Carrier phase positioning (CPP) is widely used in satellite system applications, enabling centimeter-level localization accuracy. Recently, CPP is gaining attraction also in terrestrial mobile networks, particularly in 5G New Radio (NR) evolution toward 6G. One key challenge is to resolve the so-called integer ambiguity problem, as carrier phase provides only relative position information. This work introduces and studies a multi-band CPP scenario with intra- and inter-band carrier aggregation (CA) opportunities across FR1, mmWave-FR2, and emerging 6G FR3 bands. Specifically, we derive multi-band CPP performance bounds, showcasing the superiority of multi-band CPP for high-precision localization in current and future mobile networks. A wide collection of numerical results is provided, covering the impacts of the available carrier bandwidth, number of aggregated carriers, transmit power, and the number of network nodes. The offered results highlight that only two carriers need to be aggregated to substantially facilitate resolving the integer ambiguity problem.

The rapid increase in the number of Internet of Things (IoT) devices has led to the development of advanced networking technologies such as bluetooth low energy (BLE) standardized by special interest group (SIG) mesh and Wirepas mesh networks. Each of these technologies offers unique features and capabilities. BLE is a widely used short-range technology that has made a significant impact on the IoT paradigm development thanks to its simplicity, low power consumption, robustness, and low cost. In contrast, Wirepas mesh is a decentralized wireless communication protocol, meaning that each node in the network selects its own role while maintaining and optimizing the connection automatically based on its environment. This paper provides a comparative analysis of BLE SIG mesh and Wirepas mesh, focusing on their security, scalability, power, and memory efficiency, as well as application suitability in diverse ad hoc IoT environments. The study highlights that while BLE SIG mesh benefits from easy adoption, low power consumption, and ease of integration into the consumer IoT ecosystem, Wirepas mesh excels in high-density, large-scale industrial applications due to its robust management and decentralized control. Security is realized in BLE SIG mesh with standard authentication and encryption, whereas Wirepas mesh utilizes deployment-configurable security mechanisms to handle diverse network topologies. This comparative review enables IoT industrialists and researchers to select appropriate mesh technologies based on their specific requirements and deployment constraints.

Recent advances in CMOS technology have enabled the implementation of high-performance and energy-efficient digital-intensive radio transceivers for fifth-generation (5G) and beyond (6G) wireless communication systems. Among the various transmitter architectures, the outphasing transmitter with constant-amplitude modulation, which allows the use of highly energy-efficient nonlinear power amplifiers, has garnered significant attention since it easily lends itself to a digital-intensive implementation, thus fully exploiting the benefits of scaled CMOS technologies. However, an outphasing transmitter with a delay-based phase modulator suffers from performance degradation due to mismatch-induced static nonlinearity. This paper presents a linearization algorithm that minimizes mismatch-induced static nonlinearity by reordering the unary-weighted delay elements. The effectiveness of this algorithm is demonstrated through simulations and its application to measured delay characteristics from a 22-nm FDSOI CMOS transmitter prototype in a system simulation. The proposed reordering algorithm results in a 7.94% points improvement from 10.63% in EVM and a 7.76dB enhancement from 25dB in ACLR for a 5G NR 64-QAM OFDM waveform with a 200MHz bandwidth.

This article introduces a digital conversion and linearization unit (DCLU) that performs signal conversion and power amplifier (PA) linearization when the digital-to-analog converter (DAC) is constrained by limited bit resolution. The proposed structure applies an error-and-distortion-feedback (EDF) processing principle that integrates re-quantization, noise shaping, and digital predistortion (DPD) within a single digital unit and avoids explicit inverse modeling. The embedded PA forward description uses a polar look-up table (PLUT) representation of the PA static AM/AM and AM/PM characteristics and a linear filter that captures the measured dynamic response. More advanced approaches are also described in terms of incorporating more elaborate forward models within the DCLU, together with a parallel multi-channel realization that relaxes internal clock-rate requirements through time-interleaved processing. The concept is validated through two RF measurement experiments using a 5G New Radio (NR) transmit waveform at 3.5 GHz and 3.6 GHz (NR band n78). The first experiment uses a broadband Gallium-Nitride (GaN) HMC1114 PA to demonstrate the proof of concept and quantify system behavior under reduced bit resolutions. The second experiment uses the Qorvo QPA3503 GaN Doherty PA to evaluate the approach for an amplifier type that is typically more challenging to linearize. The measured results show that the proposed approach maintains good linearization capability under reduced bit resolutions and, in the reported cases, provides improved transmit waveform quality relative to conventional reference DPD schemes, particularly at lower bit widths.

In this paper, we propose a hybrid precoding/combining framework for communication-centric integrated sensing and full-duplex (FD) communication operating at mmWave bands. The designed precoders and combiners enable multiuser (MU) FD communication while simultaneously supporting monostatic sensing in a frequency-selective setting. The joint design of precoders and combiners involves the mitigation of self-interference (SI) caused by simultaneous transmission and reception at the FD base station (BS). Additionally, MU interference needs to be handled by the precoder/combiner design. The resulting optimization problem involves non-convex constraints since hybrid analog/digital architectures utilize networks of phase shifters. To solve the proposed problem, we separate the optimization of each precoder/combiner, and design each one of them while fixing the others. The precoders at the FD BS are designed by reformulating the communication and sensing constraints as signal-to-leakage-plus-noise ratio (SLNR) maximization problems that consider SI and MU interference as leakage. Furthermore, we design the frequency-flat analog combiner such that the residual SI at the FD BS is minimized under communication and sensing gain constraints. Finally, we design an interference-aware digital combining stage that separates MU signals and target reflections. The communication performance and sensing results show that the proposed framework efficiently supports both functionalities simultaneously.

Satellite networks have become central to the evolution of modern communications systems due to their potential for extensive global coverage. However, high latency in satellite systems poses critical challenges for delay-sensitive applications, underscoring the need for performance metrics that characterize ultra-reliable low-latency communications. To address these challenges, data-oriented approach, which is already widely studied in terrestrial networks, provides a fresh perspective by evaluating the transmission performance where it prioritizes both reliability and latency. This work introduces the data-oriented approach to uplink hybrid satellite-terrestrial networks (HSTNs), focusing on non-orthogonal multiple access (NOMA)-assisted semi-grant-free (SGF) transmission where terrestrial relays support the transmission between the satellite and users. Specifically, a novel grant-free user (GFU) admission protocol based on distributed contention control, the corresponding power allocation scheme for GFUs, and the relay selection procedure presented following the data-oriented approach. Then, the maximum number of GFUs that can be admitted under given data-oriented design requirements is determined. The analytical results are verified by extensive Monte-Carlo simulations, while a wide body of numerical results are also offered to understand and demonstrate the impacts of different system parameters. The proposed analytical framework offers useful insights toward the design of practical HSTNs, particularly in the context of delay-sensitive applications, by revealing the relationship between the amount of information data, the power consumption, and the satellite distance. The results demonstrate that the proposed scheme improves the DOR performance compared to conventional grant-free access and frequency division multiple access methods by dynamically selecting GFUs and allocating transmit power based on channel conditions. It is also shown that more GFUs can be admitted, especially with larger bandwidths and/or relaxed threshold settings.

