This paper presents an analytical analysis of a spread spectrum underlay channel designed to coexist with the 5G New Radio (NR) Orthogonal Frequency Division Multiplexing (OFDM) waveform. This coexistence is intended for scenarios where this underlay channel is utilized by either a 5G base station or users. This analytical investigation plays a crucial role in designing the underlay-5G OFDM waveform and facilitates decisions related to the relative average symbol energy levels of the 5G channels, i.e., Physical Downlink Shared Channel (PDSCH) and underlay, the choice of modulation schemes, and spreading factors based on the target reliability of the channels and the signal-to-noise ratio (SNR) at the receiver. We derive and present the bit error rate (BER) expressions for the PDSCH and the underlay channels. Monte Carlo simulation results show that the proposed BER expressions are highly accurate. The expression for the BER of the PDSCH is generalized for M-QAM modulation schemes, and the BER of the underlay with BPSK/QPSK is presented. Our findings provide insight into the appropriate parameterization of underlay symbol energy level and spreading factors to avoid degradation to the PDSCH channel as a function of the PDSCH modulation scheme and the received SNR.

This work investigates the use of linear reinforce-ment learning to effectively jam an OFDM-modulated victim signal. Prior work has shown improved convergence with the use of linear bandits, a variant of reinforcement learning, to jam single-carrier digital phase-amplitude modulation schemes using time-domain (TD) jamming schemes. However, communication systems today typically employ orthogonal frequency division multiplexing (OFDM) to transmit data, particularly in 4G/5G networks. This work explores the use of linear Thompson Sampling (TS) to efficiently jam OFDM-modulated signals where the jammer may select from single-carrier and OFDM jamming schemes. We show that linear TS performs better than traditional reinforcement learning (UCB-1 algorithm) in terms of maximizing the victim symbol error rate (SER). We also draw novel insights by observing the action states to which the reinforcement learning algorithm converges to.

The paper proposes a new architecture for Distributed MIMO (D-MIMO) in which the base station (BS) jointly transmits with wireless mobile nodes to serve users (UEs) within a cell for 6G communication systems. The novelty of the architecture lies in the wireless mobile nodes participating in joint D-MIMO transmission with the BS (referred to as D-MIMO nodes), which are themselves users on the network. The D-MIMO nodes establish wireless connections with the BS, are generally near the BS, and ideally benefit from higher SNR links and better connections with edge-located UEs. These D-MIMO nodes can be existing handset UEs, Unmanned Aerial Vehicles (UAVs), or Vehicular UEs. Since the D-MIMO nodes are users sharing the access channel, the proposed architecture operates in two phases. First, the BS communicates with the D-MIMO nodes to forward data for the joint transmission, and then the BS and D-MIMO nodes jointly serve the UEs through coherent D-MIMO operation. Capacity analysis of this architecture is studied based on realistic 3GPP channel models, and the paper demonstrates that despite the two-phase operation, the proposed architecture enhances the system’s capacity compared to the baseline where the BS communicates directly with the UEs.

Mobile and Vehicular Ad-hoc Networks (MANETs and VANETs) represent critical types of ad-hoc networks with diverse applications. Establishing a resilient wireless network for mobile devices in areas lacking traditional infrastructure, such as 5G base stations, presents significant potential for research fields like tactical military networks or commercial vehicular networks. In this study, leveraging the capabilities of the Common Open Research Emulator (CORE) and the Extendable Mobile Ad-hoc Network Emulator (EMANE), we present the development and evaluation of an emulated 5G sidelink (SL) network model. By customizing EMANE’s IEEE 802.11 model to adjust modulation and coding schemes based on signal quality and Packet Completion Rate (PCR) curves derived from link-level simulations, we have created a representative model of the 5G SL physical layer. Furthermore, our comparative analysis of routing protocols, specifically Optimized Link State Routing (OLSR) and Open Shortest Path First with MANET Designated Router (OSPF-MDR), underscores the critical role of protocol selection in diverse network environments. While OSPF-MDR exhibits advantages in stable networks, OLSR demonstrates greater efficiency in mode dynamic mobile scenarios. These insights not only contribute to optimizing vehicular network efficiency but also inform future advancements across various research domains.

