The surge in low-altitude economic activities has spurred a significant interest in sensing Unmanned Aerial Vehicles (UAVs). With the widespread deployment of 5G infrastructure and the increasing prominence of integrated sensing and communication, monitoring UAVs via 5G base stations is a natural consideration. However, the rapid Doppler shifts of UAVs and sparse 5G reference signals violate Nyquist sampling requirements. To bridge this gap, we propose LaSen, which merges reference and downlink data signals for sensing. A key challenge stems from the fact that the combination of the periodic reference signals and stochastic data signals constitutes a non-uniform, time-varying measurement matrix. LaSen formulates the tracking of UAVs as a sparse recovery problem, where the target’s kinematics are reconstructed from non-uniform, sub-Nyquist observations. LaSen overcomes the challenges of volatile 5G signal patterns in an iterative way, starting from good measurements as an anchor and progressively refining the suboptimal measurements. Real-world experiments show that LaSen significantly extends the velocity sensing capability, where the measurable speed is up to 20.2 m/s. LaSen can detect drones at a distance of 108 m and can continuously track the distance and velocity of multiple targets, even when the downlink channel is sparsely and dynamically occupied. This work demonstrates the feasibility of high-speed target sensing in next-generation dual-function 5G/6G infrastructures.

Terahertz (THz) communication is emerging as a pivotal enabler for 6G and beyond wireless systems owing to its multi-GHz bandwidth. One of its novel applications is in wireless data centers, where it enables ultra-high data rates while enhancing network reconfigurability and scalability. However, due to numerous racks, supporting walls, and densely deployed antennas, the line-of-sight (LoS) path in data centers is often instead of fully obstructed, resulting in quasi-LoS propagation and degradation of spectral efficiency. To address this issue, Airy beam-based hybrid beamforming is investigated in this paper as a promising technique to mitigate quasi-LoS propagation and enhance spectral efficiency in THz wireless data centers. Specifically, a cascaded geometrical and wave channel model (CGWCM) is proposed for quasi-LoS scenarios, which accounts for diffraction effects while being more simplified than conventional wave-based model. Then, the characteristics and generation of the Airy beam are analyzed, and beam search methods for quasi-LoS scenarios are proposed, including hierarchical focusing-Airy beam search, and low-complexity beam search. Simulation results validate the effectiveness of the CGWCM and demonstrate the superiority of the Airy beam over Gaussian beams in mitigating blockages, verifying its potential for practical THz wireless communication in data centers.

The Terahertz (0.1-10 THz) band wireless data center networks (DCNs) are promising to provide high data rates and low latency for next-generation cloud applications. However, one research gap that is still existed is the lack of measurement data and thorough characterization of the THz wave propagation in data centers. To address this problem, in this paper, two sets of measurement campaigns are conducted in a data center scenario at 130–140 GHz band, by using a vector network analyzer (VNA)-based channel sounder system with different receiver heights. The measured data is further processed to extract the multi path components (MPCs) and classify the MPCs into clusters. Furthermore, the channel characteristics, including the path loss, shadow fading, K-factor, delay and angular spreads are calculated and analyzed. Clustering results and MPCs propagation are analyzed and examined in light of the real geometry in the data center. Interestingly, comparison with measured results in meeting room scenarios at 140 GHz shows that the reflections and scattering from metal racks in the data center are more significant, resulting in lower path loss, smaller K-factor, and larger delay spreads. The measured results in this work substantiate guidelines for system design of THz wireless DCNs.

Multiple-input–multiple-output (MIMO) is a promising enabler for massive connectivity of Internet of Things (IoT) devices by offering abundance of spatial degrees of freedom. One of the challenging issues is the excessive overhead induced by users’ channel state information (CSI) feedback, which impedes the gains of MIMO techniques. The popular solution to reduce the overhead is to adopt implicit feedback. However, existing implicit feedback methods mainly rely on either expensive hardware circuits or large antenna separation, making them unaffordable to low-cost and small-factor IoT devices. To circumvent this issue, in this article, a new channel feedback mechanism calledLazyBackis proposed, which jointly calibrates multiple users’ channels by removing the hardware diversity of different users. Furthermore, to ensure up-to-date CSI, LazyBack adopts a channel prediction algorithm to infer channel stability. When the channel varies quickly over time, LazyBack switches back to the explicit feedback mode to obtain the real-time downlink channel. A LazyBack prototype is implemented based on USRPs, and the experimental results show that it provides a$1.7 \times $and$3.1 \times $throughput improvement compared with the IEEE 802.11ac for$4 \times 4$and$8 \times 8$multiuser MIMO (MU-MIMO), respectively.

