UHF RFID tags have been widely used for contactless inventory and tracking applications. One fundamental problem with RFID readers is their limited tag reading rate. Existing RFID readers (e.g., Impinj Speedway) can read about 35 tags per second in a read zone, which is far from enough for many applications. In this paper, we present the first-of-its-kind RFID reader (mReader), which borrows the idea of multi-user MIMO (MU-MIMO) from cellular networks to enable concurrent multi-tag reading in passive RFID systems. mReader is equipped with multiple antennas for implicit beamforming in downlink transmissions. It is enabled by three key techniques: uplink collision recovery, transition-based channel estimation, and zero-overhead channel calibration. In addition, mReader employs a Q-value adaptation algorithm for medium access control to maximize its tag reading rate. We have built a prototype of mReader on USRP X310 and demonstrated for the first time that a two-antenna reader can read two commercial off-the-shelf (COTS) tags simultaneously. Numerical results further show that mReader can improve the tag reading rate by 45% compared to existing RFID readers.

Federated Learning (FL) is a promising technique to enhance the safety and efficiency of intelligent transportation systems. While FL has been extensively studied, the communication and networking challenges related to the operations of FL in dynamic yet dense vehicular networks remain under-explored. Limited storage and communication capacities of individual vehicles throttle the timely training of an FL model in distributed vehicular networks. In this paper, we present a communication framework for FL (CF4FL) in transportation systems. CF4FL aims to accelerate the convergence of FL training process through the innovation of two complementary networking components: (i) a deadline-driven vehicle scheduler (DDVS), and (ii) a concurrent vehicle polling scheme (CVPS). DDVS identifies a subset of vehicles for local model training in each iteration of FL, with the aim of minimizing data loss while respecting the deadline constraints derived from vehicles’ storage, computation, and energy budgets. CVPS takes advantage of multiple antennas on an edge server to enable concurrent local model transmissions in dynamic vehicular networks, thereby reducing the airtime overhead of each FL iteration. We have evaluated CF4FL through a blend of experimentation and simulation. Trace-driven simulation shows that, compared to existing scheduling and transmission schemes, CF4FL reduces the convergence time of FL training by 39%.

LoRa has emerged as a key wireless communication technology for a gateway to provide geographically-distributed IoT devices with low-rate, long-range connections. In this paper, we present MaLoRaGW, the first-of-its-kind Multi-antenna LoRa GateWay that enables multi-user MIMO (MU-MIMO) LoRa communications in both uplink and downlink. MaLoRaGW was inspired by the success of MU-MIMO in cellular and Wi-Fi networks. The key component of MaLoRaGW is a joint baseband PHY design for uplink packet detection and downlink beamforming. Its innovation lies in three modules: spatial signal projection, accurate channel estimation, and implicit beamforming, all of which reside only in a LoRa gateway and require no modification on LoRa client devices. We have built a prototype of two-antenna MaLoRaGW on a USRP device and extensively evaluated its performance with commercial LoRa dongles in three scenarios: lab, office building, and university campus. Our experimental results show that, compared to the state-of-the-art, the two-antenna MaLoRaGW increases uplink throughput by 10% and downlink throughput by 95%.

Wireless Internet of Things (IoT) applications have penetrated every aspect of our society and become increasingly important in smart homes, smart cities, and smart hospitals. However, many WiFi-based IoT devices (e.g., light switches, door/window open alert sensors, and Google Home) do not have input interfaces such as keypad or touchscreen due to their limits in physical size, power consumption, and/or manufacturing cost, making it inconvenient and onerous for end users to authenticate those IoT devices for wireless Internet access. In this article, we present AuthIoT, a learning-based authentication scheme for wireless IoT devices without input interfaces. The key component of AuthIoT is a channel state information (CSI)-based character classification algorithm for a WiFi access point (AP), which recognizes the passcode from an IoT device when an end user holds it in hand and writes the passcode over the air. AuthIoT has two salient features: 1) it is transferable for cross-environment applications and 2) it works in more realistic scenarios where AP is equipped with nonlinear antenna array. We have built a prototype of AuthIoT and evaluated its performance on two testbeds: 1) Intel 5300 WiFi card with three linear antennas and 2) USRP N310 with four nonlinear (square-shaped) antennas. The experimental results show that AuthIoT achieves 84% and 83% recognition accuracy on the two testbeds.