In addition to satellite systems, carrier phase positioning (CPP) is gaining attraction also in terrestrial mobile networks, particularly in 5G New Radio (NR) evolution toward 6G. One key challenge is to resolve the so-called integer ambiguity problem, as the carrier phase provides only relative position information. This work introduces and studies a multi-band CPP scenario with intra- and inter-band carrier aggregation (CA) opportunities across FR1, mmWave-FR2, and emerging 6G FR3 bands. Specifically, we derive multi-band CPP performance bounds, showcasing the superiority of multi-band CPP for high-precision localization in current and future mobile networks, while noting also practical imperfections such as clock offsets between the user equipment (UE) and the network as well as mutual clock imperfections between the network nodes. A wide collection of numerical results is provided, covering the impacts of the available carrier bandwidth, number of aggregated carriers, transmit power, and the number of network nodes or base stations. The offered results highlight that only two carriers suffice to substantially facilitate resolving the integer ambiguity problem while also largely enhancing the robustness of positioning against imperfections imposed by the network-side clocks and multi-path propagation. In addition, we also propose a two-stage practical estimator framework that achieves the derived bounds under all realistic bandwidth and transmit power conditions. Furthermore, we show that with an additional search-based refinement step, the proposed estimator becomes particularly suitable for narrowband Internet of Things (IoT) applications operating efficiently even under narrow carrier bandwidths. Finally, both the derived bounds and the proposed estimators are extended to scenarios where the bands assigned to each base station are nonuniform or fully disjoint, enhancing the practical deployment flexibility.

In this paper, a decentralized iterative maximum likelihood (ML) direct position determination (DIM-DPD) algorithm is proposed based on the expectation maximization (EM) concept for user equipment (UE) localization in Wi-Fi systems. By innovatively treating the non-line-of-sight (NLoS) angles-of-arrival (AoAs) and observed times-of-arrival (ToAs) as nuisance parameters in the received signal model and parameter estimation procedure, the proposed DIM-DPD demonstrates its adaptability and localization efficiency in dense multipath indoor environments. In the proposed method, the position of the UE is incorporated in the vectors denoting the location differences between the UE and the access point (APs), referred to as the UE-AP location difference vectors. The set of UE-AP location difference vectors allows constructing the related UE-AP variables, serving as the latent variables in the EM iterations. Then, by taking advantage of the alternating projection technique, the nuisance parameters and UE-AP variables in the DIM-DPD algorithm are alternatively updated on separated APs in parallel. Furthermore, instead of traditional grid search, the UE position is updated with an efficient closed-form solution by aggregating the distributed estimated low-dimensional UE-AP variables. Thus, overall, the proposed DIM-DPD approach facilitates decentralized direct localization with implementation feasible processing complexity. The provided numerical simulation results demonstrate that the proposed DIM-DPD algorithm achieves high positioning accuracy, fast convergence, and a good balance between computational complexity and performance.

Radio-based simultaneous localization and mapping (SLAM) has the potential to provide precise user equipment (UE) localization and environmental sensing capabilities by exploiting radio signals. Most existing approaches leverage line-of-sight (LoS) and single-bounce non-line-of-sight (NLoS) paths solely, while higher-order NLoS paths are treated as disturbance. In this paper, we investigate the benefits of leveraging double-bounce NLoS paths for solving the bistatic snapshot radio SLAM problem.We derive the Cramér-Rao bound (CRB) for joint estimation of the UE state and landmark positions when double-bounce NLoS paths are present. In addition, we propose an algorithm to identify double-bounce NLoS paths and leverage them into joint UE and landmarks estimation. The derived bounds are validated through simulated data, and the proposed algorithms are evaluated using experimental millimeter wave (mmWave) measurements harnessing beamformed 5G cellular reference signals. The numerical and experimental results demonstrate that the double-bounce NLoS paths which share at least one incidence point (IP) with the single-bounce NLoS paths improve the estimation accuracy of the UE state and existing IPs of single-bounce NLoS paths. Importantly, exploiting double-bounce NLoS paths enhances environmental mapping capabilities by revealing landmarks that are unobservable with single-bounce NLoS paths alone.

Integrated sensing and communication enables simultaneous communication and sensing tasks, including precise radio positioning and mapping, essential for future 6G networks. Current methods typically model environmental landmarks as isolated incidence points or small reflection areas, lacking detailed attributes essential for advanced environmental interpretation. This paper addresses these limitations by developing an end-to-end cooperative uplink framework involving multiple base stations and users. Our method uniquely estimates extended landmark objects and incorporates obstruction-based outlier removal to mitigate multi-bounce signal effects. Validation using realistic ray-tracing data demonstrates substantial improvements in the richness of the estimated environmental map.

Accurate mobile device localization is critical for emerging 5G/6G applications such as autonomous vehicles and augmented reality. In this paper, we propose a unified localization method that integrates model-based and machine learning (ML)-based methods to reap their respective advantages by exploiting available map information. In order to avoid supervised learning, we generate training labels automatically via optimal transport (OT) by fusing geometric estimates with building layouts. Ray-tracing based simulations are carried out to demonstrate that the proposed method significantly improves positioning accuracy for both line-of-sight (LoS) users (compared to ML-based methods) and non-line-of-sight (NLoS) users (compared to model-based methods). Remarkably, the unified method is able to achieve competitive overall performance with the fully-supervised fingerprinting, while eliminating the need for cumbersome labeled data measurement and collection.

Satellite networks have become central to advancing modern communication standards due to their potential for extensive global coverage. Despite this, high latency in satellite systems poses critical challenges for delay-sensitive applications, underscoring the need for reliable metrics that reflect ultra-reliable, low-latency communication. To address these challenges, data-oriented approaches, widely adopted in terrestrial networks, offer a fresh perspective by assessing transmission performance through delay outage rates. This work introduces the data-oriented approach to hybrid satellite-terrestrial networks (HSTNs), focusing on an uplink non-orthogonal multiple access (NOMA) scheme where grant-based and grant-free users coexist. The proposed analytical framework reveals the relationship between the amount of information data, power consumption, and the satellite distance, providing valuable pathway for optimizing the performance of HSTNs in the context of delay-sensitive applications.

The concept of 6G distributed integrated sensing and communications (DISAC) builds upon the functionality of integrated sensing and communications (ISAC) by integrating distributed architectures, significantly enhancing both sensing and communication coverage and performance. In 6G DISAC systems, tracking target trajectories requires base stations (BSs) to hand over their tracked targets to neighboring BSs. Determining what information to share, where, how, and when is critical to effective handover. This paper addresses the target handover challenge in DISAC systems and introduces a method enabling BSs to share essential target trajectory information at appropriate time steps, facilitating seamless handovers to other BSs. The target tracking problem is tackled using the standard trajectory Poisson multi-Bernoulli mixture (TPMBM) filter, enhanced with the proposed handover algorithm. Simulation results confirm the effectiveness of the implemented tracking solution.

