Next-generation Wireless Systems Research Articles

Parallel Implementation of K-Best Quadrature Amplitude Modulation Detection for Massive Multiple Input Multiple Output Systems

Massive MIMO (Multiple Input Multiple Output) systems impose significant processing burdens along with strict latency requirements. The combination of large-scale antenna arrays and wide bandwidth requirements for next-generation wireless systems creates an exponential increase in frontend to backend data. Balancing the processing latency and reliability is critical for baseband processing tasks such as QAM detection. While linear detection algorithms have low computational complexity, their use in Massive MIMO scenario has heavy degradation in error performance. Nonlinear detection methods such as Maximum Likelihood and Sphere Decoding have good error performance, but they suffer from high, variable, and uncontrollable computational complexity. For such cases, the K-best QAM detection algorithm can provide required control over the system performance while maintaining near-ML error performance. In this paper, hard-output, as well as soft-output K-best QAM detection, is implemented in a CPU by utilizing the multiple cores combined with vector processing. Similarly, hard-output detection in a GPU is implemented by leveraging the SIMD (Single Instruction, Multiple Data) architecture and Warp-based execution model. The processing time per bit and the energy consumption per bit are compared for CPU and GPU implementations for QAM constellation density and MIMO array size. The GPU implementation shows up to 5× processing latency per bit improvement and up to 120× energy consumption per bit improvement over the CPU implementation for typical QAM constellations such as 4, 16, and 64 QAM. GPU implementation also shows up to 125× improvement over CPU implementation in energy consumption per bit for larger MIMO configurations such as 24 × 24 and 32 × 32. Finally, the soft-output detector is combined with a LDPC (Low-Density Parity Check) decoder to obtain the FER (Frame Error Rate) performance for CPU implementation. The FER is then combined with frame processing latency to form a Goodput metric to demonstrate the latency and reliability tradeoff.

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.

Power Amplifier Modeling Framework for Front-End-Aware Next-Generation Wireless Networks

Next-generation wireless systems require increased spectral and energy efficiency in terminals. This can only be achieved if the nonlinear characteristics of the radio front-end, primarily the power amplifier (PA), are known and considered while designing algorithms or optimizing radio links. PA datasheets contain limited information, and wideband PA measurements typically require advanced equipment and software. This paper proposes a measurement and signal processing framework that enables the acquisition of nonlinear PA characteristics using generic complex sample transmitters and receivers, e.g., software-defined radio (SDR). This paper also proposes a multistage signal calibration and synchronization procedure that allows obtaining nonlinear PA characteristics that are non-distorted by other phenomena. The correctness and usefulness of the proposed framework are demonstrated by measuring three PAs, each under varying supply voltage and carrier frequency. At the same time, the high variability of the obtained nonlinear characteristic justifies the need for PA measurements to obtain awareness of nonlinear characteristics.

PEPesc: A TCP Performance Enhancing Proxy for Non-Terrestrial Networks

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6 G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transport Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PEPesc</i> which has two distinctive features. First, it features <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retransmission-free</i> loss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> smoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iperf</i> and HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

On the Interplay Between Sensing and Communications for UAV Trajectory Design

The unmanned aerial vehicles (UAVs) are envisioned as promising aerial facilities for providing advanced communication services as well as sensing functionalities in the next-generation wireless system. This paper considers a UAV-enabled integrated sensing and communications (ISAC) system, where a moving ground user (GU) is simultaneously tracked by multiple UAVs and receives the downlink communication information transmitted from the UAV. In particular, to jointly enhance the sensing and communication (S&C) performance, optimizing the UAV moving trajectory is demanded. To achieve this goal, we first harness the extended Kalman filtering (EKF) method for predicting and tracking the motion parameters of GU at each time slot, which relies on the range measurements extracted from the sensing echoes at the base station (BS). Afterward, we formulate a weighted optimization problem that addresses the design of UAV trajectories and GU-UAV association simultaneously, incorporating the consideration of real-time downlink communication rates and the Cramér-Rao bound (CRB) for GU tracking. The problem further is constrained by the maximum consumed power, maximum traveling distance, and minimum collision avoidance distance. As a step forward, to address the resultant non-convex problem, we develop an efficient iterative algorithm to obtain a near-optimal solution by utilizing the successive convex approximate (SCA) technique. Specifically, we alternately solve the GU-UAV association and the real-time trajectory design problem at each time slot. Finally, our numerical simulations illustrate that our proposed algorithm can track the GU accurately while meeting the sensing-centric and/or communication-centric requirements.

