This demo introduces WaveBox, a dynamic, software-defined waveform generation system developed to assess the resilience of communication networks against many types of interference scenarios. WaveBox features seamless waveform switching, allowing users to efficiently adjust interference patterns to adapt to diverse operational scenarios. We will showcase the system's effectiveness and versatility, highlighting its ability to adapt to evolving mission requirements. Additionally, the system's intuitive graphical user interface (GUI) supports rapid waveform adjustments, enhancing its responsiveness in dynamic environments. WaveBox can provide a flexible software-defined tool for evaluating the robustness of wireless systems.

We design and implement a new multiple-input multiple-output (MIMO) frequency-division-duplex (FDD) spread-spectrum link and demonstrate real-time high-definition (HD) video streaming over a Radio Frequency System-on-a-Chip (RFSoC) software-radio testbed. To the best of our knowledge, this is the first-of-its-kind high-throughput low-latency full-duplex spread-spectrum link on RFSoC platforms which demonstrates an aggregated data throughput of 122 Mbps that supports real-time recording and playback of uncompressed full-HD video. The testbed comprises two Xilinx Zynq Ultrascale+ RFSoC ZCU111 evaluation kits with a custom-built application layer. A host-based graphical user interface (GUI) demonstrates live performance of the proposed 4×4 MIMO wireless link in terms of error vector magnitudepre-detection SINR and bit error rate (BER) and enables on-the-fly reconfiguration of link parameters such as spreading code sequence, transmit/receive antenna gains.

In highly dynamic and contested RF environments, communication systems must swiftly adapt to fluctuating spectral conditions while ensuring network quality of service (QoS). Maintaining link synchronization and spectral efficiency during waveform adaptation is particularly challenging due to the high mobility and autonomy of devices, coupled with the possibility of operating in GPS-denied environments. In this demo, we introduce a scalable, high-speed FPGA-based parallel decoding algorithm that leverages the HORNets signature adaptation protocol to address these challenges. Our solution preserves link synchronization within a multi-node network and enables efficient, lightweight waveform adaptation without reliance on GPS. The algorithm's resilience and effectiveness are demonstrated using a three-node cluster configuration, all subjected to non-colored or colored intentional interference.

We consider the problem of interference avoidance in orthogonal frequency-division multiplexing (OFDM) communication systems. Unlike conventional OFDM systems that rely on static waveform designs, we propose to apply a coding sequence at the input of the inverse discrete Fourier transform (IDFT) operator to digitally shape the transmitted OFDM waveform. By dynamically optimizing the coding sequence to maximize the signal-to-interference-plus-noise ratio (SINR) at the receiver, the proposed approach enables resilient communication in heavily congested spectral environments in a technically simple and efficient manner. We carry out extensive simulation studies to evaluate the performance of the proposed system under various interference scenarios and demonstrate significant improvements in SINR when compared to conventional OFDM systems. When the proposed dynamic OFDM waveform optimization process is fielded in commercially available software-defined radio platforms, the transceiver can evade rapidly changing (msec–scale or faster) interference and survive in contested spectral environments.

Support vector machines (SVMs) have been the learning model of choice in numerous classification applications. While SVMs are widely successful in real-world deployments, they remain susceptible to mislabeled examples in training datasets where the presence of few faults can severely affect decision boundaries, thereby affecting the model's performance on unseen data. In this brief, we develop and describe in implementation detail a novel method based on $L_{1}$ -norm principal-component data analysis and geometry that aims to filter out atypical data instances on a class-by-class basis before the training phase of SVMs and thus provide the classifier with robust support-vector candidates for making classification boundaries. The proposed dataset curation method is entirely data-driven (touch-free), unsupervised, and computationally efficient. Extensive experimental studies on real datasets included in this brief illustrate the $L_{1}$ -norm curation method and demonstrate its efficacy in protecting SVM models from data faults during learning.

We consider the problem of dynamically optimizing a multiple-input multiple-output (MIMO) wireless waveform in a given potentially heavily utilized fixed frequency band with applications in near-field or far-field autonomous machine-to-machine communications. In particular, we find the transmitter beam weight vector and the pulse code sequence that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR joint space-time receiver filter. We propose and derive two novel model-based solutions: (a) Disjoint, space first (transmit weight vector) then time (pulse code sequence) waveform optimization and (b) jointly optimal transmit weight vector and pulse code sequence optimization (a mixed integer programming problem.) The proposed formally derived algorithmic solutions are studied in extensive simulations under varying waveform code length, near-field/far-field and spread-spectrum/ non-spread-spectrum interference, in light and dense interference scenarios. Our findings highlight the effectiveness of the described methods compared to static conventionally designed MIMO links and the remarkable ability of the joint space-time optimized waveforms to avoid heavy interference.