Wireless local area networks (WLANs) are a key component of the telecommunications infrastructure in our society. While many solutions have been produced to improve their downlink throughput, the techniques for enhancing their uplink throughput remain limited. The stagnation can be attributed to the lack of fine-grained inter-node synchronization due to the hardware limitation of most devices. In this paper, we present an uplink distributed multiple-input-and-multiple-output scheme (termed UD-MIMO) for WLANs to enable concurrent uplink transmission in the absence of fine-grained inter-node synchronization. The enabling technique behind UD-MIMO is a practical solution to decoding uplink packets from asynchronous users. UD-MIMO makes it possible for WLANs to significantly improve their uplink throughput while not requiring tight internode synchronization. We have built a prototype of UD-MIMO on a wireless testbed and demonstrate its compatibility with commercial off-the-shelf Atheros 802.11 client devices (with modified Linux driver). Our experimental results show that, for a WLAN with 8 APs in a conference room, UD-MIMO offers 3.4× throughput compared to interference-avoidance approach.

Current data-driven intelligent transportation systems are mainly reliant on IEEE 802.11p to collect and exchange information. Despite promising performance of IEEE 802.11p in providing low-latency communications, it is still vulnerable to jamming attacks due to the lack of a PHY-layer countermeasure technique in practice. In this paper, we propose JammingBird, a novel receiver design that tolerates strong constant jamming attacks. The enablers of JammingBird are two MIMO-based techniques: Jamming-resistant synchronizer and jamming suppressor. Collectively, these two new modules are able to detect, synchronize, and recover desired signals under jamming attacks, regardless of the PHY-layer technology employed by the jammers. We have implemented JammingBird on a vehicular testbed and conducted extensive experiments to evaluate its performance in three common vehicular scenarios: Parking lots (0~15 mph), local traffic areas (25~45 mph), and highways (60~70 mph). In our experiments, while the jamming attacks degrade the throughput of conventional 802.11p-based receivers by 86.7%, JammingBird maintains 83.0% of the throughput on average. Experimental results also show that JammingBird tolerates the jamming signals with 25 dB stronger power than the desired signals.

ZigBee is a wireless communication technology that has been widely used to provide low-bandwidth wireless services for Internet-of-Things applications, such as building automation, medical data collection, and industrial equipment control. As ZigBee operates in the industrial, scientific and medical radio frequency bands, it may suffer from unintentional interference from coexisting radio devices (e.g., WiFi and Bluetooth) and/or radio jamming attacks from malicious devices. Although many results have been produced to enhance ZigBee security, there is no technique that can secure ZigBee against jamming attack. In this article, we propose a new ZigBee receiver by leveraging MIMO technology, which is capable of decoding its desired signal in the presence of constant jamming attack. The enabler is a learning-based jamming mitigation method, which can mitigate the unknown interference using an optimized neural network. We have built a prototype of our proposed ZigBee receiver on a wireless testbed. Experimental results show that it is capable of decoding its packets in the face of 20-dB stronger jamming. The proposed ZigBee receiver offers an average of 26.7-dB jamming mitigation capability compared to off-the-shelf ZigBee receivers.

In cellular networks, multiuser multiple-input multiple-output (MU-MIMO) is a key technology and has already been deployed in many real systems. Recently, device-to-device (D2D) communication has emerged as another promising technology as it offers several advantages, such as traffic offloading, low-latency transmissions, and enhanced spectral efficiency. Although there are many results of these two technologies, most of them are limited to their respective domains and there is a lack of practical design to combine both technologies for cellular networks. In this article, we present DM-COM, a practical scheme for enabling the coexistence of D2D and MU-MIMO subsystems in cellular networks. The enabler of DM-COM is a new approach for managing the mutual interference between the two subsystems, which does not require channel state information and is, therefore, amenable to practical implementation. We have built a prototype of DM-COM on a wireless testbed and evaluated its performance in a real-world wireless environment. Our experimental results show that, using DM-COM in a small cellular network, D2D users achieve 1.9 bit/s/Hz spectral efficiency, while MU-MIMO users have less than 8% throughput degradation compared to the case without D2D users.

