This paper presents a hybrid automatic repeat request (HARQ) scheme based on multi-rate coupled low-density parity-check (LDPC) codes, which can be efficiently implemented using existing 5G New Radio (NR) LDPC matrices. The HARQ scheme enables coupling among parity-check matrix blocks with different code rates, making it well-suited for adaptive modulation and coding (AMC) and ultra-reliable low-latency communication (URLLC) applications. To enhance reliability and goodput, the HARQ mechanism can be integrated with incremental redundancy (IR). Simulation results demonstrate that HARQ-IR with multi-rate coupled LDPC codes achieves superior performance, providing up to 10% gain in goodput over conventional HARQ-IR. Additionally, the proposed scheme outperforms single-rate coupled HARQ-IR by approximately 4% goodput gain, and the optimized multi-rate coupling results in a 0.2 dB reduction in the required signal-to-noise ratio (SNR) at a bit error rate (BER) of $10^{-7}$ . This approach can be readily extended to support the coupling of different LDPC codes with varying information and codeword lengths. Moreover, latency, memory usage, and computational complexity are analyzed, showing that multi-rate coupling improves successful decoding and reduces the average age of information (AoI) at high SNR, with only a limited increase in complexity.

In this paper, we investigate the effectiveness of a newly proposed frame superposition Hybrid Automatic Repeat reQuest (HARQ) scheme, which adopts different variants of spatially coupled low-density parity-check (LDPC) codes. The construction of the method with the use of 5 G new radio (NR) LDPC codes is explained in detail. Bit error rate (BER), frame error rate (FER), and the projected data goodput in additive white Gaussian noise (AWGN) and Rayleigh block fading channels are investigated. The HARQ scheme is based on frame combination by using spatially coupled LDPC codes. It outperforms all classical variants of HARQ and can, hence, be seen as a potential successor of the classical methods. The main cost is the need for more complicated LDPC decoding, whereby the decoder needs to support the decoding of two or even more blocks at the same time.

Indoor localization has attracted considerable attention in recent years, driven by the growth of Internet of Things (IoT) applications. The transition of wireless communication systems toward higher frequency bands has further enabled higher accuracy user positioning. Various positioning methods have emerged over the years, supporting different levels of accuracy. In this work, we leverage measurements from multiple wireless communication technologies to address a dynamic positioning scenarios. We employ a Particle Filter (PF) to combine Time-of-Arrival (ToA) estimates from Sub-6 GHz and mmWave technology to enhance the localization accuracy in an indoor environment. Additionally, we evaluate the performance of the proposed algorithm by examining the positioning error statistics in different scenarios. Simulation results indicate that the proposed hybrid algorithm improves the mean localization and tracking error by approximately 42.8% and 17.8%, in comparison to the individual use of Sub-6 GHz and mmWave positioning technologies, respectively. The combination of these estimates results in a mean absolute positioning error better than 10 cm.

With new technological advancements in wireless communication, reflected in 3GPP (Third Generation Partnership Project) cellular or wireless local area network (WLAN) standards, increasing data rate is a prominent aspect. Consequently, some backhaul networks require a data rate in the order of 100 gigabits per second. The D-Band, which can provide ample bandwidth, potentially can support this data rate. This paper analyzes the line-of-sight D-Band multiple input multiple output (MIMO) channel for different antenna arrangements, which are best suited for achieving the nearly orthogonal channels for exploiting MIMO full-rank spatial multiplexing. This allows for flexibility in antenna arrangement design in hardware, which is not limited to uniform linear or rectangular arrays. We have also addressed the impact of the transmitter array orientation and provided an algorithm to compensate for the misalignment by estimating the orientation and mechanically re-aligning the receiver array in that direction. Furthermore, a general simplified communication link is numerically simulated, showing that tilt significantly impacts equalizer performance and overall spectral efficiency, and the compensation algorithm is able to restore system performance effectively. Additionally, by comparing different antenna arrangements, it is found that a triangular antenna arrangement has exceptional performance over a range of antenna spacing. Unlike other antenna arrangements, that require optimum antenna spacing, it does not require optimum but tolerates a range of deviations with little practical impact on system performance.