Fast, robust, high-accuracy localization is a key enabler for future location-aware applications in streetscape communication networks and next-generation networked autonomous agents. Specifically, massive multiple-input and multiple-output (MIMO) antenna systems have received increasing attention due to high angular resolution. However, in dense multipath environments, such as urban areas, pure direction-of-arrival (DoA)-based techniques have not been very popular due to large localization errors.In this paper, we present and evaluate, on real measurements from the POWDER-RENEW platform, a novel method to carry out DoA estimation from just one antenna array snapshot. The measurements are taken from an indoor testbed that is based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system. Experimental results – in the presence of spatial aliasing – show that for certain emitter locations our proposed universal one-shot DoA estimator outperforms in azimuth/elevation accuracy state-of-the-art subspace-based methods that involve collection of a sufficiently large data record of antenna array snapshots.

Wireless remote control of a single or a group of underwater vehicles by a single human operator offers the opportunity to collect more real-time data than a single ship or vehicle. Commercial underwater modems are often too large to fit small-size submersibles, prohibitively expensive for large-scale deployments, and typically closed source which limits their compatibility with other sensors and therefore their application in research. In this work, we focus on establishing wireless communication between a remotely operated vehicle (ROV) and a surface station using an in-house built low-size, weight, power, and cost underwater acoustic modem and an affordable underwater ROV. To assess wireless communication performance we built a high-fidelity simulation framework in an underwater robotics simulator. Additionally, we verify the simulation with physical tests of the ROV and modems. Finally, to evaluate the feasibility of deploying a fleet of ROVs we designed and built a small, lightweight, low-cost ROV. This ROV will serve as the platform to test connected underwater robotics technology in future work.

In this article we explore new techniques that can enable open remote experimentation for mobile networks. We first propose a cloud-based framework called CloudRAFT, based on which experimenters are allowed to remotely access and control experimental resources via public cloud AWS and share the resulting data and code via the cloud. Then, we discuss the enabling techniques for CloudRAFT, including Amazon serverless service, VNC-based remote command line, and Websocket-based real time communications, among others. Finally, we showcase the application of these techniques in enabling remote access to UB NeXT, a software-defined testbed that has been developed at University at Buffalo for wireless mobile network modeling, optimization and deployment. This work verifies the feasibility of accessing, controlling and sharing wireless testbeds through a remote public cloud.

We introduce RF-SITL, a radio frequency (RF) software-in-the-loop (SITL) channel emulator developed with GNU Radio and the University at Buffalo’s Airborne Networking and Communications (UB-ANC) emulator to enable integrated simulation of systems comprising multiple unmanned aerial vehicles (UAVs) interacting over a wireless communication channel. RF-SITL could be paired with any multi-robot simulator to enable I/Q sample-level fidelity simulation of communication interactions between the robots by accurately simulating channel effects, including interference, noise, distance-dependent path loss, and packet losses. RF-SITL works as follows: 1) it instantiates a virtual software-defined transceiver in GNU Radio for each UAV simulated in the UB-ANC Emulator; 2) it builds an interference channel model in which each network node receives the superposition of signals transmitted from other nodes; and 3) it synchronizes the location of each simulated UAV in the UB-ANC Emulator with the virtualized RF transceivers in RF-SITL, such that the communication channel between nodes can accurately model distance-dependent channel effects, such as path loss. With these capabilities, we can use both off-the-shelf and custom-built signal processing flowgraphs that simulate Gaussian Minimum Shift Keying (GMSK), 802.11-like Orthogonal Frequency Division Multiplexing (OFDM), and direct sequence spread-spectrum (DSSS) links in GNU Radio to simulate swarm UAV networks prior to their deployment in software-defined radios in a swarm UAV network.