Data-Driven Participant Selection and Bandwidth Allocation for Heterogeneous Federated Edge Learning

Federated edge learning (FEEL) is a rapidly growing distributed learning technique for next-generation wireless edge systems. Smart systems across various application domains face challenges, such as data heterogeneity, limited wireless resources, and device heterogeneity, which necessitate intelligent participant selection schemes that accelerate convergence rates. Consequently, this article presents joint participant selection and bandwidth allocation schemes to address these challenges. First, we formulate an optimization problem that considers communication and computation latencies, as well as imbalanced data distribution, while meeting round deadlines and bandwidth constraints. To address the combinatorial problems of participant selection, we employ a relaxation method followed by a proposed priority selection algorithm to select near-optimal participants. The proposed algorithm initially prioritizes participants with larger datasets, effective channel states, and better CPU speeds. To address data heterogeneity, we propose a randomized deadline-controlling algorithm that diversifies updates by allowing the edge server to include different participants with fewer data samples in training rounds. The proposed algorithms offer near-optimal performance compared to the brute-force method. Experiments demonstrate that our proposed scheme accelerates the convergence rate by up to 55% under extensive non-IID settings compared to benchmarks. Furthermore, the deadline-controlling algorithm improves performance at high levels of data heterogeneity, resulting in faster FEEL systems.

Vision-Assisted Millimeter-Wave Beam Management for Next-Generation Wireless Systems: Concepts, Solutions, and Open Challenges

Beamforming techniques have been widely used in the millimeter-wave (mm-wave) bands to mitigate the path loss of mm-wave radio links as narrow straight beams by directionally concentrating the signal energy. However, traditional mm-wave beam management algorithms usually require excessive channel state information (CSI) overhead, leading to extremely high computational and communication costs. This hinders the widespread deployment of mm-wave communications. By contrast, the revolutionary vision-assisted beam management system concept employed at base stations (BSs) can select the optimal beam for the target user equipment (UE), based on location information determined by machine learning (ML) algorithms applied to visual data, without requiring channel information. In this article, we present a comprehensive framework for a vision-assisted mm-wave beam management system, its typical deployment scenarios, and the specifics of the framework. Then, some of the challenges faced by this system and their efficient solutions are discussed from the perspective of ML. Next, a new simulation platform is conceived to provide both visual and wireless data for model validation and performance evaluation. Our simulation results indicate that vision-assisted beam management is indeed attractive for next-generation wireless systems.

A universal metasurface antenna to manipulate all fundamental characteristics of electromagnetic waves

Metasurfaces have promising potential to revolutionize a variety of photonic and electronic device technologies. However, metasurfaces that can simultaneously and independently control all electromagnetics (EM) waves’ properties, including amplitude, phase, frequency, polarization, and momentum, with high integrability and programmability, are challenging and have not been successfully attempted. Here, we propose and demonstrate a microwave universal metasurface antenna (UMA) capable of dynamically, simultaneously, independently, and precisely manipulating all the constitutive properties of EM waves in a software-defined manner. Our UMA further facilitates the spatial- and time-varying wave properties, leading to more complicated waveform generation, beamforming, and direct information manipulations. In particular, the UMA can directly generate the modulated waveforms carrying digital information that can fundamentally simplify the architecture of information transmitter systems. The proposed UMA with unparalleled EM wave and information manipulation capabilities will spark a surge of applications from next-generation wireless systems, cognitive sensing, and imaging to quantum optics and quantum information science.

Empowering Next-Generation IoT WLANs Through Blockchain and 802.11ax Technologies

Blockchain emerges as a potential solution for enabling distributed management and accountability of wireless Internet of Thing (IoT) networks. In addition, blockchain offers increased IoT security, as it involves every single node around the network to verify and approve new transactions. On the other hand, IEEE 802.11ax, a recently concluded WiFi standard aimed at achieving high network efficiency, plays a key role for solving spectrum sharing and cross-technology coexistence among WiFi and 6G cellular users, one of the key challenges faced by next-generation wireless systems. In this paper, we leverage the power of blockchain and 802.11ax technologies to propose a medium access control (MAC) protocol design for future IoT wireless local area networks (WLANs), also referred to as wireless IoT-Blockchain networks setup. Our simulation results show that the proposed protocol design enhances transmission latency and achievable network throughput.

Technology Trends for Massive MIMO towards 6G.

At the dawn of the next-generation wireless systems and networks, massive multiple-input multiple-output (MIMO) in combination with leading-edge technologies, methodologies, and architectures are poised to be a cornerstone technology. Capitalizing on its successful integration and scalability within 5G and beyond, massive MIMO has proven its merits and adaptability. Notably, a series of evolutionary advancements and revolutionary trends have begun to materialize in recent years, envisioned to redefine the landscape of future 6G wireless systems and networks. In particular, the capabilities and performance of future massive MIMO systems will be amplified through the incorporation of cutting-edge technologies, structures, and strategies. These include intelligent omni-surfaces (IOSs)/intelligent reflecting surfaces (IRSs), artificial intelligence (AI), Terahertz (THz) communications, and cell-free architectures. In addition, an array of diverse applications built on the foundation of massive MIMO will continue to proliferate and thrive. These encompass wireless localization and sensing, vehicular communications, non-terrestrial communications, remote sensing, and inter-planetary communications, among others.