Timely vehicle-to-vehicle (V2V) communication is a key component of intelligent transportation systems to improve driving safety and efficiency. Although many results have been produced for vehicular networks, most of them focused on improving vehicular communication capacity and reliability. Very limited progress has been made so far in the design of practical V2V communication schemes for large-scale vehicular networks. In this paper, we present VehCom, a fully distributed message broadcast scheme for V2V communication networks. VehCom offers a delay guarantee for each vehicle's message broadcast while minimizing the packet loss rate. The enabler of VehCom is an asynchronous packet reception technique, which leverages a vehicle's multiple antennas to decode asynchronous collided packets from its neighboring vehicles. We have implemented the asynchronous packet reception technique on a vehicular wireless testbed, and examined the performance of VehCom in a large-scale vehicular network where i) each vehicle is equipped with four antennas, ii) each vehicle has 240 vehicles in its communication range, and iii) each vehicle broadcasts a 624-bit packet over 10 MHz spectrum in every 100 ms (guaranteed delay). Our experimental and analytical results show that the packet loss rate is less than 3.9% on parking lots, less than 4.1% on local roads, and less than 6.2% on highways.

Multi-user MIMO (MU-MIMO) is a key technology for current and next-generation wireless local area networks (WLANs). While it has widely been deployed in WLANs, its potential is not fully exploited in real-world systems. This can be attributed to the large airtime overhead induced by channel acquisition in existing MU-MIMO protocols, which significantly compromises the throughput gain of MU-MIMO. In this paper, we present LB-SciFi, a learning-based channel feedback framework for MU-MIMO in WLANs. LB-SciFi takes advantage of recent advances in deep neural network autoencoder (DNN-AE) to compress channel state information (CSI) in 802.11 protocols, thereby conserving airtime and improving spectral efficiency. The key component of LB-SciFi is an online DNN-AE training scheme, which allows an AP to train DNN-AEs by leveraging the side information of existing 802.11 protocols. With this training scheme, DNN-AEs are capable of significantly lowering the airtime overhead for MU-MIMO while preserving its backward compatibility with incumbent Wi-Fi client devices. We have implemented LB-SciFi on a wireless testbed and evaluated its performance in indoor wireless environments. Experimental results show that LB-SciFi offers an average of 73% airtime overhead reduction and increases network throughput by 69% on average when compared to 802.11 feedback protocols.

Co-channel interference is a fundamental issue in wireless local area networks (WLANs). Although many results have been developed to handle co-channel interference for concurrent transmission, most of them require network-wide fine-grained synchronization and data sharing among access points (APs). Such luxuries, however, are not affordable in many WLANs due to their hardware limitation and data privacy concern. In this paper, we present TCCI, a co-channel interference management scheme to enable concurrent transmission in WLANs. TCCI requires neither network-wide fine-grained synchronization nor inter-network data sharing, and therefore is amenable to real-world implementation. The enabler of TCCI is a new detection and beamforming method for an AP, which is capable of taming unknown interference by leveraging its multiple antennas. We have built a prototype of TCCI on a wireless testbed and demonstrated its compatibility with commercial Atheros 802.11 devices. Our experimental results show that TCCI allows co-located APs to serve their users simultaneously and achieves up to 113% throughput gain compared to existing interference-avoidance protocol.

As most Internet-of-Things (IoT) devices are powered by small-sized batteries and expected to operate for many years without battery replacement, energy-efficient wireless IoT communication has been considered as a crucial component of the future network infrastructure. In this article, we propose a practical design (termed WiFi-IoT) to add energy-efficient IoT communication capability into WLANs. WiFi-IoT features two innovative techniques: 1) an asymmetric physical (PHY) design and 2) a transparent coexistence scheme. The asymmetric PHY allows an access point (AP) to communicate with multiple IoT devices at a much low sampling rate (250 ksps), thereby significantly reducing the power consumption for the IoT devices. The transparent coexistence scheme enables a multiantenna AP to serve Wi-Fi and IoT devices simultaneously, leading to an efficient utilization of spectrum. We have built a prototype of WiFi-IoT on a USRP2 wireless testbed and evaluated its performance in real-world wireless environments. The experimental results show that a two-antenna AP can simultaneously serve one broadband Wi-Fi device and 24 narrowband IoT devices on the same spectrum.