We prove, for the first time in the literature of communication theory and machine learning, the equivalence of joint maximum-likelihood (ML) optimal channel estimation and data detection (JOCEDD) to the problem of finding the L1-norm principal components of a real-valued data matrix. Optimal algorithms for L1-norm principal component analysis (PCA) are therefore direct solvers to the problem of interest, thus the proposed JOCEDD approach requires a polynomial number of operations. To avoid high computational costs incurred by the exact calculation of optimal L1principal components, we implement an efficient bit flipping-based algorithm for L1-norm PCA in a software-defined radio. In particular, we carry out experiments with two radios that operate at Wi-Fi frequencies in a multipath indoor radio environment and have no direct line-of-sight. We apply L1-norm PCA for JOCEDD over short frames that are transmitted over the single-input single-output communication link. We compare the performance of supervised data-aided channel estimation techniques versus JOCEDD in terms of bit-error-rate and demonstrate the superiority of the proposed approach across a wide range of signal-to-noise ratios.

We envision dense network deployments of Internet-of-Things (IoT) connected devices that report data to a common base station (BS). The devices utilize repeats of a basic shaping pulse occupying the entire continuum of the device-accessible spectrum. We propose an optimal algorithm to adaptively design sparse waveforms with well-placed energy that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear filter at the BS. Additionally, we propose a computationally efficient suboptimal waveform design algorithm for the same problem. Simulation studies show that the proposed waveform designs attain superior pre-detection SINR performance than conventional binary, quaternary, and sparse-binary/quaternary waveform designs, thus offering a promising PHY-MAC approach to maintain wireless connectivity in overloaded network setups.

We consider the problem of interference avoidance via all-spectrum digital waveform design in wireless communication links that operate in multipath fading environments. Specifically, we select a square-root raised cosine pulse-shaping signal that occupies all-hardware accessible frequency bandwidth. We propose an algorithm that optimizes a sequence of L sign/phase-shifted repeats of the basic shaping pulse to form the all-spectrum digital waveform that will carry our information symbols. The sequence can take values from either binary or quaternary alphabets. We propose to optimize the specific values of the sign/phase shift sequence via bit flipping such that the signal-to-interference-plus-noise ratio (SINR) at the output of the max-SINR linear receiver is maximized at any given time. The complexity of the proposed algorithm is O( L3) and is independent of the alphabet size. Simulation studies demonstrate that the proposed digital waveform designs achieve practically the same SINR post-filtering performance with max-SINR optimal waveforms designed via exhaustive search.

We consider the problem of blind channel estimation with minimal pilot signaling in multi-cell multi-user MIMO systems with very large antenna arrays at the base station. We develop a least-squares (LS)-type algorithm that iteratively extracts channel and data estimates in short-data record multicell massive MIMO environments with no prior channel state information. The proposed algorithm utilizes a novel initialization step that is based on auxiliary-vector (AV) subspace decomposition. Simulation studies show that for pilot signaling of about 4%, information data extraction can be achieved with lower probability of error than eigendecomposition-based initialization techniques, while for observation records of sufficient length it nearly attains the error rate performance achieved with complete knowledge of the channels.

We introduce maximum-SINR sparse-binary waveforms that modulate data information symbols from any finite alphabet and span the whole continuum of the available/device-accessible spectrum. We offer an optimal algorithm that designs the proposed waveforms by maximizing the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear receiver. In addition, we offer a suboptimal algorithm for the same problem with significantly reduced computational complexity. The post-filtering SINR improvements attained by the proposed waveforms in a single-input single-output (SISO) communication system with colored interference are presented analytically. Simulation studies compare the proposed waveforms with their conventional non-sparse counterparts and demonstrate their superior SINR performance.

We design, implement, and evaluate an acoustic receiver structure for distributed multi-input and multi-output (MIMO) underwater systems that accounts for multiple carrier frequency offsets (CFOs) and multiple timing offsets (TOs) encountered in real deployments of underwater communication systems. We focus on challenging practical issues that arise in underwater acoustic sensor network setups where co-located multi-antenna sensor deployment is not feasible due to power, computation, and hardware limitations. In this paper, we utilize distributed underwater sensors to form virtual MIMO underwater systems without requiring frequency or time synchronization. The proposed receiver consists of a bank of matched filters (one per effective CFO) at each receive antenna, followed by an information symbol detector. Each filter in the bank is sampled at the symbol rate with sampling timing selected according to the corresponding TO. We evaluate in real-time the performance of our algorithmic developments in a software-defined underwater testbed that utilizes in-house built software-defined acoustic modems (SDAMs). Experimental studies in both indoor, lab-controlled (tank) and outdoor (lake) real-world environments demonstrate superior bit-error-rate (BER) receiver performance compared to receiver designs that are not able to accommodate multiple CFOs and multiple TOs.

