Current lidar and camera-based solutions for low-cost indoor mobile robots have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-based localization suffers from sparse point cloud generation, noise, and false detections. Thus, in this work, we introduce RaGNNarok, a real-time, lightweight, and generalizable graph neural network (GNN)-based framework to enhance radar point clouds, even in complex and dynamic environments. With an inference time of only 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs even on such resource-constrained devices, without additional computational resources. We evaluate its performance across key tasks, including localization, SLAM, and autonomous navigation, in three different environments. Our results demonstrate strong reliability and generalizability, making RaGNNarok a robust solution for low-cost indoor mobile robots.

Future wireless networks need to support the increasing demands for high data rates and improved coverage. One promising solution is sectorization, where an infrastructure node is equipped with multiple sectors employing directional communication. Although the concept of sectorization is not new, it is critical to fully understand the potential of sectorized networks, such as the rate gain achieved when multiple sectors can be simultaneously activated. In this paper, we focus on sectorized wireless networks, where sectorized infrastructure nodes with beam-steering capabilities form a multi-hop mesh network. We present a sectorized node model and characterize the capacity region of these sectorized networks. We define the flow extension ratio and the corresponding sectorization gain, which quantitatively measure the performance gain introduced by node sectorization as a function of the network flow. Our objective is to find the sectorization of each node that achieves the maximum flow extension ratio, and thus the sectorization gain. Towards this goal, we formulate the corresponding optimization problem and develop an efficient distributed algorithm that obtains the node sectorization under a given network flow with an approximation ratio of 2/3. Additionally, we emphasize the class of Even Homogeneous Sectorizations, which simultaneously enhances the efficiency of dynamic routing schemes with unknown arrival rates and increases network capacity. We further propose that if sectorization can be adapted dynamically over time, either a backpressure-driven or maximum weighted b-matching-based routing approach can be employed, thereby expanding the achievable capacity region while preserving stability under unknown traffic conditions. Through extensive simulations, we evaluate the sectorization gain and the performance of the proposed algorithms in various network scenarios.

Full-duplex (FD) wireless can significantly enhance spectrum efficiency but requires effective self-interference (SI) cancellers. RF SI cancellation (SIC) via frequency-domain equalization (FDE), where bandpass filters channelize the SI, is suited for integrated circuits (ICs). In this paper, we explore the limits and higher layer challenges associated with using such cancellers. We evaluate the performance of a custom FDE-based canceller using two testbeds; one with mobile FD radios and the other with upgraded, static FD radios in the PAWR COSMOS testbed. The latter is a lasting artifact for the research community, alongside a dataset containing baseband waveforms captured on the COSMOS FD radios, facilitating FD-related experimentation at the higher networking layers. We evaluate the performance of the FDE-based FD radios in both testbeds, with experiments showing 95 dB overall achieved SIC (52 dB from RF SIC) across 20 MHz bandwidth. We conduct network-level experiments for (i) uplink-downlink networks with inter-user interference, and (ii) heterogeneous networks with half-duplex and FD users, showing FD gains of$1.14\times $–$1.25\times $and$1.25\times $–$1.73\times $, respectively, confirming analytical results. We also evaluate the performance of an FD jammer-receiver, demonstrating a strong dependence on relative transmit power levels and modulation schemes.

In this work, we present RadCloud, a novel real-time framework for directly obtaining higher-resolution lidar-like 2D point clouds from low-resolution radar frames on resource-constrained platforms commonly used in unmanned aerial and ground vehicles (UAVs and UGVs, respectively); such point clouds can then be used for accurate environmental mapping, navigating unknown environments, and other robotics tasks. While high-resolution sensing using radar data has been previously reported, existing methods cannot be used on most UAVs, which have limited computational power and energy; thus, existing demonstrations focus on offline radar processing. RadCloud overcomes these challenges by using a radar configuration with 1/4th of the range resolution and employing a deep learning model with 2.25× fewer parameters. Additionally, RadCloud utilizes a novel chirp-based approach that makes obtained point clouds resilient to rapid movements (e.g., aggressive turns or spins) that commonly occur during UAV flights. In real-world experiments, we demonstrate the accuracy and applicability of RadCloud on commercially available UAVs and UGVs, with off-the-shelf radar platforms on-board.