The recent development of mobile communication systems has introduced a myriad of new use cases from XR headsets to industrial automation, where high-precision 3D position and orientation information together with low latency operation, is of paramount importance. In this paper, we propose a novel high-precision 3D position and 3D orientation tracking scheme with per-antenna millimeter-wave delay-Doppler measurements, while considering a quaternion-based representation for the device orientation. Compared to representing the orientation with conventional yaw, pitch and roll angles, quaternion-based approach avoids problematic singular points and angle discontinuities, and provides stable tracking with all possible device orientations. The proposed tracking scheme is founded on extended Kalman filter, for which we derive and express all the needed processing steps for prediction and update stages. The numerical results show that the proposed approach is able to avoid the singular point issue faced with the conventional tracking of yaw, pitch and roll angles, while reaching the accuracy of a benchmark carrier phase based ranging method. Furthermore, by exploiting Doppler measurements’ capability to directly measure a device velocity and an angular velocity of device rotation, millimeter-level positioning accuracy and degree-level orientation estimation accuracy is reached in the considered tracking scenario.

High-Precision localization turns into a crucial added value and asset for next-generation wireless systems. Carrier phase positioning (CPP) enables sub-meter to centimeter-level accuracy and is gaining interest in 5G-Advanced standardization. While CPP typically complements time-of-arrival (ToA) measurements, recent literature has introduced a phase-only positioning approach in a distributed antenna/MIMO system context with minimal bandwidth requirements, using deep learning (DL) when operating under ideal hardware assumptions. In more practical scenarios, however, antenna failures can largely degrade the performance. In this paper, we address the challenging phase-only positioning task, and propose a new DL-based localization approach harnessing the so-called hyperbola intersection principle, clearly outperforming the previous methods. Additionally, we consider and propose a processing and learning mechanism that is robust to antenna element failures. Our results show that the proposed DL model achieves robust and accurate positioning despite antenna impairments, demonstrating the viability of data-driven, impairment-tolerant phase-only positioning mechanisms. Comprehensive set of numerical results demonstrates large improvements in localization accuracy against the prior art methods.

This paper investigates the uplink carrier phase positioning (CPP) in cell-free (CF) or distributed-antenna-system context, assuming a challenging case where only the phase measurements are utilized as observations. In general, CPP can achieve sub-meter to centimeter-level accuracy but it is challenged by the integer ambiguity problem. In this work, we propose two deep learning approaches for phase-only positioning, overcoming the integer ambiguity challenge. The first one directly uses the phase measurements, while the second one first estimates the integer ambiguities and then it integrates them with the phase measurements for improved accuracy. Our numerical results demonstrate that an inference complexity reduction of two to three orders of magnitude is achieved, compared to the maximum likelihood baseline solution, depending on the approach and on the parameter configuration. This emphasizes the potential of the developed deep learning solutions for efficient and precise positioning in future CF 6G systems.

As wireless systems move toward 6G, ensuring ultra-reliable low-latency communication (URLLC) securely is a key design challenge. Addressing the strict latency and reliability demands requires a shift in performance evaluation. The delay-outage rate (DOR) has recently emerged as a data-oriented metric that captures the probability of transmission time exceeding a threshold under ideal conditions. This work extends the data-oriented framework to include physical layer security, focusing on jamming and eavesdropping threats. We analyze how constant and random jamming affect DOR in the multi-antenna systems, highlighting the role of spatial and spectral resources in mitigation. Our analysis, supported by empirical simulations, offers new insights for designing secure, low-latency systems resilient to jamming—supporting robust ultra- or hyper-reliable low-latency communications in future 6G networks.

The coexistence of radar and communications in wireless systems marks a paradigm shift for the sixth-generation (6G) networks. As 6G systems are expected to operate at higher frequencies and employ larger antenna arrays than fifth-generation (5G) systems, they can also enable more accurate sensing capabilities. To this end, the integrated sensing and communication (ISAC) paradigm aims to unify the physical and radio frequency (RF) domains by introducing the sensing functionality into the communication network. However, the clutter poses a challenge, as it can significantly degrade the sensing accuracy in ISAC systems. This paper presents a novel two-dimensional root multiple signal classification (2D-rootMUSIC)-based algorithm for static background clutter suppression. Computer simulation results indicate that the proposed method effectively mitigates the strong background clutter, yields accurate parameter estimation performance, and offers a notable improvement in the signal-to-clutter-and-noise ratio (SCNR), while outperforming the prior-art benchmark methods.

Transitions between "realities" play an important role in designing XR experiences, as they significantly influence user experience.However, integrating these transitions into XR applications poses a significant challenge on multiple levels, including both technical and design aspects.Although this challenge has been tackled over the past decade, existing efforts seem to be fragmented, often examining different issues in isolation.This paper aims to address this issue by conducting a systematic literature review of research related to transitions between realities using XR technology.The study seeks to provide an overview of these transitions, enhancing our understanding of the relevant elements and structures involved in the process.The review covers 38 papers from the Scopus and ACM DL databases.To provide the current state of research, we classify the literature into three main themes: research topics, application domains, and transition entities.Our findings reveal three research gaps in this area: 1) limited exploration of XR transitions across diverse domains, 2) contradictions in the transition metaphor, and 3) a lack of comprehensive understanding of XR transitions across multiple scales.In conclusion, we outline future research opportunities aimed at advancing knowledge in the field.

DECT-2020 NR is a radio access technology designed for 5G Internet of Things (IoT) applications. By leveraging multi-hop communications and listen-before-talk (LBT) mechanisms, DECT-2020 NR offers a flexible and cost-effective deployment solution for massive machine-type communications (mMTC). The ETSI standardization committee currently considers extending use-case options to ultra-reliable low-latency communications by enabling scheduled access along with LBT. However, this approach can increase the cost of the end systems. The aim of this study is to investigate whether standardized physical and medium access control (MAC) mechanisms, including power control, LBT, and priority queuing, can provide near-URLLC operation over multihop topologies. Our results show that to improve loss performance one needs to disable power control, use a shorter back-off window, and enable priority data transmission. For improving latency performance it is critical to use shorter acknowledgement (ACK) timeout and send ACKs immediately after data packet reception. Applying these techniques allows us to improve multi-hop URLLC latency performance by up to 30-80% and loss performance by up to 10-20% depending on URLLC and mMTC traffic conditions with only negligible impact on mMTC traffic performance.

Ultra-reliable low-latency IoT communications (URLLC-IoT) has recently gained a growing interest. Here the challenge in reliable low-latency uplink transmission results from the transmission power limitations and lack of multiple antennas. However, in many IoT services the data volumes are small and sensor deployments may include massive number of devices. In this work we consider a coordinated uplink transmission of clustered IoT devices. The focus is on scenarios where location-based channel knowledge map (CKM) can be applied to enable cooperation. We model and analyse the impact of hardware impairments and erroneous CKM information. In the performance evaluation we focus on the recently introduced dataoriented approach that has gathered significant attention in the context of short-packet transmissions. Specifically, it introduces a transient performance metric for small data transmissions, where the amount of data and available bandwidth play crucial roles. Results show that cooperation between clustered IoT devices may provide notable benefits in terms of increased range. Yet, the performance of the coordinated transmission system is heavily depending on the strength of the static channel component in the CKM based cooperation, the level of hardware impairments and the quality of CKM information. Analytic results are verified against simulations, showing only minor differences between analytical and experimental results.

