Radio Access Networks (RANs) are increasingly softwarized and disaggregated, resulting in diverse and interoperable components running on commodity hardware and compute platforms. In this demo, we present a real-time RAN testing framework, called ETHOS, designed to capture fine-grained metrics on performance, energy consumption, and computational efficiency for O-RAN-compliant systems across a range of compute platforms. ETHOS also interfaces with the RAN stack for real-time controlling to test various scheduling algorithms. ETHOS leverages a shared memory-based inter-process communication (IPC) framework and automates testing through the emulation of diverse user traffic and channel conditions. We deploy ETHOS on the NVIDIA ARC-OTA platform in this demonstration to show its effectiveness. ETHOS is designed to be easily portable to other commercial and open-source RAN software, offering the potential for a unified testing and evaluation framework to accelerate O-RAN research and innovation.

In the rapidly evolving field of wireless communication, Multiple Input Multiple Output (MIMO) networks have emerged as a pivotal technology, offering enhanced data rates and spectral efficiency by leveraging multiple antennas at both the transmitter and receiver. The introduction of Open Radio Access Network (O-RAN) architecture has further revolutionized this domain, enabling greater flexibility, scalability, and interoperability through its open interfaces and software-defined approach. This paper presents DRAGON, a novel Deep Reinforcement Learning (DRL)-based framework for joint Layer and Modulation and Coding Scheme (MCS) selection, tailored for downlink single-user MIMO networks under the O-RAN framework. Our approach is designed to be highly scalable, capable of efficiently managing a large number of configuration options in one-shot prediction, including up to 25 MCS and 4 layer choices. The proposed solution has been rigorously evaluated using an O-RAN-based simulation environment, demonstrating up to an 18% performance improvement over the state-of-the-art (SOTA) methods and achieving a best throughput of 87.4% when compared to the collected ground-truth dataset. Furthermore, our method supports real-time prediction, making it viable for practical deployment. In addition to these advancements, we explore the potential integration of our DRL-based solution with real-world platforms and discuss the extension of our approach to handle multi-user (MU) MIMO scenarios, paving the way for broader applications in next-generation wireless networks.

We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the underdamped Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between computational complexity and performance as required by the application at hand. This balance is achieved by tuning hyperparameters that control the length of the simulated Langevin dynamic. Through numerical experiments, we demonstrate that our detector yields lower SER than competing approaches (including learning-based ones) with a lower running time compared to a previously proposed overdamped Langevin-based MIMO detector.

Wireless channels are vulnerable to eavesdroppers due to their broadcast nature. One approach to thwart an eavesdropper (Eve) is to decrease her SNR, e.g., by reducing the signal in her direction. Unfortunately, such methods are vulnerable to (1) a highly directional Eve that can increase her received signal strength and (2) Eve that is close to the receiver, Bob, or close to the transmitter, Alice. In this paper, we design and experimentally evaluate Multipath Multicarrier Misinformation to Adversaries (M3A), a system for Alice to send data to Bob while simultaneously sending misinformation to Eve. Our approach does not require knowledge of Eve's channel or location and, with multipath channels, randomly transforms Eve's symbols even if Eve is located one wavelength-scale distance from Bob (approximately 10 cm) or if Eve is located between Alice and Bob in their direct path (Eve is approximately 1/3 closer to Alice). In particular, our approach is to move each of Eve's received symbols (over time and across subcarriers), to an independently random transformation as compared to Bob, without Alice or Bob knowing Eve's location or channel. We realize this by modulating Alice's per-subcarrier beamforming weights with an i.i.d. random binary sequence, as if Alice had a separate antenna array for each subcarrier, and could randomly turn antennas in each array on and off. We implement M3A on a real-time Massive MIMO testbed and show that M3A can increase Eve's bit error rate more than two hundredfold compared to beamforming, even if she is positioned approximately a wavelength away, whether above, below, or beside Bob. Finally, to ensure reliability at Bob, we show that with M3A, Bob's bit error rate is approximately an order of magnitude lower than achieved with prior work.

Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term– the score of the likelihood– by a neural network. Through numerical experiments in both synthetic and real-world data, we show that our proposed detector yields state-of-the-art symbol error rate performance and the robust version becomes noise-variance agnostic.

Massive multiple-input multiple-output (MIMO) is a key technology in 5G New Radio (NR) to improve spectral efficiency. A major challenge in its realization is the huge amount of real-time computation required. All existing massive MIMO baseband processing solutions use dedicated and specialized hardware like FPGAs, which can efficiently process baseband data but are expensive, inflexible and difficult to program. In this paper, we show that a software-only system called Agora can handle the high computational demand of real-time massive MIMO baseband processing on a single many-core server. To achieve this goal, we identify the rich dimensions of parallelism in massive MIMO baseband processing, and exploit them across multiple CPU cores. We optimize Agora to best use CPU hardware and software features, including SIMD extensions to accelerate computation, cache optimizations to accelerate data movement, and kernel-bypass packet I/O. We evaluate Agora with up to 64 antennas and show that it meets the data rate and latency requirements of 5G NR.

We present the third generation of Argos platforms, ArgosV3, intended for real-world applications and research. Developed from scratch specifically for many-antenna MU-MIMO, ArgosV3 is highly efficient in space, power, computation, and cost. While this new platform is highly configurable, featuring FPGA SoCs and frequency agile transceivers capable of operation from 50 MHz to 3.8 GHz, it is also highly compact and power efficient, enabling a complete 160 radio base station in less than 2 cubic feet. ArgosV3 is currently being deployed in a campus-wide multi-cell many-antenna network. For our demonstration we will show a single ArgosV3 base station serving multiple clients using a realtime LTE stack.

Full duplex communication promises a paradigm shift in wireless networks by allowing simultaneous packet transmission and reception within the same channel. While recent prototypes indicate the feasibility of this concept, there is a lack of rigorous theoretical development on how full duplex impacts medium access control (MAC) protocols in practical wireless networks. In this paper, we formulate the first analytical model of a CSMA/CA based full duplex MAC protocol for a wireless LAN network composed of an access point serving mobile clients. There are two major contributions of our work: First, our Markov chain-based approach results in closed form expressions of throughput for both the access point and the clients for this new class of networks. Second, our study provides quantitative insights on how much of the classical hidden terminal problem can be mitigated through full duplex. We specifically demonstrate that the improvement in the network throughput is up to 35-40 percent over the half duplex case. Our analytical models are verified through packet level simulations in ns-2. Our results also reveal the benefit of full duplex under varying network configuration parameters, such as number of hidden terminals, client density, and contention window size.

Wireless RF power transmission promises battery-less, resilient, and perpetual wireless sensor networks. Through the action of controllable Energy Transmitters (ETs) that operate at-a- distance, the sensors can be re-charged by harvesting the radiated RF energy. However, both the charging rate and effective charging range of the ETs are limited, and thus multiple ETs are required to cover large areas. While this action increases the amount of wireless energy injected into the network, there are certain areas where the RF energy combines destructively. To address this problem, we propose a duty-cycled random- phase multiple access (DRAMA). Non-intuitively, our approach relies on deliberately generating random interferences, both destructive and constructive, at the destination nodes. We demonstrate that DRAMA optimizes the power conversion efficiency, and the total amount of energy harvested. Through real-testbed experiments, we prove that our proposed scheme provides significant advantages over the current state of the art in our considered scenario, as it requires up to 70% less input RF power to recharge the energy buffer of the sensor in the same time.