A microwave radiometer (MWR) at NOAA-CESSRST in Manhattan, NYC has experienced interference from nearby sources operating in the 5G FR2 n257 band (26.50--29.50 GHz). In this poster, we produce interference using a mobile 28 GHz IBM Phased Array Antenna Module (PAAM). The mobile PAAM leverages a software-defined radio which offers flexibility in varying center frequency, modulation, gain, bandwidth, time schedule, and more. In this poster, we show preliminary experiments which successfully created controlled interference to a MWR's 28 GHz channel which lead to distortion in some of the MWR final products, such as water vapor profile. We transmitted a 10 MHz bandwidth OFDM signal with varying amplitude, observing the highly sensitive MWR voltage response to fractional dB increments of the transmitter gain. The mobile PAAM is characterized in an anechoic chamber and MWR measurements are taken at various azimuth angles to help estimate the MWR antenna pattern. Future work will develop a Spectrum Consumption Model to help enable coexistence of MWRs and Beyond-5G networks.

Next-generation wireless networks necessitate large signal bandwidth to support the growing demands of high data rates, which poses significant challenges in the design of real-time radio platforms. We demonstrate SPEAR, a realtime wideband software-defined radio (SDR) utilizing the Xilinx RFSoC ZCU216 evaluation board. SPEAR leverages a customized "Streaming Direct Memory Access (DMA)" IP to address the latency issues associated with DMA control, thereby enabling high bandwidth data streaming in real-time. It also features a Python-based hardware configuration tool and signal processing framework incorporating an OFDM-based Physical layer. We showcase a real-time data link using the direct RF radio architecture between two RFSoC ZCU216 boards, achieving an error vector magnitude (EVM) of 3.2% for 256QAM across a bandwidth of 1.25 GHz.

Next-generation wireless systems utilize large signal band-widths to meet the growing data rate demands of emerging applications and to provide enhanced resolution for wireless sensing and imaging. This poses significant challenges in the design of the underlying datapaths that carry and transfer signals across different domains, such as between memory and data converters in various software-defined radio (SDR) platforms. In this paper, we present the design and implementation of SPEAR, which is an SDR platform based on the Xilinx RFSoC ZCU216 evaluation board capable of supporting real-time streaming of signals with a bandwidth of up to 1.25 GHz employing the direct RF radio architecture. SPEAR features hardware-assisted direct memory access (DMA) control for real-time data streaming, and a Python-based hardware configuration tool and signal processing framework. Our experiments show that SPEAR can support a real-time bandwidth of up to 1.25 GHz for 256QAM modulation that satisfies the 3GPP error vector magnitude (EVM) requirement of 3.5%.

Beyond-5G and 6G wireless networks exploit the millimeter-wave (mmWave) frequency bands to achieve significantly improved data rates, and existing mmWave systems rely on analog single-user beamforming (SUBF) or hybrid multi-user beamforming (MUBF). In this work, we focus on improving the performance of multi-user communication in mmWave networks by exploring analog MUBF using an array of subarrays (ASA) with reduced system overhead and hardware complexity as it eliminates digital beamforming and the need for estimating the channel state information (CSI). We present Mambas, a novel system that maneuvers analog MUBF using an ASA to support simultaneous communication with multiple users located in close proximity, e.g., within the half-power beamwidth of the ASA. In essence, Mambas effectively decouples the user selection, subarray allocation, and beamforming optimization based on a comprehensive understanding of the multi-user support determined by the ASA. We evaluate Mambas using a 28 GHz software-defined radio testbed and show that, compared to existing methods, Mambas can effectively support users that are 2× more closely spaced while achieving an improved sum rate of up to 2×, using only two subarrays. Large-scale ray tracing-based simulations also show that Mambas can achieve a sum rate gain of 1.92--3.86× and is able to maintain consistent performance with significantly increased user density.