Reconfigurable intelligent surfaces (RISs) have emerged as a transformative technology for sixth-generation (6 G) communication networks, offering the ability to dynamically shape wireless propagation environments and thus efficiently enhance received signal quality. However, practical implementation of RIS faces challenges, including potential failures of individual elements (pixels), which can degrade the performance significantly. This paper leverages autoencoders and end-to-end (E2E) learning in RIS-aided systems to jointly optimize the RIS phase profiles and receiver angle-of-departure (AoD) estimation in the presence of pixel failures. The proposed E2E approach demonstrates resilience against practical pixel errors while is shown to achieve performance close to the fundamental bounds, thereby advancing the state-of-the-art in RIS-aided systems towards the 6 G era.

DECT-2020 NR is a recently standardized radio access technology (RAT) for massive machine-type 5G communications operating in the license-exempt and licensed bands. By relying on multi-hop communications and listen-before-talk (LBT) medium access, DECT-2020 NR allows for cost-controlled flexible deployments. However, the medium access procedures for some of the bands currently used by classic DECT systems are strictly regulated by long-standing policies in the USA. The goal of this study was to evaluate and compare the performance of DECT-2020 NR and medium access specified by the Federal Communications Commission (FCC) for the Unlicensed Personal Communications Service (UPCS) band. Our results demonstrate that in a mesh system, the use of FCC access rules leads to drastic performance degradation, resulting in less than 85% of delivered packets compared to 98-99% for DECT-2020 NR. This results in a slight 1-2% degradation for coexisting classic DECT devices. For efficient use of the UPCS band, we recommend revisiting current FCC rules, allowing for LBT-based operations.

The data-oriented communications approach was initially introduced to develop novel transmission strategies for individual data sessions by accounting for instantaneous channel conditions under latency constraints. However, existing studies are limited to scenarios where channel state information (CSI) is either fully or partially available at the receiver, which may be impractical for short-packet transmissions. In this context, the present work focuses on analyzing the delay-outage rate (DOR), a data-oriented performance metric, under the noncoherent communications paradigm, where accurate CSI is not required. To this end, we first derive and calculate the DOR metric, which is then used to define transmission as well as receiver design requirements based on data and bandwidth parameters. Beyond emphasizing the significance of the new data-oriented DOR metric, the results provide valuable insights for designing and facilitating delay-sensitive and highly reliable noncoherent data transmission in future networks.

High peak-to-average-power ratio (PAPR) is an inevitable challenge in orthogonal frequency-division multiplexing (OFDM) based networks, known to be particularly harmful to efficient utilization of practical power amplifiers (PAs). To preserve the waveform quality, PA back-off can be introduced which, however, directly limits the potential uplink (UL) coverage. This paper proposes a novel PAPR reduction method, by transmitting a peak-cancellation signal (PCS) spatially-precoded to the frequency resources within the operating channel, without introducing any overheads or receiver side interference. The proposed approach can be applied to codebook-based and non-codebook-based transmissions, while also allows for extending the PCS frequency allocation towards neighboring physical resource blocks (PRBs) without interference to other users. Extensive numerical results are provided, conforming with the 3GPP 5G NR transmitter requirements, while also incorporating realistic uplink PA models. The obtained numerical results at FR1 reveals up to 2.6 dB net gain in the effective uplink link budget via using the novel PCS, compared to the plain legacy OFDM signal. Such link budget gains translate to substantial uplink coverage improvements, which can be one major asset in future network deployments towards the 6G era.

Unlocking the potential of millimeter-wave (mmWave) phased array systems demands robust nonlinear transmitter modeling and digital pre-distortion (DPD) techniques. In this article, we present a novel behavioral modeling approach and the corresponding linearization solution for beamforming antenna arrays comprising multiple and mutually interacting nonlinear power amplifier (PA) units. Our non-recursive transmitter model simplifies numerical evaluations across diverse phased array/multiple-input multiple-output (MIMO) configurations under crosstalk-induced load modulation. We introduce a novel, nonlinear forward model parameter identification algorithm tailored for crosstalk-prone array systems and applicable in arbitrary MIMO transmitter configurations, enabling precise modeling and characterization using over-the-air (OTA) observations. Furthermore, we propose an offline direct learning architecture based DPD method, harnessing the estimated nonlinear array forward model and specific beam-sweeping procedure, for linearizing phased arrays under severe load modulation. Numerical assessments across various scenarios demonstrate superior performance, while physical validation on a measurement test bench reinforces our methodology’s real-world applicability. Overall, this work paves the way for advanced nonlinear array transmitter optimization and linearization, vital for next-generation wireless communication networks.

In this article, we study the timely notion of short-packet communications, with specific focus onuplink rate-splitting multiple access (RSMA) systems under the finite blocklength regime. Specifically, we consider rate-adaptive and power-adaptive uplink RSMA mechanisms, incorporating multiple user groups, and derive analytical expressions for the fundamentaldelay-outage rate (DOR)metric. The analytical derivations are validated through the corresponding Monte Carlo numerical simulations, reflecting high accuracy. Then, the derived DOR expressions are exploited for providing optimal DOR performance under diverse individual transmission parameters, such as different information delivery time thresholds, varying blocklengths and channel bandwidths, while also considering the non-orthogonal multiple access (NOMA) as a particular special case. Importantly, DOR optimization in terms of the message splitting ratio is also pursued, and a feasible optimization algorithm is proposed. A vast collection of numerical results is then provided, comparing between the rate-adaptive and the power-adaptive schemes, assessing the impact of message splitting ratio optimization while also comparing between RSMA and NOMA. Additionally, the overall power efficiency and impacts of imperfect channel state information (CSI) are assessed and shown. Overall, the offered analysis methods and numerical results provide valuable tools and insight for deploying, designing and optimizing data-oriented uplink RSMA mechanisms in future wireless systems such as the emerging 6G networks.

In this paper, a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted uplink non-orthogonal multiple access (NOMA) system with semi-grant-free (SGF) transmission in the presence of the illegal user is investigated. Particularly, with the help of SGF transmission, grant-free (GF) users omit the tedious process of requesting authorization from the base station and are able to transmit signals by sharing the resource blocks reserved for grant-based (GB) user. Among theKGF users, the one with the best channel conditions is eligible to share the resource block for GB user, which ensures the quality of service for the GB user and avoids collisions due to too many GF users. For this setup, we consider the covert performance and secrecy performance. Particularly, expressions for the outage probability (OP), detection error probability (DEP), optimal detection threshold, and secrecy outage probability (SOP) are derived to assess the system performance. In addition, we present many special cases to get more intuitive insights. Finally, the simulation results verify the correctness and validity of the theoretical calculations, and the effect of each parameter on the system performance is also investigated.