A Novel Performance Bound for Massive MIMO Enabled HetNets

Massive multiple-input and multiple-output (MIMO) networks with higher throughput rates, where a base station (BS) with a large-scale antenna array serves multiple users, have been widely employed in next-generation wireless communication test systems. Massive MIMO-enabled dense heterogeneous networks (HetNets) have also emerged as a promising architecture to increase the system spectrum efficiency and improve the system reliability. Massive MIMO-enabled HetNets have been successfully exploited in sustainable Internet of Thing networks (IoTs). In order to facilitate the testing and performance estimation of IoTs communication systems, this paper studies the achievable rate performance of massive MIMO and HetNets. Differing from the existing literature, we first consider an interference power model for massive MIMO-enabled HetNets. We next obtain an expression for the signal-to-interference-plus-noise ratio (SINR) by introducing an interference power. Furthermore, we derive a new closed-form lower bound expression for the achievable rate. The proposed closedform expression shows that the achievable rate is an explicit expression of the number of transmit antennas. In simulation results, the impact of the number of transmit antennas on the achievable rate performance is investigated.

Multi-frequency 5G NR millimeter-wave signal generation over analog RoF link using optical frequency combs

Optical heterodyne analog radio-over-fiber (A-RoF) links can provide a cost-effective and spectrally efficient solution for the generation and distribution of millimeter-wave/Terahertz (mm-wave/THz) carriers for the next-generation wireless systems. The use of an optical frequency comb (OFC) source in such a system can facilitate carrier generation free from frequency fluctuation. However, decorrelation between tones, due to path length difference, can lead to phase noise on the generated mm-wave carrier — limiting the performance of low subcarrier spacing multi-carrier signals specified in the 5G standard. In this work, we analyze the effect of comb tones decorrelation due to path length difference on the performance of 5G compatible 244 kHz subcarrier spacing 59 GHz OFDM signal over an optical heterodyne A-RoF link using a gain switched laser (GSL) OFC source. Furthermore, we demonstrate the ability of the OFC-based A-RoF optical/mm-wave system to support flexible and interoperable mm-wave functionality through signal generation at 27 GHz, 43 GHz and 59 GHz using different pairs of GSL OFC tones for remote heterodyning. The capability of multi-frequency 5G NR compatible mm-wave signal generation and potential for photonic integration make the demonstrated GSL OFC based optical heterodyne A-RoF system a suitable candidate for deployment in the next generation wireless systems.

Real-Time CRLB Based Antenna Selection in Planar Antenna Arrays

Estimation of User Terminals’ (UTs’) Angle of Arrival (AoA) plays a significant role in the next generation of wireless systems. Due to high demands, energy efficiency concerns, and scarcity of available resources, it is pivotal how these resources are used. Installed antennas and their corresponding hardware at the Base Station (BS) are of these resources. In this paper, we address the problem of antenna selection to minimize Cramer-Rao Lower Bound (CRLB) of a planar antenna array when fewer antennas than total available antennas have to be used for a UT. First, the optimal antenna selection strategy to minimize the expected CRLB is proposed. Then, using this strategy as a preliminary step, we present a two-stage greedy antenna selection method whose goal is to minimize the instantaneous CRLB. The optimal start point of the greedy algorithm is presented alongside some methods to reduce the algorithm’s computational complexity. Numerical results confirm the accuracy of proposed solutions. They demonstrate that the proposed antenna selection method only requires a small proportion of the total available antennas to accomplish a significant amount of the total performance, enhancing hardware utilization efficiency. Also, it is shown that the presented algorithm has a high error tolerance.

Predistorter for Nonlinear Distortion Mitigation in GFDM Systems

Next-generation wireless systems use the non-orthogonal modulation technique known as generalized frequency division multiplexing (GFDM). The nonlinearity of the high-power amplifier (HPA) greatly affects the performance of the wireless system. Without phase distortion and only amplitude distortion in the GFDM system using HPA Model Rapp. The effects of nonlinear distortion that occur involve distortion of the amplitude, signal constellation distribution, and spectral distribution. There are two contributions to this paper. First, the impact of HPA Model Rapp's nonlinear distortion on the GFDM system in the AWGN (additive white Gaussian noise) and Rayleigh channels was investigated. The simplicity of this model is the reason it was picked and has an AM/AM (amplitude modulation/amplitude modulation) curve. Second, the linearization of HPA with the use of a predistorter method in GFDM systems has been proposed. The nonlinear distortion impact of HPA can be mitigated by this predistorter. This technique is implemented in the transmitter section. The system performance investigated is determined by a bit error rate (BER) analysis, a constellation diagram, and a spectrum analysis. HPA generates a 55 dB rise in out-of-band (OOB) in the signal spectrum. The HPA effect was successfully compensated for by a 55 dB decrease in the OOB value of the predistorter system.