Next-generation Wi-Fi networks employ large signal bandwidth to meet the demands of high data rates, which poses challenges to Wi-Fi monitoring systems that typically rely on a full sampling rate receiver (RX) to capture signals at full bandwidth for demodulation and decoding. Interestingly, preambles of Wi-Fi packets contain unencrypted information that can be decoded to extract Wi-Fi Physical (PHY) layer information such as modulation and coding scheme (MCS), transmission time, and PHY service data unit (PSDU) length. In this paper, we propose DeepMon, which leverages low-cost RXs operating at sub-Nyquist sampling rates and deep learning (DL) to identify the Wi-Fi protocol and decode PHY layer packet properties from the Wi-Fi preamble. To evaluate DeepMon, we use PlutoSDR as the low sampling rate RX to collect a dataset of over 390K real-world 802.11a/n/ac Wi-Fi packets for the DL model training and testing. Our experiments show that for Wi-Fi packets with up to 160 MHz bandwidth, an RX running DeepMon at 3 MHz sampling rate (i.e., a downsampling ratio of >50×) can achieve an average bit decoding accuracy of 96.20% for the legacy signal field, corresponding to a mean absolute error of only 0.077 ms for predicting the packet transmission time.

We demonstrate RadCloud, a real-time framework for obtaining high-resolution lidar-like 2D point clouds from low-resolution millimeter-wave (mmWave) radar data on resource-constrained platforms commonly found on unmanned aerial and ground vehicles (UAVs and UGVs). Such point clouds can then be used for mapping key features of the environment, route planning and navigation, and other robotics tasks. Rad-Cloud is specifically optimized for UAVs and UGVs by using a radar configuration with 1/4th the range resolution, using a model with 2.25× fewer parameters, and reducing total sensing time by a factor of 250×. The real-time ROS framework will be demonstrated on a UGV and UAV equipped with CPU-only compute platforms in diverse environments.

Federated learning (FL) offers a privacy-preserving way to train models across decentralized data. However, fine-tuning pre-trained language models (PLMs) in FL is challenging due to restricted model parameter access, high computational demands, and communication overheads. Our method treats large language models (LLMs) as black-box inference APIs, optimizing prompts with gradient-free methods. This approach, FedBPT, reduces exchanged variables, boosts communication efficiency, and minimizes computational and memory costs. We demonstrate the practical implementation of FedBPT on resource-limited edge devices, showcasing its ability to efficiently achieve collaborative on-device LLM fine-tuning.

We present experiments using mobile 28 GHz Phased Array Antenna Modules (PAAMs), demonstrating their ability to perform beam steering with high granularity. The mobile node contains a 64-element IBM 28 GHz PAAM along with a USRP software defined radio, allowing for configuration of the transmit/receive (TX/RX) parameters. These parameters include beam shape, beam steering, and duty cycling. We demonstrate the capabilities of the mobile PAAMs by forming a wireless OFDM link between two mobile PAAMs. We then showcase the beam steering capabilities of the PAAM by performing beam sweeping on the RX PAAM to find the angle of arrival from the TX PAAM. A simple graphical user interface is presented for configuring the PAAMs. A tutorial is available online for users interested in experimentation with 28 GHz PAAMs*.

5G new radio (NR) employs frequency range 2 (FR2) in the millimeter-wave (mmWave) bands, which employs a much shorter slot duration compared to FR1 (sub-7 GHz) systems and, therefore, poses significant challenges for softwarized baseband processing in virtualized radio access networks (vRANs). Existing systems supporting software baseband processing focus on enabling (massive) multiple-input and multiple-output (MIMO) using multi-core edge server(s). These solutions may fail to meet the more stringent processing deadline in FR2 or require more intensive computational resources. In this paper, we present Savannah, an efficient mmWave baseband processing framework using minimal and heterogeneous computing resources including CPU and eASIC. Savannah addresses the challenges associated with baseband processing in FR2 by applying techniques for vectorizing matrix operations and memory access patterns, supporting heterogeneous computation via offloading LDPC decoding to an eASIC, and enabling single-core operation. We show that Savannah, using a single CPU core and the ACC100 accelerator, can support a 2×2 MIMO link with 100 MHz bandwidth, yielding a data rate of up to 487 Mbps.