Positioning is a vital capability in different radio systems for extracting situational awareness, with path loss (PL)-based positioning playing a crucial role due to its widespread use in wireless standards. In this work, we propose anidle-modePL-based positioning approach without line-of-sight (LOS) detection that is suitable for millimeter-wave (mmWave) urban networks with directive beams in the base stations (BSs). With the beam gain significantly affecting the observed PLs, we divide the data and models relative to the beam direction rather than to LOS and non-line-of-sight (NLOS) BSs. Different approaches for obtaining the PL model parameter estimates are proposed, including model fitting taking into account the influence of the noise limit and direct optimization based on positioning accuracy using the training data set. In addition to the PLs, azimuth- and elevation-of-departure (AoD and EoD) are estimated, modeled, and used in the positioning calculations, building on maximum likelihood (ML) estimation. Comprehensive numerical results and performance assessments are provided, harnessing ray-tracing (RT) data in a 28GHz urban microcellular environment. The demonstrated median positioning error is under 20m reflecting an improvement of 50%-70% compared to the classical CellID method. Additionally, the results show that the proposed method outperforms machine learning based reference solutions. Finally, the methods and the resulting positioning performance are shown to be robust against variations in the underlying technical parameters, such as the BS transmit beam-width, as well as errors or imperfections in the assumed BS locations and BS orientation information.

The use of up to hundreds of antennas in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) poses a complexity challenge for digital predistortion (DPD) aiming to linearize the nonlinear power amplifiers (PAs). While the complexity for conventional time domain (TD) DPD scales with the number of power PAs, frequency domain (FD) DPD has a complexity scaling with the number of user equipments (UEs). In this work, we provide a comprehensive analysis of different state-of-the-art TD and FD-DPD schemes in terms of complexity and linearization performance in both rich scattering and line-of-sight (LOS) channels and with antenna crosstalk. We propose a novel low-complexity FD convolutional neural network (CNN) DPD. We also propose a learning algorithm for any FD-DPDs with differentiable structure. The analysis shows that FD-DPD, particularly the proposed FD CNN, is preferable in LOS scenarios with few users, due to the favorable trade-off between complexity and linearization performance. On the other hand, in scenarios with more users or isotropic scattering channels, significant intermodulation distortions among UEs degrade FD-DPD performance, making TD-DPD more suitable. The proposed learning algorithm allows FD-DPDs to outperform TD-DPD optimized by indirect learning architecture under antenna crosstalk.

This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database generator. RFF is a unique signature created in the emitter transmission chain by hardware flaws. These flaws may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce a virtual database and suggest utilizing it for the examination of three crucial aspects when creating a RFF database: the number of signals required to perform DL classification, the impact of RFF similarities between emitters, and the propagation channel impact in static and dynamic contexts. For instance, such analysis shows that data augmentation with 10 channels improves accuracy classification up to 70% in a scenario where RFFs are close from a transmitter to another.

WiFi Indoor localization plays a crucial role in an emerging application domain for tracking indoor people, however, the serious issue is that the WiFi signals from access points (APs) vary greatly over time and the deployment structure of APs may be changed, for example, some APs are replaced or removed over time, which cause localization accuracy reduced. To solve this problem, this paper presents WiLoc, a Long-term WiFi localization with Lightweight Siamese Neural Network. This method introduces a Siamese neural encoder-based framework to learn the similarity between three inputs, where the Siamese network only consists of three linear layers without any convolutional layer or transformer. The triplet loss function is utilized to supervise the training of the feature encoder. Then, the encodings from this encoder are input to K-Nearest Neighbors (KNN) to predict the user’s positions. Extensive experiments on the UJI dataset, show the proposed WiLoc can effectively relieve the degradation of localization accuracy over time compared to the state-of-the-art algorithms, the degradation is reduced from 51% to 12.1%, and the average localization error is 2.06 m.

Wireless localization and sensing are increasingly important capabilities when the networks are evolving towards the $6^{t h}$ generation era. While the physics-inspired geometrical models are known to perform well in line-of-sight (LoS) dominant scenarios, harnessing the power of artificial intelligence (AI) to improve robustness, efficiency, and performance in more complex propagation scenarios is an intriguing prospect. To this end, the hypernetwork (HN) is an emerging neural network (NN) architecture, where one model is used to parameterize the weights of the other, promising dynamic weight adaptation among other performance improvements. In this work, we propose the concept of Hypernetwork Localization (HypLoc) - a hybrid HN-based architecture for localization in beamforming millimeter-wave (mmWave) networks, while combining angle-of-arrival (AoA), time-of-flight (ToF), and received power (RP) as representative measurements. Considering a realistic urban vehicular environment, we first demonstrate the baseline effectiveness of HypLoc with a fixed and known gNodeB (gNB) deployment scenario. We then also study a scenario where the factory pre-training covers multiple different gNB deployment constellations and show that the proposed HypLoc clearly outperforms the traditional NNs. Finally, we also show that the HypLoc adapts faster and requires less training data when adapting to a previously unseen deployment scenario. Overall, the proposed approach facilitates efficient factory pre-training when operating under multiple different gNB deployment options.

In this paper, a novel binary waveform with low peak-to-average power-ratio (PAPR) and low out-of-band (OOB) emissions is presented, targeting at extreme uplink coverage solutions towards the 6G era. It uses the discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) signal model, as the baseline, togetherwith quadrature phase-shift keying (QPSK) modulation symbols, similar to the widely considered $\pi / 2$ binary phase-shift keying ($\pi / 2$-BPSK). The proposed approach further incorporates trellis-coding in a way which avoids sharp phase transitions in the generated signal, resulting in greatly reduced PAPR and OOB emissions. Additionally, while linear filtering based spectrum shaping has been widely considered in recent low-PAPR waveform studies, we propose to use phase interpolation to increase the sampling rate. This is nonlinear operation, but it helps to significantly reduce the PAPR compared to linear spectrum shaping. This can be achieved both with and without bandwidth extension. Further enhancement to the spectrum localization is achieved by improving the phase continuity of the generated signal, avoiding excessive phase transitions between consecutive transform-precoded OFDM symbols and between each main symbol and its cyclic prefix (CP).

In modern wireless networks evolving towards 6thgeneration, localization, and sensing in indoor environments play an increasingly critical role in ensuring reliability, security, and control over network users, including vehicular assets. Despite recent advancements in deep learning, using k-Nearest Neighbors (k-NN) as a positioning algorithm in Received Signal Strength Indicator (RSSI) fingerprinting-based localization still provides numerous advantages, including localization accuracy, reliability, and interpretability. In this work, we introduce Intersection over Union (IoU) as a novel similarity metric and introduce κ-enhanced k-NN, which enables dynamic neighbor selection leading to improved performance and generalization capabilities of the positioning algorithm. In the evaluation using 26 publicly available indoor positioning datasets, we clearly show the improvements in localization accuracy of the combined IoU with κ-enhanced k-NN over the relevant baselines.

Ensuring accurate, reliable, and effortless localization capabilities becomes one of the key requirements of the upcoming 6G and beyond wireless networks, while user equipment expands beyond traditional smartphones to countless Internet of Things (IoT) devices, vehicles, or drones. Location awareness becomes a necessity for smooth operation, security, and safety, while fingerprinting-based methods are able to ensure reliability and accuracy. k-Nearest Neighbors (k-NN) remains to this day one of the most popular localization algorithms, while its main drawbacks include increased complexity when operating on voluminous data, and requires exhaustive hyperparameter sweeping to find optimal performance. In this work, we propose a localization system denoted σ-MESS, which reduces the volume of the dataset, accelerates the positioning speed, and improves the positioning performance, while at the same time alleviates the requirement for finding optimal parameters for k-NN. The method is evaluated on 13 openly available indoor positioning datasets, reducing the achieved positioning error by 15% and positioning time by 87.5% on average, when compared to the k-NN with the same hyperparameters. We further compare the achieved results with the ones achieved in recently published papers outperforming numerous solutions.