The rapid growth of high-precision location-based services (LBSs), has driven indoor localization as a key research area in recent years. The transition towards higher frequency bands, such as millimeter wave (mmWave), enables high-precision localization for future wireless technologies. By deploying a diverse set of positioning techniques, we can further mitigate the limitations of single measurement position estimation, resulting in a more robust user localization and tracking system. This also enhances the design of positioning systems that support different levels of accuracy. In this work, we use a particle filter to combine the time difference of arrival (TDoA) and angle of arrival (AoA) measurements in the mmWave band for a more accurate position estimate. In addition, we evaluate the performance of the proposed algorithm by examining the positioning performance with that of the individual measurements using the least square (LS) method and hybrid measurements using the weighted least square (WLS) method. The simulation results show that the proposed hybrid particle filter (PF)-based algorithm improves the positioning accuracy by 23.9% compared to that of the hybrid WLS-based method. The combination of the hybrid TDoA and AoA measurements results in a mean positioning error of 17.4 cm.

Today, machine learning has a crucial role in wireless communications, notably in 5G and 6G. It contributes significantly for increasing network capacity, improving user experience, and enhancing network reliability. Among machine learning techniques, reinforcement learning is vital due to its suitability for many real-world scenarios. It enables agents to learn from the environment with zero-knowledge and make rational decisions. Thus, in this article, we aim to explore the role of classical reinforcement learning in predicting optimal beam angles within urban environments. The goal is to minimize interference between antennas by finding optimal beamforming angles using ray tracing techniques. We examine various classic reinforcement learning methods in an urban scenario, focusing on maximizing total channel capacity. Initially, we identify the optimal beamforming angles for maximizing channel capacity with four antennas. After validating the learning methods and achieving over 99% accuracy, we proceeded to utilize them in a larger scenario. In the first phase, these methods and their accuracy are validated based on the results of the exhaustive search for a small number of nodes. In the second phase, we predict optimal antenna beam angles for scenarios with an increased number of transmitters and receivers for a realistic urban environment situated in the north-eastern part of Berlin.

This work is focused on enhancing the object localization and room reconstruction using an Integrating Sensing and Communication (ISAC) system in the millimeter-wave range. The applications of ISAC can improve the generation or accuracy of digital twins and assist in fast switching between steering vectors for robust communication. In this paper, we utilized the antenna pattern knowledge to scan objects in the areas that are not covered by intersections of the main lobe beams. We also utilize the side lobe and main lobe intersection. In order to evaluate the suggested approach, we created two different room models with the transmitter and the receiver located apart from each other – similar to a bistatic RADAR set-up. Both nodes are equipped with phased array antennas that perform beam steering. The digitized beam patterns are used for all simulations. The propagated signal over channel is obtained via ray-tracing simulations and cross-correlated to obtain the channel impulse response (CIR). Based on the CIR, we applied the LASSO technique. The obtained results are visualized and evaluated. This paper presents the approach of extending the sensing area for object localization from the main intersection of the beam steering angles to the side and main lobe intersection area using the complete antenna patterns, including the side lobes.

As Artificial Intelligence (AI) and Machine Learning (ML) technologies continue to evolve, their integration into 5G and 6G networks has become critical for improving performance and efficiency. These technologies enhance wireless communication by leveraging deep neural networks and data-driven methods to optimize resource allocation, signal detection, and channel coding. They also address the growing need to reduce energy consumption in next-generation networks. Deep Q-Networks (DQN) play a key role in this transformation by enabling dynamic resource management, adaptive beamforming, and efficient network slicing. The use of DQN can improve spectrum utilization, power consumption, and beamforming in massive MIMO systems, supporting demands like eMBB and URLLC. In 6G, it is considered for optimizing resource allocation, dynamic beamforming, RIS control for Terahertz communication, and complex network slicing. This article focuses on maximizing channel capacity by deploying DQN to identify optimal beamforming angles and transmit power values for multiple transmitters and receivers. The model determines the best steering vectors and power levels to achieve maximum channel capacity while minimizing energy consumption and mutual interference. Its performance is compared to conventional optimization methods, demonstrating its effectiveness in enhancing the efficiency and reliability of 5G and 6G networks.

