Fundamental Trade-Offs in Monostatic ISAC: A Holistic Investigation Towards 6G

Integrated sensing and communication (ISAC) emerges as a cornerstone technology for the upcoming 6G era, seamlessly incorporating sensing functionality into wireless networks as an inherent capability. This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM ISAC systems-namely, the time-frequency domain trade-off and the spatial domain trade-off. To ensure robust sensing across diverse modulation orders in the time-frequency domain, including high-order QAM, we design a linear minimum mean-squared-error (LMMSE) estimator tailored for sensing with known, randomly generated signals of varying amplitude. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate, time-non-overlapping beams for sensing and communications. Simulations demonstrate superior performance of the LMMSE estimator in detecting weak targets in the presence of strong ones under high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines. Key insights into these trade-offs under various modulation schemes, SNR conditions, target radar cross section (RCS) levels and transmission strategies highlight the merits of the proposed LMMSE approach

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, as well as a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network which should be extremely intelligent and capable of concurrently supporting hyper-fast, ultra-reliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning will play an instrumental role for advanced vehicular communication and networking. To this end, we provide an overview on the recent advances of machine learning in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies

Receiver Side Signal Processing for Nonlinear Distortion Compensation in 5G AND Beyond

Trading between transmit waveform quality and power efficiency is one of the most challenging issues in radio transmitter implementation. To this end, digital predistortion is the de-facto solution for mitigating power amplifier (PA) nonlinear distortion in cellular base-stations due to its high flexibility and good linearization performance. Theoretically, it is convenient to describe predistorter (PD) transfer function as the mathematical inverse of the PA transfer function, and PD modeling is often performed through parametric methods. Thus, an additional feedback loop is required in the system for PD model parameter estimation. PA is an analog device and DPD is a part of digital front-end, implying that PA output signal is needed to be downconverted to baseband and sampled in the parameter estimation path. Consequently, it is required to employ additional components in the feedback loop such as attenuator, downconverter, and analog-to-digital converter (ADC). In order to be able to capture higher order nonlinearities, it is necessary to perform upsampling operation, which implies that in addition to digital-to-analog converters (DACs) in the forward loop, the components in the feedback loop should support higher bandwidths than the original transmission bandwidth. Additionally, to have a good linearization performance, a high resolution ADC is required. Having an ADC/DAC that supports wide bandwidth and has high resolution is directly increasing the material cost and power consumption. When future millimeter-wave (mmWave) systems are considered, adopting DPD becomes even more complex and costly due to wider waveform bandwidths and employing active antenna arrays. Alternative to DPD, receiver based approaches, referred to as digital post-distortion (DPoD), can be utilized to mitigate the nonlinear effects of transmitter PA. Naturally, receiver side techniques do not provide any improvement in terms of out-of-band (OOB) emission issues, rather they aim to improve received signal error vector magnitude (EVM). As the radiated power at mmWave is typically EVM limited and OOB emission requirements are relaxed compared to sub-6 GHz band, DPoD can offer means for improved network energy-efficiency. Several iterative DPoD methods are proposed in the literature such as power amplifier nonlinearity cancellation (PANC), and reconstruction of distorted signals (RODS). In this thesis, we present a non-iterative computationally efficient receiver side nonlinearity mitigation technique, referred to as digital post-inverse (DPoI), along with the parameter estimation approach targeting existing 5G NR standard-compliant reference signal. The receiver EVM performance of presented approach is analyzed by using computer simulations. It is seen that DPoI can reach similar or improved performance compared to the iterative PANC method, which is chosen as a reference DPoD method. Moreover, it is shown that both DPoD methods overperform ideally linearized transmitter PA under strong nonlinear conditions, which allows higher power efficiency when receiver side techniques are employed

Advanced receivers for distributed cooperation in mobile ad hoc networks

Mobile ad hoc networks (MANETs) are rapidly deployable wireless communications systems, operating with minimal coordination in order to avoid spectral efficiency losses caused by overhead. Cooperative transmission schemes are attractive for MANETs, but the distributed nature of such protocols comes with an increased level of interference, whose impact is further amplified by the need to push the limits of energy and spectral efficiency. Hence, the impact of interference has to be mitigated through with the use PHY layer signal processing algorithms with reasonable computational complexity. Recent advances in iterative digital receiver design techniques exploit approximate Bayesian inference and derivative message passing techniques to improve the capabilities of well-established turbo detectors. In particular, expectation propagation (EP) is a flexible technique which offers attractive complexity-performance trade-offs in situations where conventional belief propagation is limited by computational complexity. Moreover, thanks to emerging techniques in deep learning, such iterative structures are cast into deep detection networks, where learning the algorithmic hyper-parameters further improves receiver performance. In this thesis, EP-based finite-impulse response decision feedback equalizers are designed, and they achieve significant improvements, especially in high spectral efficiency applications, over more conventional turbo-equalization techniques, while having the advantage of being asymptotically predictable. A framework for designing frequency-domain EP-based receivers is proposed, in order to obtain detection architectures with low computational complexity. This framework is theoretically and numerically analysed with a focus on channel equalization, and then it is also extended to handle detection for time-varying channels and multiple-antenna systems. The design of multiple-user detectors and the impact of channel estimation are also explored to understand the capabilities and limits of this framework. Finally, a finite-length performance prediction method is presented for carrying out link abstraction for the EP-based frequency domain equalizer. The impact of accurate physical layer modelling is evaluated in the context of cooperative broadcasting in tactical MANETs, thanks to a flexible MAC-level simulato