DECT-2020 is a recently standardized enabling technology for 5G massive machine-type communications operating in the unlicensed band. By relying upon multi-hop communications, DECT-2020 allows for cost-controlled flexible deployments, it requires an efficient solution for topology design, including the relay selection and next-hop selection mechanisms. The goal of this paper is to evaluate the impact of these algorithms on the fraction of successfully delivered packets for DECT-2020 technology. To this aim, we utilize the system-level simulation tool to capture the functionality of the physical and data-link DECT-2020 solutions. Our numerical results illustrate that the system is susceptible to the choice of the energy conservation parameters with the difference in the fraction of delivered packets reaching 10% and may effectively lead to non-operational conditions. Without simple analytical models, these parameters must be set with caution. The relay-selection mechanism procedure is generally insensitive to the parameters of the relay selection procedure, resulting in a deviation of at most 3%. Finally, the power control feature gives an additional gain of around 2-4% compared to the system without power control.

The so-called data-oriented approach has gathered significant attention in the context of short-packet communications in fifth-generation (5G) and beyond networks. Specifically, it introduces a transient performance metric for small data transmissions, where the amount of data and available bandwidth play crucial roles. Such data-oriented approach has recently been introduced in downlink rate-splitting multiple access (RSMA) system context, which represents a flexible and promising non-orthogonal multiple access paradigm. Building upon such prior-art, this paper introduces the data-oriented approach to uplink RSMA systems under the finite blocklength regime. Specifically, we consider rate-adaptive and power-adaptive uplink RSMA mechanisms, under the data-oriented approach, and derive analytical expressions for the delay-outage ratio (DOR) metric. The analytical derivations are validated through corresponding Monte Carlo numerical simulations. The numerical results show that the DOR performance is directly linked with the transmit antenna diversity, while being also dependent on the accuracy of the channel state information as well as on the power budget of the uplink users. The obtained results highlight the importance of the new data-oriented DOR metric for efficient design of the uplink RSMA mechanism in future networks.

The Internet of Things (IoT) technology uses small and cost-effective sensors for various applications, such as Industrial IoT. However, these sensor nodes are powered by fixed-size batteries, which creates a trade-off between network performance and long-term sustainability. Moreover, some applications require the network to provide a certain level of service, such as a lower delay for critical data, while ensuring the operational reliability of sensor nodes. To address this energy challenge, external energy harvesting sources, such as solar and wind, offer promising and eco-friendly solutions. However, the available energy from a single energy source is insufficient to meet these requirements. This drives the utilization of a hybrid energy harvesting approach, such as the integration of solar and wind energy harvesters, to increase the amount of harvested energy. Nevertheless, to fully utilize the available energy, which is dynamic in nature, the sensor node must adapt its operation to ensure sustainable operation and enhanced network performance. Therefore, this paper proposes a hybrid energy harvesting-based energy neutral operation (ENO) medium access control (MAC) protocol, called HENO-MAC, that allows the receiver node to harvest energy from the solar-wind harvesters and adapt its duty cycle accordingly. The performance of the proposed HENO-MAC was evaluated using the latest realistic solar and wind data for two consecutive days in GreenCastalia. The simulation results demonstrate that the duty cycle mechanism of HENO-MAC effectively utilizes the harvested energy to achieve ENO and uses the available energy resources efficiently to reduce the packet delay for all packets and the highest priority packet by up to 28.5% and 27.3%, respectively, when compared with other existing MAC protocols.

Improving the performance of Artificial Neural Network (ANN) regression models on small or scarce datasets, such as wireless network positioning data, can be realized by simplifying the task. One such approach includes implementing the regression model as a classifier, followed by a probabilistic mapping algorithm that transforms class probabilities into the multi-dimensional regression output. In this work, we propose the so-called c2r, a novel ANN-based architecture that transforms the classification model into a robust regressor, while enabling end-to-end training. The proposed solution can remove the impact of less likely classes from the probabilistic mapping by implementing a novel, trainable differential thresholded Rectified Linear Unit layer. The proposed solution is introduced and evaluated in the indoor positioning application domain, using 23 real-world, openly available positioning datasets. The proposed C2R model is shown to achieve significant improvements over the numerous benchmark methods in terms of positioning accuracy. Specifically, when averaged across the 23 datasets, the proposed c2r improves the mean positioning error by 7.9% compared to weighted knn with k=3, from 5.43 m to 5.00 m, and by 15.4% compared to a dense neural network (DNN), from 5.91 m to 5.00 m, while adapting the learned threshold. Finally, the proposed method adds only a single training parameter to the ann, thus as shown through analytical and empirical means in the article, there is no significant increase in the computational complexity.

In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we propose a new multipath channel parameter estimation solution that operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beampatterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior art, in practical built environments with complex clutter and multi-bounce propagation scenarios, and do not rely on any a priori motion model. The performance of the proposed methods is assessed at the 60GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. A wide set of offered results demonstrate the improved performance, compared to the relevant prior art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.

Future wireless networks will integrate sensing, learning, and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment (UE), and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency (RF) environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services but also become more resilient to channel-dependent effects such as blockage and better support adaptation in dynamic environments as networks reconfigure. In this article, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization, and machine learning (ML) techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray-tracing measurements and mathematical models that mix the digital and physical worlds.

One of the most fundamental problems in simultaneous localization and mapping (SLAM) is the ability to take into account data association (DA) uncertainties. In this paper, this problem is addressed by proposing a multi-hypotheses sampling distribution for particle filtering-based SLAM algorithms. By modeling the measurements and landmarks as random finite sets, an importance density approximation that incorporates DA uncertainties is derived. Then, a tractable Gaussian mixture model approximation of the multi-hypotheses importance density is proposed in which each mixture component represents a different DA. Finally, an iterative method for approximating the mixture components of the sampling distribution is utilized and a partitioned update strategy is developed. Using synthetic and experimental data, it is demonstrated that the proposed importance density improves the accuracy and robustness of landmark-based SLAM in cluttered scenarios over state-of-the-art methods. At the same time, the partitioned update strategy makes it possible to include multiple DA hypotheses in the importance density approximation, leading to a favorable linear complexity scaling, in terms of the number of landmarks in the field-of-view.

The digital front-end of the communication transceivers envisioned for fifth-generation (5G) and beyond requires highly configurable high-performance digital signal processing (DSP) hardware operating at very high sampling rates to accommodate increasing signal bandwidths and support a range of modulation schemes and transmitter architectures. In this article, we present an efficient implementation of a highly configurable DSP hardware generator that can generate high-performance DSP hardware for multiple transmitter architectures including Cartesian, polar, outphasing, and multilevel outphasing modulators. The generated hardware unit, which consists of multistage multirate filters and other required DSP operations, runs at sample rates up to 4 GHz. The hardware supports an adjacent channel leakage ratio (ACLR) down to −48 dB and an error vector magnitude (EVM) of 0.78% with a 7-bit phase signal at a sampling rate of 4 GHz for multilevel outphasing modulation. Digital synthesis of the circuit in a 5-nm complimentary metal-oxide semiconductor (CMOS) process yields a core area consumption of 0.01 mm2 and an estimated power consumption of 37.2 mW for a 200-MHz bandwidth 5G new radio (NR) baseband (BB) signal.