In this paper we design, implement, and experimentally evaluate a wireless software-defined radio platform for cognitive channelization in the presence of narrowband or wideband primary stations. Cognitive channelization is achieved by jointly optimizing the transmission power and the waveform channel of the secondary users. The process of joint resource allocation requires no a-priori knowledge of the transmission characteristics of the primary user and maximizes the signal-to- interference-plus-noise ratio (SINR) at the output of the secondary receiver. This is achieved by designing waveforms that span the whole continuum of available/device-accessible spectrum, while satisfying a peak power constraint for the secondary users and an interference temperature (IT) constraint for the primary users. We build a four-node software-defined radio testbed and experimentally demonstrate in an indoor laboratory environment the theoretical concepts of all-spectrum cognitive channelization in terms of pre-detection SINR and bit-error-rate (BER) at both primary and secondary receivers.

Existing commercial wireless systems are mostly hardware-based, and rely on closed and inflexible designs and architectures. Moreover, despite recent significant algorithmic developments in cross-layer network adaptation and resource allocation, existing network architectures are unable to incorporate most of these advancements. While software-defined radio (SDR) was envisioned as a new paradigm promising radical runtime adaptation through all layers of the networking protocol stack, the reality of the state-of-the-art in wireless networking practice is far from having fulfilled such promise of fast and intelligent reconfigurability and adaptability. Networking research based on the “software-defined radio” paradigm has suffered almost invariably from the lack of adequate and coherently designed abstractions to (i) define networking protocols and their cross-layer interactions across all layers of the protocol stack; (ii) define decision-making algorithms to control such interactions. To address this need, we introduce RcUBe (Real-time Re-configurable Radio), a novel architectural radio framework based on abstractions that offer real-time reconfigurability and optimization capabilities at the PHY, MAC, and network layers of the protocol stack. Unlike state-of-the-art solutions, RcUBe offers a structured methodology at variable levels of abstraction to accommodate implementations of a wide range of network architectures and protocols and complex decision-making in a modular, platform-independent way. RcUBe provides these features through a design structured into four distinct, but interacting planes, namely decision, control, data, and register plane. The broad capabilities of the proposed framework are demonstrated on a network level software-defined radio setup through a range of experiments where RcUBe is used to implement various reconfigurable functionalities of a wireless system at the PHY, MAC, and network layer.

This work is motivated by the reachback connectivity scenario in resource-constrained wireless sensor networks (WSNs): a single terminal at maximum power cannot establish a reliable communication link with the intended destination. Thus, neighboring distributed transmitters should contribute their radios and transmission power, in order to achieve reliable transmission of a common message. This work is particularly interested in low-SNR scenarios with unreliable feedback channels, no channel state information (CSI), and commodity radios, where carrier phase/frequency synchronization is not possible. Concrete non-coherent maximum likelihood and energy detection receivers are developed for zero-feedback distributed beamforming. The proposed receivers are compared with non-coherent energy harvesting reception, based on simple time-division multiple access. It is shown that the proposed zero-feedback distributed beamforming receivers overcome connectivity adversities at the low-SNR regime. This is achieved by exploiting signals' alignment of$M$distributed transmitters (i.e., beamforming), even with commodity radios, at the expense of network (total) power consumption. Application scenarios include resource-constrained WSNs or emergency radio situations.

We present the setup of a complete software-defined radio (SDR) testbed for non-coherent zero-feedback distributed beamforming. Three custom-built, embedded RF transceivers along with a commodity, low-cost SDR commercial receiver are deployed in an indoors lab environment. In sharp contrast with prior art on collaborative beamforming, the proposed scheme assumes no feedback between receiver and transmitters and no access to the transmitters' local oscillators for carrier phase adjustments. Quite interestingly, frequency offsets are exploited in this work. Zero-feedback beamforming with unsynchronized carriers is experimentally validated in terms of bit-error-rate (BER) and compared with simulation results. To the best of our knowledge, this is the first testbed for demonstrating and evaluating zero-feedback, channel state information (CSI)-free, distributed beamforming.