Cognitive radio (CR) networks will enable dynamic spectrum re-use and thereby accelerate the adoption of high bandwidth services in available licensed frequencies with better channel characteristics. However, the possibility of the licensed user reclaiming the channel raises additional concerns on how best to reserve resources for secondary users (SUs) that are likely to have different qualities of service (QoSs) depending on their application requirements. This paper addresses the problem of spectrum resource management for co-located SUs with both streaming and intermittent data by efficiently identifying the number of backup channels that will ensure seamless end to end service. The contributions of this paper are threefold: First, a comprehensive analytical framework based on queueing theory is devised to calculate the theoretical delay in accessing the spectrum depending on the required QoS, with guidelines on how to optimize the set of back-up channels for possible future use; second, a method of spectrum allocation for SUs with these different QoS demands is formulated, especially as they co-exist and affect the performance of each other; third, a case study of applying these techniques in a novel application area of wireless medical telemetry is presented. Results reveal that the simulated spectral efficiency of the channel allocation using our approach matches closely with our theoretical predictions, within a 5% bound.

Wireless Medical Telemetry Systems (WMTS) currently operate on FCC designated bands for transmitting critical patient health information to distant receivers within hospitals. However, the current devices experience intermittent interference from digital TV transmissions in neighboring channels; are prohibited from transmitting multimedia data; and must operate with a secondary access priority in portions of the WMTS band, also shared with utility metering. We propose a fundamentally new communication paradigm for medical telemetry through dynamic spectrum access technology that adheres to the access rules in the WMTS band, and yet addresses the above concerns. The contributions of the paper are as follows: (i) we undertake a spectrum measurement study at hospital locations in the Boston area to model spectrum usage and activities in the medical band, (ii) we formulate the channel and power allocation task as an optimization problem under constrains of permissible electromagnetic interference to sensitive medical equipment, and latency, bandwidth thresholds of the medical data. Simulation results reveal the potential benefit of the use of dynamic spectrum access to improve medical telemetry and promises long-term improvement in the healthcare domain.

Cognitive radio (CR) ad hoc networks are composed of wireless nodes that may opportunistically transmit in licensed frequency bands without affecting the primary users of that band. In such distributed networks, gathering the spectrum information is challenging as the nodes have a partial view of the spectrum environment based on the local sensing range. Moreover, individual measurements are also affected by channel uncertainties and location-specific fluctuations in signal strength. To facilitate the distributed operation, this paper makes the following contributions: (i) First, an experimental study is undertaken to measure the signal characteristics for indoor and outdoor locations for the TV channels 21 – 51, and these results are used to identify the conditions under which nodes may share information. (ii) Second, a Cooperative reinforcement LearnIng scheme for Cognitive radio networKs (CLICK) is designed for combining the spectrum usage information observed by a node and its neighbors. (iii) Finally, CLICK is integrated within a MAC protocol for testing the benefits and overhead of our approach on a higher layer protocol performance. The proposed learning framework and the protocol design are extensively evaluated through a thorough simulation study in ns-2 using experimental traces of channel measurements.

In this paper a novel signaling protocol for co-existence and spectrum sharing among cognitive radio nodes is proposed. This protocol allows the radios to rendezvous with each other in a statically allocated spectrum band through on-off keying signaling and reliable spectrum sensing. It enables the radios to have non-cooperative communication in the sense that they do not need to exchange their modulation schemes and frequency channel. The proposed protocol requires no synchronization and is augmented with power control mechanisms. Analysis of the proposed protocol in terms of timing, probability of a successful connection and energy efficiency is also given.

Wireless sensor networks are equipped with batteries with limited charge, and are often deployed in conditions that make their retrieval and replacement infeasible. Thus, energy conservation has been a primary consideration for protocol design for such networks. Recent advancements in the transfer of energy wirelessly over large distances, such as through radio frequency electromagnetic (EM) waves and magnetic coupling, may give rise to a new class of networks that allow the sensors to be charged on the field, thereby prolonging the network lifetime. Moreover, wireless charging though EM waves may be undertaken in the same unlicensed band as that used for communication, leading to several unique protocol design challenges for such a network. The contribution of this paper is threefold: First, a set of experiments is undertaken to investigate the effect of distance and location on the energy transfer through EM waves. Second, a new routing metric based on the charging ability of the sensor nodes is proposed. Finally, an optimization framework is developed to determine the optimal charging and transmission cycle for the sensor network, resulting in enhanced lifetime of the network under user-specified end-to-end constraints of throughput and latency.