCom systems and networks which are more complex than the traditional RFID systems @cite_5 @cite_11 @cite_7 @cite_8 @cite_16 . One focus of the research is to design multiple-access BackCom networks where a single reader serves multiple tags. As proposed in @cite_5 , collision can be avoided by directional beamforming at the reader and decoupling tags covered by the same beam using the frequency-shift keying modulation. Subsequently, alternative multiple-access schemes were proposed in @cite_11 and @cite_7 based on time-division multiple access and collision-detection-carrier-sensing based random access, respectively. A novel approach for collision avoidance was presented in @cite_8 which treats backscatter transmissions by tags as a sparse code and decodes multi-tag data using a compressive-sensing algorithm.
@cite_7 @cite_8 @cite_5 @cite_16 @cite_11
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Recently, ambient backscatter that utilizes surrounding radio frequency (RF) signals for both power and communications, has attracted vast interest since it can free sensors and tags from batteries and has extensive applications in Internet of Things (IoT), Existing studies about ambient backscatter often assume single antenna for each tag. Actually, as we show in this paper, equipping tags with multiple antennas can enlarge communication distance, enhance detection performance, and thus be practically useful. One key challenge of using multiple-antenna tags is the signal detection at the reader because the tag may have limited power and can transmit few training symbols. Therefore, in this paper, we design a blind detector based on F-test for the reader to recover tag signals without any knowledge of RF signals power, noise variance and all channel state information (CSI). Furthermore, we derive the lower and upper bounds of detection probabilities, and its exact expression in a special case. The optimal antenna selection scheme is also proposed to maximize the detection probability. Finally, simulation results are provided to corroborate our theoretical studies. Reliable and energy-efficient reading of Radio Frequency IDentification (RFID) tags is of utmost importance, especially in mobile and dense tag settings. We identify tag collisions as a main source of inefficiency in terms of wasting both medium access control (MAC) frame slots and reader’s energy. We propose modulation silencing (MS), a reader-tag interaction framework to limit the effect of tag collisions. Utilizing relatively simple circuitry at the tag, MS enhances the performance of existing anti-collision protocols by allowing readers to terminate collision slots once a decoding violation is detected. With shorter collision slots, we revisit the performance metrics and introduce a new generalized time efficiency metric and an optimal frame selection formula that takes into consideration the MS effects. Through analytical solutions and extensive simulations, we show that the use of MS results in significant performance gains under various scenarios. When the joint processing technique is applied in the coordinated multipoint (CoMP) downlink transmission, the user data for each mobile station needs to be shared among multiple base stations (BSs) via backhaul. If the number of users is large, this data exchange can lead to a huge backhaul signaling overhead. In this paper, we consider a multi-cell CoMP network with multi-antenna BSs and single antenna users. The problem that involves the joint design of transmit beamformers and user data allocation at BSs to minimize the backhaul user data transfer is addressed, which is subject to given quality-of-service and per-BS power constraints. We show that this problem can be cast into an l0-norm minimization problem, which is NP-hard. Inspired by recent results in compressive sensing, we propose two algorithms to tackle it. The first algorithm is based on reweighted l1-norm minimization, which solves a series of convex l0-norm minimization problems. In the second algorithm, we first solve the l2-norm relaxation of the joint clustering and beamforming problem and then iteratively remove the links that correspond to the smallest transmit power. The second algorithm enjoys a faster solution speed and can also be implemented in a semi-distributed manner under certain assumptions. Simulations show that both algorithms can significantly reduce the user data transfer in the backhaul. We propose and investigate a compressive architecture for estimation and tracking of sparse spatial channels in millimeter (mm) wave picocellular networks. The base stations are equipped with antenna arrays with a large number of elements (which can fit within compact form factors because of the small carrier wavelength) and employ radio frequency (RF) beamforming, so that standard least squares adaptation techniques (which require access to individual antenna elements) are not applicable. We focus on the downlink, and show that “compressive beacons,” transmitted using pseudorandom phase settings at the base station array, and compressively processed using pseudorandom phase settings at the mobile array, provide information sufficient for accurate estimation of the two-dimensional (2D) spatial frequencies associated with the directions of departure of the dominant rays from the base station, and the associated complex gains. This compressive approach is compatible with coarse phase-only control, and is based on a near-optimal sequential algorithm for frequency estimation which approaches the Cramer Rao Lower Bound. The algorithm exploits the geometric continuity of the channel across successive beaconing intervals to reduce the overhead to less than 1 even for very large ( @math ) arrays. Compressive beaconing is essentially omnidirectional, and hence does not enjoy the SNR and spatial reuse benefits of beamforming obtained during data transmission. We therefore discuss system level design considerations for ensuring that the beacon SNR is sufficient for accurate channel estimation, and that inter-cell beacon interference is controlled by an appropriate reuse scheme.