Utilization of the Delay-Doppler (DD) domain enables the recently proposed two-dimensional (2D) Orthogonal Time Frequency Space (OTFS) waveform to provide consistent performance under high mobility communication systems. OTFS outperforms existing standard waveforms under such time-frequency selective channels, making it a waveform candidate for future wireless communication systems. In this work, we present an implementation of an OTFS waveform-based system on a Universal Software Radio Peripheral (USRP) X310 Software Defined Radio (SDR). This system is tested in a realistic indoor office environment at a 5 GHz carrier frequency band with 150 MHz bandwidth. The resulting constellation diagrams were observed for BPSK, 4-QAM, and 16-QAM modulated OTFS symbols. Furthermore, for 4-QAM symbols, several communication metrics, such as Bit Error Rate (BER) and Error Vector Magnitude (EVM), were evaluated against different gains at the USRP. The resulting constellation diagrams, BER, and EVM graphs show a successful implementation of our OTFS system.

In this work, the performance evaluation of a prototype 240 GHz 2x2 Line-of-Sight MIMO link is reported, as a proof-of-concept for high throughput point-to-point wireless backhaul. The LoS MIMO system is implemented in a hardware-in-the-loop setup and demonstrated at a range of 3.5 m in an indoor exhibition hall. It is based on 130-nm SiGe BiCMOS RF front-ends operating at a carrier frequency of 240 GHz, in combination with dielectric lenses. Relying on optimal antenna arrangement, a highly orthogonal LoS MIMO channel with a mean estimated condition number of 1.14 is obtained. By transmitting two spatially multiplexed QPSK-modulated data streams at 25 GHz modulation bandwidth, an error-free uncoded throughput of 100 Gb/s is demonstrated. Since the system is still in the preliminary prototyping phase, ongoing development focuses on increasing the antenna gain and modulation bandwidth, to further enhance its performance for practical application scenarios.

Low-density parity-check (LDPC) codes are widely used in modern communication systems due to their near-capacity error correction performance. This paper presents a practical FPGA implementation of a universal hardware coprocessor for LDPC encoding and decoding, focusing on a system-level architecture, achievable data rate, latency measurements, and hardware resource utilization. The LDPC coding is realized by the Xilinx hardware macros available in the Xilinx RF-SoC FPGAs. We explore various design simplifications, including core combining, memory management, and data scheduling, to achieve high throughput while maintaining the lowest implementation complexity. The proposed architecture is implemented on an FPGA platform and is equipped with 10Gb/s Ethernet interfaces, demonstrating real-time decoding capabilities and improved performance compared to software-based approaches. Experimental results validate the design, showcasing its applicability in high-speed communication systems. This work can serve as a reference for engineers and researchers aiming to deploy LDPC decoding in FPGA-based environments by reusing the existing Intellectual Property (IP), which is freely available in Xilinx SoC.

In this paper, we propose an improved Hybrid Automatic Repeat reQuest (HARQ) scheme based on cross-packet superposition retransmission by employing dedicated low-density parity-check (LDPC) matrices. Simulation results demonstrate that the scheme improves Bit Error Rate (BER) and Packet Error Rate (PER) performance up to 0.4 dB at the cost of increased packet handling complexity. The key element of our system is the combination of the systematic part of a codeword with the parity part of another codeword without any reductions in the code rate. Thus, to some extent, retransmission can be performed without any need to send additional parity bits.

Optimization methods are crucial for improving the performance of 5G and 6G networks, primarily by enhancing key aspects such as beamforming and transmission power. These methods enable the efficient use of the available spectrum, leading to increased data rates, lower latency, and improved reliability, all of which are fundamental for supporting the applications envisioned for these networks. By optimizing beamforming, networks can achieve more precise directional signal transmission, reducing interference and increasing capacity. Similarly, power optimization ensures that the transmission power is used judiciously, extending battery life in user devices and reducing overall energy consumption. These improvements are essential for meeting the ever-growing demand for higher data throughput and better quality of service in modern wireless communication systems. This article explores how optimization methods can be applied to finetune transmission power of beamforming antennas with various transmit and receive patterns, ultimately maximizing overall channel capacity. We employed these optimization techniques to identify the highest achievable total channel capacity by identifying the optimal beamforming steering vectors for both transmitters and receivers. Among the seven optimization methods evaluated, Genetic Algorithm, Hill Climbing, and Shotgun Hill Climbing consistently demonstrated superior performance achieving up to 99.7% accuracy which making them the most effective approaches when considering both accuracy and execution time. The goal of this approach is to improve the efficiency and performance of 5G and 6G networks while also minimizing energy consumption.