In this article, we consider a multiple-input multiple-output (MIMO) transceiver performing joint communications and sensing (JCAS) using fifth-generation New Radio (5G NR) standard-compliant orthogonal frequency-division multiplexing (OFDM) waveforms. Communication links are maintained with users having multiple spatial data streams over frequency-selective non-line-of-sight channels while simultaneously transmitting separate spatial data streams to different sensing directions, where a portion of the communication data streams’ power is reallocated to the sensing data streams. The received reflections from the environment due to all transmit (TX) streams are used to obtain range–velocity and range–angle maps. Through optimizing the TX precoding and receive combining, inter-user, intra-user, and radar–communications interference are also canceled. In addition, streams transmitted in the sensing directions are optimized to minimize the lower bounds of direction-of-arrival and delay estimates jointly, and the solution is analytically derived. The simulation results illustrate that the JCAS system can reliably perform target detection while minimizing lower bounds compared with a communications-only scenario. Further, the detection probability and estimation errors of sensing can be improved while also controlling the communications capacity of the OFDM waveform, thereby indicating the need to appropriately choose the optimization parameters to obtain an optimal trade-off.

In this article, we comprehensively investigate the potential of the digital polar radio transmitter architecture for multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) downlink system. In terms of throughput performance, we derive a lower bound for the average sum-rate achievable with Gaussian signaling inputs and zero-forcing (ZF) precoding based on Bussgang decomposition. By diagonal approximation, we derive an approximate, yet accurate, model for the distortion caused by uniform polar quantization, which can be used to evaluate the corresponding sum-rate in closed form. To assess the power efficiency, we provide power consumption models with realistic parameters and values for the quantized polar and Cartesian transmitters, based on state-of-the-art integrated circuit (IC) designs and measurements. Extensive numerical results demonstrate that the proposed quantized polar transmitter can enable excellent performance in terms of average sum-rate, symbol error rate (SER), and out-of-band (OOB) emission level, compared to the Cartesian architecture. Furthermore, the power consumption comparisons show that the digital polar transmitter can save more than 36% in the energy consumption under 64-antenna setting in typical 5G enhanced mobile broadband use cases, thus making it highly appealing for future power-efficient massive MIMO transmitter implementations.

Reconfigurable intelligent surface (RIS)-assisted localization has attracted extensive attention as it can enable and enhance localization services in extreme scenarios. However, most existing works treat RISs as anchors with known positions and orientations, which is not realistic in applications with mobile or uncalibrated RISs. This work considers thejoint RIS calibration and user positioning(JrCUP) problem with an active RIS. We propose a novel two-stage method to solve the considered JrCUP problem. The first stage comprises a tensor-estimation of signal parameters via rotational invariance techniques (tensor-ESPRIT), followed by a channel parameters refinement using least-squares. In the second stage, a two-dimensional search algorithm is proposed to estimate the three-dimensional user and RIS positions, one-dimensional RIS orientation, and clock bias from the estimated channel parameters. The Cramér-Rao lower bounds of the channel parameters and localization parameters are derived to verify the effectiveness of the proposed tensor-ESPRIT-based algorithms. In addition, simulation results reveal that the active RIS can significantly improve the localization performance compared to the passive case under the same system power supply in practical regions. Moreover, we observe the presence of blind areas with limited JrCUP localization performance, which can be mitigated by either leveraging more prior information or deploying extra base stations.

Recently proposed by 3GPP, Integrated Access and Backhaul (IAB) technology promises to deliver a cost-efficient and flexible solution for network densification in 5G/6G systems. Since IAB architecture is based on multi-hop topology and advanced functionalities, such as multi-connectivity transmission and multi-routing, the potential utilization of IAB systems raises an issue of efficient system design. In this paper, we develop an optimization framework capable of jointly selecting transmission paths and allocating radio resources in compliance with half-duplexing and interference constraints. The presented numerical results illustrate that directional mm Wave beams employed at the wireless backhaul are essential for capacity boosting, thus allowing to fully exploit the radio resources in self-backhauled systems. We also establish that the multi-hop IAB topology provides advantages in terms of end-to-end user throughput as compared to single-hop systems.

This paper investigates the potential to leverage existing 5G NR signals for network-side integrated sensing and communications (ISAC). In general, the synchronization signal block (SSB) is a suitable candidate for always-on downlink sensing, due to its frequent periodical availability and because of its beam-sweeping nature. However, as this work demonstrates, using only the SSB has challenges related to radar ambiguity while being also limited in both distance and velocity resolution due to limited bandwidth and per-beam time duration, respectively. A novel solution is then introduced by combining SSB with downlink control information (DCI) and system information block 1 (SIB1) symbols. The corresponding implications and variants how SIB1 is optimized and configured are discussed, covering both 5G evolution and potential 6G solutions. The performance of the proposed approach is also assessed through realistic numerical evaluations at both 3.5 GHz and 28 GHz network deployments, and shown to yield up to 25 dB suppression in radar peak sidelobe level (PSL) compared to SSB-only based range-velocity profile. Also considerable improvements in the sensing resolution in the order of 120–190% are demonstrated.

Radio frequency fingerprinting (RFF), a technique based on specific transmitter hardware impairments, has emerged as an effective solution for wireless device identification. In this paper, we present a flexible deep CNN-LSTM for RF feature extraction capable of handling inputs with varying lengths. We construct a channel-independent spectrogram by exploiting the amplitude and phase information of the received RF signals, ensuring the extractor’s resilience to channel variations. To evaluate the performance of the proposed approach, we utilize the open-source LoRa dataset consisting of 60 commercial off-the-shelf LoRa devices and a USRP N210 software-defined radio platform. The experimental results show that classifiers perform better when trained with RF templates generated from amplitude-phase spectrogram than amplitude-only spectrogram. This is due to the additional information present in the amplitude-phase channel-independent spectrogram.

The recently standardized ETSI DECT-2020 New Radio (NR) technology promises to enable operator-independent Internet-of-Things (IoT) services. One of the supported operating bands is the 1880 − 1900 MHz band where IoT devices use one or more 1.728 MHz wide channels for multi-hop communications. It has been shown that such systems scale well to satisfy the requirements of 5G massive machine-type communications (mMTC). During the standardization process, the coexistence with classic DECT technology was addressed but the benefits of different options have not been deeply explored. In this paper, by utilizing system-level simulation techniques we evaluate several coexistence solutions for DECT-2020 and classic DECT systems including conventional listen-before-talk access (LBT), last-minute scan, and scheduling-based mechanisms. Our numerical results illustrate that the standard LBT operation ensures excellent performance with no more than 2% of packet drops for classic DECT, and still results in satisfactory DECT-2020 operation under 25% of resources allocated to classic DECT. The use of last-minute-scan leads to drastic performance degradation of DECT-2020 (by more than 10% in terms of packet drops) as compared to the standard LBT-only operation. Finally, a scheduling-based mechanism allows to improve the LBT performance for DECT-2020 devices by 2-5%.