5G new radio (NR) frequency range 2 (FR2) in the millimeter-wave (mmWave) band has a much shorter baseband processing deadline compared to that in the sub-7 GHz FR1 band. This tight deadline requires an efficient real-time system for baseband processing using minimal computational resources. We demonstrate Savannah, a software framework for efficient mmWave baseband processing using minimal and heterogeneous computing resources, including CPU and eASIC. Savannah vectorizes matrix operations and memory access patterns in multi-input multi-output (MIMO) arithmetic, offloads low-density parity-check (LDPC) coding to an eASIC, and enables single-core operation. We demonstrate that Savannah, using a single CPU core and an eASIC, can support a 2×2 MIMO link with 100 MHz bandwidth under full uplink traffic load, yielding a data rate of up to 487 Mbps.

The relentless demand for data in our society has driven the continuous evolution of wireless technologies to enhance network capacity. While current deployments of 5G have made strides in this direction using massive multiple-input-multiple-output (MIMO) and millimeter-wave (mmWave) bands, all existing wireless systems operate in a half-duplex (HD) mode. Full-duplex (FD) wireless communication, on the other hand, enables simultaneous transmission and reception (STAR) of signals at the same frequency, offering advantages such as enhanced spectrum efficiency, improved data rates, and reduced latency. This article presents a comprehensive review of FD wireless systems, with a focus on hardware design, implementation, cross-layered considerations, and applications. The major bottleneck in achieving FD communication is the presence of self-interference (SI) signals from the transmitter (TX) to the receiver, and achieving SI cancellation (SIC) with real-time adaption is critical for FD deployment. The review starts by establishing a system-level understanding of FD wireless systems, followed by a review of the architectures of antenna interfaces and integrated RF and baseband (BB) SI cancellers, which show promise in enabling low-cost, small-form-factor, portable FD systems. We then discuss digital cancellation techniques, including digital signal processing (DSP)- and learning-based algorithms. The challenges presented by FD phased-array and MIMO systems are discussed, followed by system-level aspects, including optimization algorithms, opportunities in the higher layers of the networking protocol stack, and testbed integration. Finally, the relevance of FD systems in applications such as next-generation (xG) wireless, mmWave repeaters, radars, and noncommunication domains is highlighted. Overall, this comprehensive review provides valuable insights into the design, implementation, and applications of FD wireless systems while opening up new directions for future research.

Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., location and person's identity). However, the literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them, resulting in the privacy-compromising HCWS design. In this work, we aim to bridge this gap to achieve the vision of secure human-centered wireless sensing. First, we propose a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses. Based on this framework, we present the taxonomy of existing inference attacks and defenses. As a result, we can identify the open challenges and gaps in achieving privacy-preserving human-centered wireless sensing in the era of machine learning and further propose directions for future research in this field.