Toward RIS-Enhanced Integrated Terrestrial/Non-Terrestrial Connectivity in 6G

The next generation of wireless systems will take the concept of communications and networking to another level through the seamless integration of terrestrial, aerial, satellite, maritime and underwater communication systems. Reconfigurable intelligent surface (RIS) is an innovative technology which, with its singular features and functionalities, can expedite the realization of this everywhere connectivity. Motivated by the unparalleled properties of this innovatory technology, this article provides a comprehensive discussion on how RIS can contribute to the actualization and proper functioning of future integrated terrestrial/non-terrestrial (INTENT) networks. As a case study, we explore the integration of RIS into non-orthogonal multiple access (NOMA)-based satellite communication networks and demonstrate the performance enhancement achieved by the inclusion of RIS via numerical simulations. Promising directions for future research in this area are set forth at the end of this article.

Guest Editorial Rate Splitting for Future Wireless Networks

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers.

Efficient Massive MIMO Detection for M-QAM Symbols

Massive multiple-input multiple-output (MIMO) systems significantly outperform small-scale MIMO systems in terms of data rate, making them an enabling technology for next-generation wireless systems. However, the increased number of antennas increases the computational difficulty of data detection, necessitating more efficient detection techniques. This paper presents a detector based on joint deregularized and box-constrained dichotomous coordinate descent (BOXDCD) with iterations for rectangular m-ary quadrature amplitude modulation (M-QAM) symbols. Deregularization maximized the energy of the solution. With the box-constraint, the deregularization forces the solution to be close to the rectangular boundary set. The numerical results demonstrate that the proposed detector achieves a considerable performance gain compared to existing detection algorithms. The performance advantage increases with the system size and signal-to-noise ratio.

Leveraging Deep Neural Networks for Massive MIMO Data Detection

Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems. Utilizing large antenna arrays at base-stations, massive MIMO enables substantial spatial multiplexing gains by simultaneously serving a large number of users. However, the complexity in massive MIMO signal processing (e.g., data detection) increases rapidly with the number of users, making conventional hand-engineered algorithms less computationally efficient. Lowcomplexity massive MIMO detection algorithms, especially those inspired or aided by deep learning, have emerged as a promising solution. While there exist many MIMO detection algorithms, the aim of this magazine paper is to provide insight into how to leverage deep neural networks (DNN) for massive MIMO detection. We review recent developments in DNN-based MIMO detection that incorporate the domain knowledge of established MIMO detection algorithms with the learning capability of DNNs. We then present a comparison of the key numerical performance metrics of these works. We conclude by describing future research areas and applications of DNNs in massive MIMO receivers.

Sensing Integrated DFT-Spread OFDM Waveform and Deep Learning-Powered Receiver Design for Terahertz Integrated Sensing and Communication Systems

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the 10°3 BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise and multi-target estimation.

Reconfigurable Intelligent Surface-Aided Orthogonal Time Frequency Space and Its Deep Learning-Based Signal Detection

Orthogonal time frequency space (OTFS) has emerged as a promising modulation for next-generation wireless systems. This two-dimensional (2D) modulation has the inherent capability of providing uninterrupted connectivity and improved performance in a high-mobility doubly-selective channel by mapping the information symbols to the delay-Doppler (DD) domain. In order to improve the directivity of OTFS signals, this paper considers a reconfigurable intelligent surface (RIS)-aided OTFS transmission for a high-speed environment. While OTFS mitigates the dispersion due to the highly mobile wireless channels by mapping the transmit symbols to the DD domain, RIS improves the directivity by appropriately shifting the phase of the signal in non-line-of-sight (NLOS) communication channels. We provide a concrete matrix-based mathematical model of the RIS-aided OTFS communication system. Capitalizing on the simple matrix multiplication-based model, a number of detectors can be used. These include the usual zero-forcing (ZF) and the minimum mean-squared error (MMSE) linear detectors, and a low-complexity message passing algorithm (MPA)-assisted detector. We evaluate the performance of these detectors for RIS-based OTFS systems. A deep learning (DL)-based signal detector is also proposed for the RIS-aided OTFS system. A significant improvement in bit-error-rate (BER) performance is achieved using the system considered, while the RIS-induced computational burden is not high. Furthermore, in NLOS communication, our system can ensure seamless connectivity with a reduced number of base stations (BS).