With the recent introduction of electromagnetic meta-surfaces and reconfigurable intelligent surfaces, a paradigm shift is currently taking place in the world of wireless communications and related industries. These new technologies are of great interest as we transition from the 5thgeneration mobile network (5G-NR) towards the 6thgeneration mobile system standard (6G). In this paper, we explore the possibility of using a reconfigurable intelligent surface in order to disrupt the ability of an unintended receiver to geolocate the source of transmitted signals in a 5G-NR communication system. We investigate how the performance of the Multiple Signal Classification (MUSIC) algorithm at the unintended receiver is degraded by correlated reflected signals introduced by a reconfigurable intelligent surface in the wireless channel. We analyze the impact of the direction of arrival, delay, correlation, and strength of the reconfigurable intelligent surface signal with respect to the line-of-sight path from the transmitter to the unintended receiver. An effective method is introduced for defeating direction-finding efforts using dual sets of surface reflections. This novel method is called Geolocation-Probability Reduction using dual Reconfigurable Intelligent Surfaces (GPRIS). We also show that the efficiency of this method is highly dependent on the geometry, that is, the placement of the reconfigurable intelligent surface relative to the unintended receiver and the transmitter.

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. However, the MF approximation is not particularly effective for multi-signal detection. For this reason, we propose a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We also developed a low-complexity variation to the proposed construction in which Gaussian BP is applied to detection and expectation propagation links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection and, in general, detection in the presence of interference.

Detection of co-channel signals is important as wireless communication systems become increasingly dense. A particularly challenging case is single antenna reception in both the presence of inter-symbol interference and co-channel interference. Optimal joint maximum a posteriori probability (MAP) detection of the co-channel signals is prohibitively complex. Therefore, in this paper, we propose a factor graph-based iterative receiver which approximates joint MAP detection. In the receiver, noise is modeled with a Gaussian mixture distribution since studies have shown that the noise affecting wireless communication systems is often impulsive. Furthermore, the parameters of the factor graph model (channel and noise parameters) are iteratively estimated within the receiver. The proposed receiver is shown to the outperform state-of-the-art receiver algorithms while having a lower complexity in both Gaussian and impulsive noise. We also show significant gains from iterative parameter estimation, especially in non-Gaussian noise.

Next generation wireless communications systems are pushing the limits of both energy efficiency and spectral efficiency. This presents a challenge at the receiver when it comes to accomplishing tasks such as synchronization, channel estimation, and equalization and has motivated the development of code-aided iterative receiver algorithms in the technical literature. In this paper, we focus on the task of frame synchronization. While previous work has predominately assumed an additive white Gaussian noise channel, we develop code-aided frame synchronization algorithms for multipath channels. An iterative receiver is presented which integrates frame synchronization with iterative channel estimation, equalization, demodulation, and decoding. The receiver design includes a novel frame pre-processing stage to reduce the complexity of the proposed receiver. The complexity and performance of the proposed receiver is compared with that of a receiver based on conventional synchronization. The results demonstrate that the proposed receiver is capable of achieving a gain of up to 3 dB while increasing complexity by only 20%.

The concept of an iterative receiver has gained attention as a means of performing reliable synchronization, especially at the low signal-to-noise ratios enabled by error correction codes. In this paper, we consider joint detection of the information bits and estimation of the channel gain, carrier phase, symbol timing, frame timing, and noise power. Our particular focus is on the frame timing where we evaluate the complexity of the iterative receiver by characterizing the frame offset distribution. A method for dynamically choosing the set of frame offsets processed by the iterative receiver is presented. The receiver utilizes the expectation-maximization algorithm to perform estimation and the sum-product algorithm to perform soft demodulation and decoding. Numerical results are presented to characterize the frame offset distribution and to demonstrate the receiver's performance.