Non-orthogonal multiple access (NOMA) has emerged as a new multiple access paradigm for wireless networks. Although many results have been produced for NOMA, most of them are limited to theoretical exploration and performance analysis in cellular networks. Very limited progress has been made so far in the design of practical NOMA schemes for wireless local area networks (WLANs). In this paper, we propose a practical downlink NOMA scheme for WLANs and evaluate its performance in real-world wireless environments. Our NOMA scheme has three key components: precoder design, user grouping, and successive interference cancellation (SIC). On the transmitter side, we first formulate the precoding design problem as an optimization problem and then devise an efficient algorithm to construct precoders for downlink NOMA transmissions. We further propose a lightweight user grouping algorithm to ensure the success of SIC at the receivers. On the receiver side, we propose a new SIC method to decode the desired signal in the presence of strong interference. In contrast to existing SIC methods, our SIC method does not require channel estimation to decode the signals, thereby improving its resilience to interference. We have built a prototype of the proposed NOMA scheme on a wireless testbed. Experimental results show that, compared to orthogonal multiple access (OMA), the proposed NOMA scheme can significantly improve the weak user's date rate (93% on average) and considerably improve WLAN's weighted sum rate (36% on average).

Spectrum shortage is a fundamental problem in wireless networks, and this problem becomes increasingly acute with the rapid proliferation of wireless devices. To address this issue, spectrum sharing in the context of cognitive radio networks (CRNs) has been regarded as a promising solution. Although there is a large body of work on spectrum sharing in the literature, most existing work is limited to theoretical exploration and the progress in practical solution design remains scarce. In this paper, we propose a practical scheme to enable transparent spectrum sharing for a small CRN by leveraging recent advances in multiple-input multiple-output (MIMO) technology. The key components of our scheme are two MIMO-based interference management techniques: blind beamforming (BBF) and blind interference cancellation (BIC). These two techniques enable secondary users to mitigate cross-network interference in the absence of inter-network coordination, fine-grained synchronization, and mutual knowledge. We have built a prototype of our scheme on a wireless testbed and demonstrated its compatibility with commercial Wi-Fi devices (primary users). Experimental results show that, for a secondary device with two/three antennas, BBF and BIC achieve an average of 25 dB and 33 dB interference cancellation capabilities in real-world wireless environments, respectively.

While Narrow-Band Internet of Things (NB-IoT) has been standardized by 3GPP to provide wireless Internet access for IoT devices, this service is expected to come with a monthly fee (e.g., $1 or $2 per month per device). As the number of IoT devices tends to be large, the service charge will impose a considerable financial burden on the end users. In this paper, we propose an Energy-Efficient IoT (EE-IoT) communication scheme by taking advantage of the existing WiFi infrastructure that is widely available in home, office, campus, and city environments. EE-IoT will not only avoid monthly service charge for the end users but also maintain a low power consumption for IoT devices. The key component of EE-IoT is an asymmetric physical (PHY) design, which enables an OFDM-based broadband AP to communicate with multiple QAM-based narrowband IoT devices at a low sampling rate (250 ksps) in both uplink and downlink. The trick in our design is that, instead of using the same carrier frequency as the AP, each IoT device tunes its carrier frequency to a particular subcarrier of the AP's OFDM signal, making it possible to encode/decode the data on that subcarrier at a low sampling rate. Based on this new PHY, we propose a MAC protocol to enable EE-IoT in WLANs. We have built a prototype of EE-IoT on a USRP2 wireless testbed and evaluated its performance in an office building environment. Experimental results show that an AP can serve 24 IoT devices simultaneously and each IoT device can achieve more than 187 kbps in the downlink and more than 125 kbps in the uplink.

As the proliferation of mobile devices has led to an ever-growing demand for wireless Internet services, the spectrum shortage issue becomes increasingly severe and spectrum sharing is regarded as a promising approach to addressing the spectrum shortage issue. In this paper, we propose a practical underlay spectrum sharing scheme for cognitive radio networks (CRNs) where the primary users are oblivious to the secondary users. The key components of our scheme are two MIMO-based interference cancellation (IC) techniques to handle cross-network interference on the secondary network side. The first one is a blind beamforming technique for secondary transmitters. This IC technique allows a secondary transmitter to nullify its generated interference for primary users without requiring channel state information (CSI). The second one is a blind interference cancellation (BIC) technique for secondary receivers. This IC technique enables a secondary receiver to decode its desired signal in the presence of strong unknown interference from primary transmitters. Based on these two MIMO-based IC techniques, we develop a MAC protocol for the secondary network to enable underlay spectrum sharing in CRNs. We have implemented the proposed underlay spectrum sharing scheme on a GNURadio-USRP2 wireless testbed. Experimental results show that the secondary users can achieve an average of 1 bit/s/Hz spectrum efficiency without degrading the performance of the primary users in a real-world office building environment.