Mobile distributed MIMO (MD-MIMO) is an innovative extension of distributed MIMO, where mobile radio nodes with antenna arrays connect wirelessly to a base station. To explore the potential of these systems, we developed a software-defined radio (SDR) testbed and created a prototype implementation. This testbed serves as a platform for research and prototyping of MD-MIMO systems.

Physical layer key generation techniques aim to extract secret keys from the information contained in wireless channels. However, existing key generation schemes often rely on time-frequency domain waveforms for channel estimation, which not only makes secret extraction less reliable but may also compromise the confidentiality of the extracted secret information. This paper presents physical layer key generation methods relying on the Orthogonal Time Frequency and Space (OTFS) waveform. We present analysis showing that the delay-Doppler domain channel estimates obtained using OTFS are conducive to more secure and reliable secret extraction than time-frequency domain channel estimates obtained using the prevalent Orthogonal Frequency Division Multiplexing (OFDM). This analysis provides theoretical guarantees under certain simple assumptions. We then relax those assumptions in extensive time-division duplex (TDD) simulations and show that under realistic settings, OTFS offers the expected benefits to reliability and security. Our simulations show that the introduced OTFS schemes can reliably extract secret keys from channel estimates in scenarios where time-frequency domain methods deteriorate.

Multi-user interference and adversarial jamming are critical issues affecting the ultra-high capacity and reliability of next-generation (NextG) wireless networks. Conventional interference mitigation techniques usually rely on model-based analysis, which cannot fully address new challenges in NextG systems. Reservoir computing (RC), a new brain-inspired machine learning paradigm, can outperform conventional methods for various tasks in wireless systems due to its efficient training algorithm and minimum requirements for training data. However, its resilience in interference scenarios has yet to be thoroughly investigated. This paper explores the performance improvement for MIMO-OFDM systems under smart jamming attacks and introduces a new resilient RC architecture (ResRC) for iterative interference detection and mitigation. The performance results from Monte Carlo simulations show that ResRC can effectively mitigate interference and improve system reliability and capacity. A software-defined radio prototype receiver further verifies the ResRC architecture design in various real-world scenarios.

Despite the promises of current and future cellular networks to increase security, privacy, and robustness, 5G networks are designed to streamline discovery and initiate connections with limited computation and communication costs, leading to the predictability of control channels. This predictability enables signal-level attacks, particularly on unprotected initial access signals. To assess vulnerability in access control and enhance robustness in cellular networks, we present a strategic approach leveraging O-RAN architecture in this paper that detects and classifies signal-level attacks for actionable countermeasure defense. We evaluate attack scenarios of various power levels on both 4G/LTE-Advanced and 5G communication systems. We categorize the types of attack models based on the attack cost: Overshadowing and Jamming. Overshadowing represents low attack power categories with time and frequency synchronization, while Jamming represents un-targeted attacks that cause similar quality-of-service degradation as overshadowing attacks but require high power levels. Our detection strategy relies on supervised machine-learning models, specifically a Reservoir Computing (RC) based supervised learning approach that leverages physical and MAC-layer information for attack detection and classification. We demonstrate the efficacy of our detection strategy through extensive experimental evaluations using the O-RAN platform with software-defined radios (SDRs) and commercial off-the-shelf (COTS) user equipment (UEs). Empirical results show that our method can classify the change in statistics caused by most overshadowing and jamming attacks with more than 95% classification accuracy.

In recent years, unmanned aerial vehicles (UAVs) have been deployed in a range of new applications such as remote surveillance, package delivery and relief operations. The existing scenario of next-generation communications systems envisions the use of UAVs as low altitude platforms (LAPs) as one of the enabling technologies of next-gen networks. Telecom operators have been exploring low-altitude UAV-based communications solutions for on-demand deployment. The emerging possibilities of UAVs in air-to-ground (AG) communication necessitate accurate channel models in order to facilitate the design and implementation of such AG links. However, the propagation channels of Pakistan and in general the South Asian region have not been as of yet widely investigated. In this paper, a comprehensive study is presented on the air-to-ground channel parameters along with details of measurement campaigns as well as the limitations of this work and future research directions.