Next-generation Wi-Fi systems embrace large signal bandwidth to achieve significantly improved data rates, while requiring efficient methods for network monitoring and spectrum sharing applications. A radio receiver (RX) operating at low sampling rates can largely improve the energy- and cost-efficiency in such systems if it can extract useful network information such as the duration and structure of wireless packets. In this paper, we present the design of Swirls, a novel framework for sniffing Wi-Fi Physical layer information using RXs operating at sampling rates that are (much) smaller than the signal bandwidth. Swirls consists of three modules tailored for low sampling rate RXs: joint packet detection, optimized RX frequency selection, and packet property decoder. We implement Swirls using three software-defined radio platforms and extensively evaluate Swirls in real-world scenarios. The experiments show that for 20/40 MHz 802.11n packets, Swirls with 5 MHz sampling rate can achieve a mean absolute error (MAE) of transmission time and physical service data unit length decoding of 0.06 ms and 1.91 kB, respectively, at only 10 dB signal-to-noise ratio. With the same setting, Swirls simultaneously achieves a classification accuracy for the modulation and coding scheme, number of spatial streams, and bandwidth of 95.3%, 96.1%, and 95.6%, respectively. In an extreme case for 160 MHz 802.11ac/ax packets, Swirls with 2.5 MHz sampling rate (i.e., a downsampling ratio of 64) can still achieve an MAE of transmission time decoding of 0.47/0.67 ms.

Future wireless networks need to support the increasing demands for high data rates and improved coverage. One promising solution is sectorization, where an infrastructure node (e.g., a base station) is equipped with multiple sectors employing directional communication. Although the concept of sectorization is not new, it is critical to fully understand the potential of sectorized networks, such as the rate gain achieved when multiple sectors can be simultaneously activated. In this paper, we focus on sectorized wireless networks, where sectorized infrastructure nodes with beam-steering capabilities form a multi-hop mesh network for data forwarding and routing. We present a sectorized node model and characterize the capacity region of these sectorized networks. We define the flow extension ratio and the corresponding sectorization gain, which quantitatively measure the performance gain introduced by node sectorization as a function of the network flow. Our objective is to find the optimal sectorization of each node that achieves the maximum flow extension ratio, and thus the sectorization gain. Towards this goal, we formulate the corresponding optimization problem and develop an efficient distributed algorithm that obtains the node sectorization under a given network flow with an approximation ratio of 2/3. Through extensive simulations, we evaluate the sectorization gain and the performance of the proposed algorithm in various network scenarios with varying network flows. The simulation results show that the approximate sectorization gain increases sublinearly as a function of the number of sectors per node.

This article extends the shadow tracking for video-synthetic aperture radar (SAR) from a single-target framework to a multitarget framework, which is crucial for SAR ground moving targets’ identification. Inspired by FairMOT, the multitarget tracking framework for SAR shadow tracking is improved by using the triplet attention (TriAtt) mechanism and the lightweight multiscale network. By employing the ability to fuse spatial and feature dimensions of TriAtt and combining the lightweight network optimized by multiscale encoder–decoder and dilated convolution, a fast multiscale feature extraction module (FMsFEM) embedded with TriAtt is proposed for better tracking efficiency and performance. Experiments on the Sandiego video-SAR dataset validate that the TriAtt mechanism can improve the tracking performance of deep layer aggregation (DLA)-34, DLA-18, and FMsFEM significantly. FMsFEM with embedded TriAtt outperforms the state-of-the-art network (FairMOT with backbones of DLA-34 and DLA-18) with much faster frame rates. The average frame rates of FMsFEM and FMsFEM-TriAtt reach 60.32 and 56.13 fps for datasets with an image size of$1088\times 608$, which are about three times higher than the frame rates of others.

Full-duplex (FD) wireless and phased arrays are both promising techniques that can significantly improve data rates in future wireless networks. However, integrating FD with transmit (Tx) and receive (Rx) phased arrays is extremely challenging, due to the large number of self-interference (SI) channels. Previous work relies on either RF canceller hardware or on analog/digital Tx beamforming (TxBF) to achieve SI cancellation (SIC). However, Rx beamforming (RxBF) and the data rate gain introduced by FD nodes employing beamforming have not been considered yet. We study FD phased arrays with joint TxBF and RxBF with the objective of achieving improved FD data rates. The key idea is to carefully select the TxBF and RxBF weights to achieve wideband RF SIC in the spatial domain with minimal TxBF and RxBF gain losses. Essentially, TxBF and RxBF are repurposed, thereby not requiring specialized RF canceller circuitry. We formulate the corresponding optimization problem and develop an iterative algorithm to obtain an approximate solution with provable performance guarantees. Using SI channel measurements and datasets, we extensively evaluate the performance of the proposed approach in different use cases under various network settings. The results show that an FD phased array with 9/36/72 elements can cancel the total SI power to below the noise floor with sum TxBF and RxBF gain losses of 10.6/7.2/6.9dB, even at Tx power level of 30dBm. Moreover, the corresponding FD rate gains are at least 1.33/1.66/1.68 ×.

