418 research outputs found
Channel Estimation for Ambient Backscatter Communication Systems with Massive-Antenna Reader
Ambient backscatter, an emerging green communication technology, has aroused
great interest from both academia and industry. One open problem for ambient
backscatter communication (AmBC) systems is channel estimation for a
massive-antenna reader. In this paper, we focus on channel estimation problem
in AmBC systems with uniform linear array (ULA) at the reader which consists of
large number of antennas. We first design a two-step method to jointly estimate
channel gains and direction of arrivals (DoAs), and then refine the estimates
through angular rotation. Additionally, Cramer-Rao lower bounds (CRLBs) are
derived for both the modulus of the channel gain and the DoA estimates.
Simulations are then provided to validate the analysis, and to show the
efficiency of the proposed approach.Comment: 5 figures, submitted to IEEE Transactions on Vehicular Technology, 29
March, 201
Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader
Backscatter communication (BSC) is being realized as the core technology for
pervasive sustainable Internet-of-Things applications. However, owing to the
resource-limitations of passive tags, the efficient usage of multiple antennas
at the reader is essential for both downlink excitation and uplink detection.
This work targets at maximizing the achievable sum-backscattered-throughput by
jointly optimizing the transceiver (TRX) design at the reader and
backscattering coefficients (BC) at the tags. Since, this joint problem is
nonconvex, we first present individually-optimal designs for the TRX and BC. We
show that with precoder and {combiner} designs at the reader respectively
targeting downlink energy beamforming and uplink Wiener filtering operations,
the BC optimization at tags can be reduced to a binary power control problem.
Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low
and high signal-to-noise-ratio regimes. Based on these developments, an
iterative low-complexity algorithm is proposed to yield an efficient
jointly-suboptimal design. Thereafter, we discuss the practical utility of the
proposed designs to other application settings like wireless powered
communication networks and BSC with imperfect channel state information.
Lastly, selected numerical results, validating the analysis and shedding novel
insights, demonstrate that the proposed designs can yield significant
enhancement in the sum-backscattered throughput over existing benchmarks.Comment: 17 pages, 5 figures, accepted for publication in IEEE Transactions on
Communication
Time-Spread Pilot-Based Channel Estimation for Backscatter Networks
Current backscatter channel estimators employ an inefficient silent pilot
transmission protocol, where tags alternate between silent and active states.
To enhance performance, we propose a novel approach where tags remain active
simultaneously throughout the entire training phase. This enables a one-shot
estimation of both the direct and cascaded channels and accommodates various
backscatter network configurations. We derive the conditions for optimal pilot
sequences and also establish that the minimum variance unbiased (MVU) estimator
attains the Cramer-Rao lower bound. Next, we propose new pilot designs to avoid
pilot contamination. We then present several linear estimation methods,
including least square (LS), scaled LS, and linear minimum mean square error
(MMSE), to evaluate the performance of our proposed scheme. We also derive the
analytical MMSE estimator using our proposed pilot designs. Furthermore, we
adapt our method for cellular-based passive Internet-of-Things (IoT) networks
with multiple tags and cellular users. Extensive numerical results and
simulations are provided to validate the effectiveness of our approach.
Notably, at least 10 dBm and 12 dBm power savings compared to the prior art are
achieved when estimating the direct and cascaded channels. These findings
underscore the practical benefits and superiority of our proposed technique
Recommended from our members
Signal Processing for Wireless Power and Information Transfer
The rapid development of the Internet of Things (IoT) and wireless sensor network (WSN) technologies enable easy access and control of a variety forms of information and data from numerous number of smart devices, and give rise to many novel applications and research areas such as smart home, machine type communications, etc. However due to the small sizes, sophisticated environment, and large number of devices in network, it is hard to directly power the devices from grid. Hence the power connectivity remains one of the major issues that needs to be addressed for related IoT applications. Wireless power transfer (WPT) and backscatter communications are provisioned to be prominent solutions to overcome the power connectivity challenge, but they suer strong efficiency limitation which becomes the barrier to universally popularize such technologies. On the other hand, network optimization is also a research focus of such applications which significantly affects the performance of the system due to the high volume of connected devices and different features. In this thesis we propose advanced techniques to overcome the challenges on the low efficiency and network design of the wireless information and power transfer systems. The thesis consists of two parts. In the first part we focus on the power transmitter design which addresses the low efficiency issue associated with backscatter communication and WPT. In Chapter 2, we consider a backscatter RFID system with the multi-antenna reader and propose a blind transmit and receive adaptive beamforming algorithm. The interrogation range and data transmission performance are both investigated under such configuration. In Chapter 3 we study wireless power transfer by the beamspace large-scale MIMO system with lens antenna arrays. We first present the WPT model for the beamspace MIMO which is derived from the spatial MIMO model. By constraining on the number of RF chains in the transmitter, we formulate two WPT optimization problems: the sum power transfer problem and the max-min power transfer problem. For both problems we consider two different transmission schemes, the multi-stream and uni-stream transmissions, and we propose different algorithms to solve both problems in both schemes respectively. In the second part we study the network optimization problems in the WPT and backscatter systems. In Chapter 4, we study the resource allocation problem for a RF-powered network, where the objective is to maximize the total data throughput of all sensors. We break the problem into two subproblems: the sensor battery energy utilization problem and the charging power allocation problem of the central node, which is an RF power transmitter that transmits RF power to the sensors. We analyze and show several key properties of both problems, and then propose computationally efficient algorithms to solve both problems optimally. In Chapter 5, we study the time scheduling problem in RF-powered backscatter communication networks, where all transmitters can operates in either backscattering mode or harvest-then-transmit (HTT) mode. The objective is to decide the operating mode of each transmitter and minimize the total transmission time of the network. We also consider both ideal and realistic transmitters based on different internal power consumption models for HTT transmitters. Under both transmitter models we show several key properties, and propose bisection based algorithms which has low computational complexity that solves the problem optimally. The results are then extended to the massive MIMO regime
Optimal Channel Estimation for Reciprocity-Based Backscattering with a Full-Duplex MIMO Reader
Backscatter communication (BSC) technology can enable ubiquitous deployment
of low-cost sustainable wireless devices. In this work we investigate the
efficacy of a full-duplex multiple-input-multiple-output (MIMO) reader for
enhancing the limited communication range of monostatic BSC systems. As this
performance is strongly influenced by the channel estimation (CE) quality, we
first derive a novel least-squares estimator for the forward and backward links
between the reader and the tag, assuming that reciprocity holds and K
orthogonal pilots are transmitted from the first K antennas of an N antenna
reader. We also obtain the corresponding linear minimum-mean square-error
estimate for the backscattered channel. After defining the transceiver design
at the reader using these estimates, we jointly optimize the number of
orthogonal pilots and energy allocation for the CE and information decoding
phases to maximize the average backscattered signal-to-noise ratio (SNR) for
efficiently decoding the tag's messages. The unimodality of this SNR in
optimization variables along with a tight analytical approximation for the
jointly global optimal design is also discoursed. Lastly, the selected
numerical results validate the proposed analysis, present key insights into the
optimal resource utilization at reader, and quantify the achievable gains over
the benchmark schemes.Comment: accepted for publication in IEEE Transactions on Signal Processing,
16 pages, 15 figures, 1 tabl
- …