A long-standing vision of backscatter networks is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatter networks toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further towards breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter networks. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. Furthermore, we devise a new metric to calculate log-likelihood ratio for accurate decoding, and present a stopping criterion of iterations to reduce decoding latency. We build a prototype PCB tag, and our experiments show that it achieves up to 11.5× throughput improvement over the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

In this article, we propose a novel waveform with low cyclic prefix (CP) overhead for narrow-band Internet-of-things (NB-IoT) systems. Compared with the classical orthogonal frequency-division multiplexing (OFDM), the proposed waveform employs the concept of symbol repetition on multiple successive OFDM symbols (SR-OFDMs) and only one CP is occupied by multiple SR-OFDM symbols; hence, the CP overhead is reduced significantly, improving the spectral and energy efficiencies in NB-IoT systems. It is proved that the proposed SR-OFDM can effectively fight against the multipath fading channels by a simple single-tap equalization, avoiding the mutual interference among symbols. In addition, it is shown that the proposed SR-OFDM and classical OFDM have the same size of the inverse discrete Fourier transform (IDFT) at the transmitter, which indicates the same subcarrier spacing. Especially, it is demonstrated that the signals of multiple symbols are separated at the receiver, and the demodulation of each symbol can be performed independently, which is helpful to reduce the complexity of symbol demodulation. To evaluate the proposed SR-OFDM, simulations have been done by considering the Stanford university interim (SUI) channels, which have large channel delay spread and exhibit high frequency selectivity.

A long-standing vision of backscatter communications is to provide long-range connectivity and high-speed transmissions for batteryless Internet-of-Things (IoT). Recent years have seen major innovations in designing backscatters toward this goal. Yet, they either operate at a very short range, or experience extremely low throughput. This paper takes one step further toward breaking this stalemate, by presenting PolarScatter that exploits channel polarization in long-range backscatter links. We transform backscatter channels into nearly noiseless virtual channels through channel polarization, and convey bits with extremely low error probability. Specifically, we propose a new polar code scheme that automatically adapts itself to different channel quality, and design a low-cost encoder to accommodate polar codes on resource-constrained backscatter tags. We build a prototype PCB tag and test it in various outdoor and indoor environments. Our experiments show that our prototype achieves up to 10× throughput gain, or extends the range limit by 1.8× compared with the state-of-the-art long-range backscatter solution. We also simulate an IC design in TSMC 65 nm LP CMOS process. Compared with traditional encoders, our encoder reduces storage overhead by three orders of magnitude, and lowers the power consumption to tens of microwatts.

The design paradigm for backscatter tags is to avoid complex functionality and make tags as simple as possible. However, such a design principle leads to the prevalence of signal collision as tags cannot sense other tags' ongoing transmissions. The high probability of tag collision will result in low overall throughput. Although there are some existing efforts to resolve tag collisions, they either require good channel conditions or can only resolve a limited number of tags as channel capacity is deficient when SNR is low. In this article, to overcome this limitation, we bring in antenna arrays to boost the channel capacity. We propose FreeScatter, which can support scalable concurrent backscatter transmission using an antenna array. FreeScatter extracts the path that signals traveled and formulates the tags' channel coefficient using the path representations. FreeScatter further exploits the frequency agnostic property so that it can support more spatial streams than the number of antennas. The experimental results show that we can enable up to 20 tags transmitting concurrently. With the ubiquitous connectivity of battery-free tags in the near future, FreeScatter can significantly boost the network throughput.

Abstract In this paper, we propose an interference mitigation method to suppress the downlink interference in multi‐macrocell/femtocell networks, and analytically evaluate the interference mitigation and average rate performances. Specifically, the proposed interference mitigation method consists of three steps: frequency partitioning, cell partitioning, and sub‐band allocation. In the frequency partitioning step, the whole downlink frequency band is divided into nine non‐overlapping sub‐bands. In the cell partitioning step, each macrocell is divided into four macrocell regions and three femtocell regions for macrocells' and femtocells' communications, respectively. In the sub‐band allocation step, each macrocell or femtocell region is allocated a sub‐band to guarantee that any two neighboring macrocell/femtocell regions use different sub‐bands. Conducted simulation results show that the proposed method is effective in mitigating the downlink interference and improving the average downlink per‐channel rate in multi‐macrocell/femtocell networks. In summary, the major contribution of the proposed interference mitigation method is that the downlink interference can be mitigated without cooperation between macrocells and femtocells, while the full frequency utilization of the macrocell is achieved. Copyright © 2016 John Wiley & Sons, Ltd.