To support experimentation with full-duplex (FD) wireless, we recently integrated two FlexICoN Gen-2 wideband FD radios in the open-access, city-scale NSF PAWR COSMOS testbed. Each integrated FD radio consists of an antenna, a customized Gen-2 RF self-interference (SI) canceller box, a USRP software-defined radio, and a remotely accessible compute node. The RF SI canceller box includes an RF canceller printed circuit board which emulates an integrated circuit implementation based on the technique of frequency-domain equalization. The Gen-2 canceller box can achieve up to 50 dB RF SI cancellation across 20MHz bandwidth. In this demo, we present the design and implementation of the open-acccess, remotely accessible FD radios that are integrated in the indoor COSMOS Sandbox 2 at Columbia University. We also demonstrate example experiments that are available to researchers, where demo participants can observe the visualized performance of the open-access FD radios.

This paper focuses on COSMOS - Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. It will enable researchers to explore the technology "sweet spot" of ultra-high bandwidth and ultra-low latency in the most demanding real-world environment. We describe the testbed's architecture, the design and deployment challenges, and the experience gained during the design and pilot deployment. Specifically, we describe COSMOS' computing and network architectures, the critical building blocks, and its programmability at different layers. The building blocks include software-defined radios, 28 GHz millimeter-wave phased array modules, optical transport network, core and edge cloud, and control and management software. We describe COSMOS' deployment phases in a dense urban environment, the research areas that could be studied in the testbed, and specific example experiments. Finally, we discuss our experience with using COSMOS as an educational tool.

Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized Greedy Maximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid-GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughput-optimal. Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of H-GMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16-$30\times $ better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm.

In order to support experimentation with full-duplex (FD) wireless, we integrated the FlexICoN Gen-2 wideband FD radio with the city-scale PAWR COSMOS testbed [1]. In particular, the implemented FD radio consists of an antenna, a customized Gen-2 RF self-interference (SI) canceller box, a USRP software-defined radio (SDR), and a compute node. The RF canceller box includes an RF SI canceller implemented using discrete components on a printed circuit board (PCB), which emulates its RFIC canceller counterpart. The Gen-2 RF SI canceller achieves 50dB RF SI cancellation across 20MHz bandwidth using the technique of frequency-domain equalization (FDE) [2]. In this abstract, we present the design and implementation of the remotely accessible Gen-2 wideband FD radio integrated with the COSMOS sandbox at Columbia University. We also present an example real-time wideband FD wireless link demonstration using the GNU Radio software.

The Cloud-Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multi-user advanced wireless testbed that is being deployed in West Harlem of New York City [1]. To keep pace with the significantly increased wireless link bandwidth and to effectively integrate the emerging C-RANs, COSMOS is designed to incorporate a fast programmable core network for providing connections across different computing layers. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies and experimentation directly in the optical physical layer. The optical architecture of COSMOS was presented in [2]. In this abstract, we present the tools and services designed to configure and monitor the performance of optical paths and topologies of the COSMOS testbed. In particular, we present the SDN framework that allows testbed users to implement experiments with application-driven control of optical and data networking functionalities.

Full-duplex (FD) wireless can significantly enhance spectrum efficiency but requires tremendous amount of self-interference (SI) cancellation. Recent advances in the RFIC community enabled wideband RF SI cancellation (SIC) in integrated circuits (ICs) via frequency-domain equalization (FDE), where reconfigurable RF filters are used to channelize the SI signal path. In [2], we designed and implemented an FDE-based RF canceller on a printed circuit board (PCB). We also presented an optimized canceller configuration scheme based on the derived canceller model, and extensively evaluated the performance of the FDE-based FD radios in a software-defined radio (SDR) testbed in different network settings.