Localization will be an essential requirement for various 6 th generation (6G) communication system applications. Integrated Sensing and Communication (ISAC) is seen as a key enabling technology that can provide the capability of combined communication and localization. Reliable ISAC in a high-mobility environment can be challenging and existing communication waveforms suffer from severe degradation due to significant Doppler effect. The Delay-Doppler (DD) domain can be commonly seen in the results of Radio Detection and Ranging (RADAR) systems, which is the baseline for Orthogonal Time Frequency Space (OTFS) modulation. Due to the information encoding in DD domain, OTFS shows significant resilience against doubly-selective channels. In this work, we report the sensing functionality performance of our complete sub-6GHz ISAC system based on the OTFS waveform, implemented on a USRP X310 Software Defined Radio (SDR). The sensing capability of the system is experimentally verified in both an anechoic chamber and in an office scenario for single and for multitargets.

The natural relation of the Delay-Doppler (DD) domain to the physical radio environment allows longer channel estimation validity. The Orthogonal Time Frequency Space (OTFS) modulation scheme based on the DD domain provides consistent performance even under high-mobility radio environments. Similar to other multi-carrier schemes, OTFS can also suffer from various synchronization challenges. In this paper, the impact of a practical synchronization challenge, viz., Sampling Time Offset (STO) on an OTFS system implemented on a Software Defined Radio (SDR) platform is investigated. The impact of STO in the Time and Frequency domains is first analyzed, and then time-based and frequency-based solutions are proposed to mitigate it for two schemes of OTFS. The computational complexity is also carried out for each method. The STO problem and all solutions for an OTFS system are experimentally evaluated using a USRP X310 SDR in an office environment. The experimental results show similar Error Vector Magnitude (EVM) results when compared to an existing STO correction scheme for a practical OTFS system. From the computational complexity perspective, the proposed methods provide STO corrections with a similar performance improvement at a much lower complexity.

RADAR-based gesture recognition has attracted a lot of attention in recent years. A large number of studies have been conducted on single RADAR-based gesture recognition. However, there is still a lot of space to explore multi-RADAR-based gesture recognition. Compared to a single-RADAR scenario, a multi-RADAR scenario can provide higher stability and recognition performance. In the context of the multi-RADAR scenario, an efficient, high-performance algorithm is proposed in this paper for the purpose of extracting features from RADAR data and classifying gestures using a simple machine-learning model. The experimental results demonstrate a high average recognition rate of 97.78% on the test set with the proposed algorithm and model, and a significant reduction in runtime compared to the reference work. This can be a very promising application in joint communication and sensing (JCAS) system.

The performance of practical millimetre wave (mmWave) communication systems is affected by different hardware impairments. This can significantly impact the channel reciprocity-based physical layer security (PLS) methods. This paper studies the impact of in-and quadrature-phase (IQ) imbalance on the physical layer secure key generation algorithm performance. Numerical analysis is performed in MATLAB by modelling and simulating the IQ imbalance, which is then applied to the 60 GHz channel used to generate secret keys. The study's outcome shows that IQ imbalance increases bit mismatches up to an average of 5% for an amplitude imbalance of 1.4 dB and a phase imbalance of 7°. However, additional iterations of parity bit sequence exchanges can recover these in the reconciliation phase. As a result, the information is slightly more exposed to an eavesdropper.

There is an increasing importance to reduce overall energy usage, especially in the realm of 5th and 6th Generation (5G, 6G) networks. To address this requirement, cutting-edge technologies such as Machine Learning (ML) and Artificial Intelligence (AI) have been integrated into the operation of 5G networks. As AI continues to progress rapidly and ML emerges as a significant subset, the integration of these innovations into 5G/6G infrastructures has become a significant area of exploration and study. This article explores how ML methods can optimize transmission power values for antennas with different transmit/receive patterns, ultimately enhancing the overall channel capacity. Essentially, we utilized ML techniques to determine the maximum channel capacity achievable by identifying the best both, the best beamforming steering vectors (for both transmitters and receivers) and determine the optimal transmit power values for transmitters. This approach aims to enhance the efficiency and performance of 5G/6G networks while minimizing energy consumption.