Full-duplex (FD) wireless can significantly enhance spectrum efficiency but requires tremendous amount of self-interference (SI) cancellation. Recent advances in the RFIC community enabled wideband RF SI cancellation (SIC) in integrated circuits (ICs) via frequency-domain equalization (FDE), where RF filters channelize the SI signal path. Unlike other FD implementations, that mostly rely on delay lines, FDE-based cancellers can be realized in small-form-factor devices. However, the fundamental limits and higher layer challenges associated with these cancellers were not explored yet. Therefore, and in order to support the integration with a software-defined radio (SDR) and to facilitate experimentation in a testbed with several nodes, we design and implement an FDE-based RF canceller on a printed circuit board (PCB). We derive and experimentally validate the PCB canceller model and present a canceller configuration scheme based on an optimization problem. We then extensively evaluate the performance of the FDE-based FD radio in the SDR testbed. Experiments show that it achieves 95dB overall SIC (52dB from RF SIC) across 20MHz bandwidth, and an average link-level FD gain of 1.87x. We also conduct experiments in: (i) uplink-downlink networks with inter-user interference, and (ii) heterogeneous networks with half-duplex and FD users. The experimental FD gains in the two types of networks confirm previous analytical results. They depend on the users' SNR values and the number of FD users, and are 1.14x-1.25x and 1.25x-1.73x, respectively. Finally, we numerically evaluate and compare the RFIC and PCB implementations and study various design tradeoffs.

Full-duplex (FD) wireless and phased arrays are both promising techniques that can significantly improve data rates in future wireless networks. However, integrating FD with transmit (Tx) and receive (Rx) phased arrays is extremely challenging, due to the large number of self-interference (SI) channels. Previous work relies on either RF canceller hardware or on analog/digital Tx beamforming (TxBF) to achieve SI cancellation (SIC). However, Rx beamforming (RxBF) and the data rate gain introduced by FD nodes employing beamforming have not been considered yet. We study FD phased arrays with joint TxBF and RxBF with the objective of achieving improved FD data rates. The key idea is to carefully select the TxBF and RxBF weights to achieve wideband RF SIC in the spatial domain with minimal TxBF and RxBF gain losses. Essentially, TxBF and RxBF are repurposed, thereby not requiring specialized RF canceller circuitry. We formulate the corresponding optimization problem and develop an iterative algorithm to obtain an approximate solution with provable performance guarantees. Using SI channel measurements and datasets, we extensively evaluate the performance of the proposed approach in different use cases under various network settings. The results show that an FD phased array with 9/36/72 elements can cancel the total SI power to below the noise floor with sum TxBF and RxBF gain losses of 10.6/7.2/6.9 dB, even at Tx power level of 30 dBm. Moreover, the corresponding FD rate gains are at least 1.33/1.66/1.68X.

Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (UD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous networks (composed of UD and FD users). We develop the hybrid Greedy Maximal Scheduling (U-GMS) algorithm, which is tailored to the special characteristics of such heterogeneous networks and combines both centralized GMS and decentralized Q-CSMA mechanisms. Moreover, we prove that H-GMS is throughput-optimal. We then demonstrate by simple examples the benefits of adding FD nodes to a network. Finally, we evaluate the performance of U-GMS and its variants in terms of throughput, delay, and fairness between FD and UD users via extensive simulations. We show that in heterogeneous UD-FD networks, U-GMS achieves 5-10x better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm.

Wireless object tracking applications are gaining popularity and will soon utilize emerging ultra-low-power device-to-device communication. However, severe energy constraints require much more careful accounting of energy usage than what prior art provides. In particular, the available energy, the differing power consumption levels for listening, receiving, and transmitting, as well as the limited control bandwidth must all be considered. Therefore, we formulate the problem of maximizing the throughput among a set of heterogeneous broadcasting nodes with differing power consumption levels, each subject to a strict ultra-low-power budget. We obtain the oracle throughput (i.e., maximum throughput achieved by an oracle) and use Lagrangian methods to design EconCast - a simple asynchronous distributed protocol in which nodes transition between sleep, listen, and transmit states, and dynamically change the transition rates. We also show that EconCast approaches the oracle throughput. The performance is evaluated numerically and via extensive simulations and it is shown that EconCast outperforms prior art by 6x - 17x under realistic assumptions. Finally, we implement EconCast using the TI eZ430-RF2500-SEH energy harvesting nodes and experimentally show that in realistic environments it obtains 57% - 77% of the achievable throughput.

Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze Panda and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.

A demonstration of a real-time full-duplex wireless link is presented, in which a pair of full-duplex transceivers perform simultaneous transmission and reception on the same frequency channel. A full-duplex transceiver is composed of a custom-designed small-form-factor analog self-interference canceller, and a digital self-interference cancellation implementation is integrated with the National Instruments Universal Software Radio Peripheral (USRP). An adaptive analog self-interference canceller tuning mechanism adjusts to environmental changes. We demonstrate the practicality and robustness of the full-duplex wireless link through the National Instruments LabVIEW interface.

Object tracking applications are gaining popularity and will soon utilize energy harvesting low-power wireless nodes where power is mostly consumed for neighbor discovery. Such applications require the design and experimentation with low-power neighbor discovery protocols. We demonstrate the Panda protocol [4, 5] implementation using commercial off-the-shelf energy harvesting devices, based on the TI eZ430-RF2500-SEH prototype. The prototypes harvest indoor light energy to perform power-aware neighbor discovery, while maintaining a power budget. A custom-designed online monitoring system interactively demonstrates the network dynamics, including the energy storage levels of the devices, the neighbor discovery events, and aggregate discovery statistics.

Object tracking applications are gaining popularity and will soon utilize energy harvesting (EH) low-power nodes that will consume power mostly for neighbor discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailoredfor the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware ND Asynchronously (Panda) protocol, in which nodes transition between the sleep, receive, and transmit states. We analyze Panda and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less than 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3× compared with related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.

This paper studies online power control policies for outage minimization in a fading wireless link with energy harvesting transmitter and receiver. The outage occurs when either the transmitter or the receiver does not have enough energy, or the channel is in outage, where the transmitter only has the channel distribution information. Under infinite battery capacity and without retransmission, we prove that threshold-based power control policies are optimal. We thus propose disjoint/joint threshold-based policies with and without battery state sharing between the transmitter and receiver, respectively. We also analyze the impact of practical receiver detection and processing on the outage performance. When retransmission is considered, policy with linear power levels is adopted to adapt the power thresholds per retransmission. With finite battery capacity, a three dimensional finite state Markov chain is formulated to calculate the optimal parameters and corresponding performance of proposed policies. The energy arrival correlation between the transmitter and receiver is addressed for both finite and infinite battery cases. Numerical results show the impact of battery capacity, energy arrival correlation and detection cost on the outage performance of the proposed policies, as well as the tradeoff between the outage probability and the average transmission times.

This paper addresses the outage minimization problem for a wireless link where both the transmitter and the receiver are powered by harvested energy, and the energy arrival processes of both nodes are correlated. We propose three power control policies to minimize the outage probability, including threshold-based On-Off policy, joint scheduling policy, and linear power levels policy. With infinite battery capacity, we analyze the optimality of the thresholds with different correlations between energy arrivals at the transmitter and the receiver. With finite battery capacity, we use finite state Markov chain (FSMC) to obtain the optimality of our policies and also numerically evaluate their performance. The optimal thresholds for minimum outages are derived according to the average energy arrival rate and the system parameters. The numerical results show the performance gains using different policies, as well as the tradeoff between the minimum outage probabilities and the average transmission times.