A symbolic feature that integrates the space, air and ground network components for service in challenging and remote areas is being envisaged with continuity and high mobility of the 6G mobile system. Simultaneously providing sensing and connectivity over the radio signal becomes essential to support the management of low-space air crafts in the mobile system with limited spectrum resources. In this paper, we investigate the optimal joint radar and communications beamforming scheme with the presence of the clutter to support the low-space airborne vehicles, e.g. unmanned aerial vehicles or drones that are essential components of Non-Terrestrial Networks. The proposed scheme achieves the optimal signal-to-clutter-plus-noise ratio of the sensing function while maintaining the performance of the predefined communications. The novel application of approximations and rank-reduction algorithms in this work maximizes the joint radar and communications performance, for a system model similar to the one that is solved with a local optimum solution in a previous work. The numeric simulation results show that our approach maintains low complexity while guaranteeing the global optimum beamforming solution.

In this article, we propose a new robust and highly efficient digital predistortion (DPD) concept for the linearization of wideband RF power amplifiers (PAs). The proposed approach is based on the combination of a parallelized delta-sigma modulator (DSM) and a forward model of the PA. This concept applies multi-rate techniques on a DSM that incorporates the forward PA model in its feedback loop to perform the required signal predistortion. Such a technique eliminates the need of reverse modeling and its associated problems. The multi-rate approach relaxes enormously the clock speed requirement of the DPD, which allows handling high signal bandwidths at feasible sampling rates. Moreover, enhanced performance can be achieved without the need of increasing the order of the modulator which reduces the sensitivity of the system to gain variations and phase distortions caused by the nonlinear PA characteristics. Three time-interleaved parallel DPD (P-DPD) variants are described and introduced, all of them have been shown to offer increased accuracy, and consequently better linearization performance compared to the DSM-based DPD state-of-the-art. The proposed architectures are tested and assessed using extensive real-world RF measurements at the 3.6 GHz band utilizing wideband 100 MHz 5G New Radio (NR) transmit waveforms, evidencing excellent transmit signal quality.

This article investigates the feasibility of combinerless multilevel outphasing transmitter as a potential architecture for large millimeter-wave (mmWave) phased arrays. We consider two distinct ways of distributing the component signals to the antennas and develop a model for the received signal at each radiated spatial direction from a phased array. Based on the received signal model, we derive expressions for the signal-to-distortion ratio as well as total power experienced at each spatial direction. Furthermore, antenna branch mismatches, overload distortion and quantization are considered, and an analytical model for the signal-to-distortion ratio at the intended receiver is derived. We additionally establish a model for comparing the achievable energy efficiency to those of the relevant reference methods. Extensive numerical experiments are carried out to verify the analytical works, and to assess the commonly used metrics of error vector magnitude (EVM) and total radiated power adjacent channel leakage ratio (TRP-ACLR). It is shown that the combinerless architecture is a valid option for mmWave phased arrays, demonstrating favorable EVM results and TRP-ACLR beyond the 28 dBc limit imposed by the 3GPP, even in the presence of the considered distortions. The conducted energy efficiency assessment shows that efficiency of the reference methods can be exceeded with sufficient amount of outphasing levels. The considered architecture is thus an interesting alternative for addressing the linearity vs. energy-efficiency challenge in mmWave phased-array systems.

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model’s robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.

In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time- and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum mean-squared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on delays and angles, and ignore the Doppler, although it contains geometric information. In this paper, we study the benefits of exploiting Doppler in SLAM through deriving the posterior Cramér-Rao bounds (PCRBs) and formulating the extended Kalman-Poisson multi-Bernoulli sequential filtering solution with Doppler as one of the involved measurements. Both theoretical PCRB analysis and simulation results demonstrate the efficacy of utilizing Doppler.

Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (sPAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).

5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the accuracy and precision improvements of the resulting SLAM filter.

This paper studies a drone relay assisted cooperative wireless communication system. Specifically, a drone is used as the relay node to establish communication between the base station and an aerial mobile terminal under realistic channel models with the consideration of line-of-sight probability. The total transmission time is divided into smaller time slots and in each time slot the relay uses decode-and-forward protocol to forward the received information to the mobile terminal. Then, an optimization problem is formulated where the objective is to maximize the sum rate over the whole transmission time. The formulated problem is non-convex. However, we show that for several cases the global optimal solution can be achieved. Moreover, we develop a low-complexity algorithm to find suboptimal solutions for the other cases.

Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform. In most of the research works so far, RIS has been treated as a passive base station (BS) with a known state, in terms of its location and orientation, to boost the communication and/or terminal positioning performance. However, such performance gains cannot be guaranteed anymore when the RIS state is not perfectly known. In this paper, by taking the RIS state uncertainty into account, we formulate and study the performance of a joint RIS calibration and user positioning (JrCUP) scheme. From the Fisher information perspective, we formulate the JrCUP problem in a network-centric single-input multiple-output (SIMO) scenario with a single BS, and derive the analytical lower bound for the states of both user and RIS. We also demonstrate the geometric impact of different user locations on the JrCUP performance while also characterizing the performance under different RIS sizes. Finally, the study is extended to a multiuser scenario, shown to further improve the state estimation performance.

The very low block error rate (BLER) targets required in ultra-reliable low-latency communications (URLLC) call for new channel state information (CSI) feedback enhancements for an accurate link adaptation (LA) in the radio interface. This paper describes and analyses two new feedback reporting types, in addition to the legacy positive and negative acknowledgements (ACK/NACK) feedback, based on a form of soft-ACK reports. The aim is to improve the outer loop link adaptation (OLLA) accuracy in 5G New Radio (5G NR) based URLLC wireless communications systems where the amount of NACK events will be negligible. These schemes are based on the physical downlink data channel (PDSCH) decoding performance. In particular, the methods are based on an indication of the decoding margin of a PDSCH transmission, for the so-called Scheme A, and on an indication of the estimated block error probability (BLEP) of a PDSCH transmission, for the so-called Scheme B. In addition, two interference measurement (IM) approaches are analysed and compared based on non zero power CSI reference signal (NZP CSI-RS) and CSI interference measurement (CSI-IM) resources. The results show that Scheme A does not converge to any predefined BLER target while Scheme B allows faster convergence times towards the predefined target compared to legacy OLLA scheme. These results indicate that a new feedback reporting approach based on the estimated BLEP is suitable to achieve the tight latency requirements in URLLC scenarios together with efficient radio link performance.

In this paper, we address and analyze different uplink (UL) transmission schemes for the 5G New Radio (NR) deployments at unlicensed spectrum. Specific emphasis is on the new NR-unlicensed (NR-U) wideband physical random access channel (PRACH) under short preamble formats as well as on the base-station receiver sensitivity requirements for the fixed reference channels. In general, in order to comply with ETSI regulations, the UL waveform resource allocation has been revised in NR-U. On one hand, the bandwidth (BW) of the PRACH sequences has been increased according to numerology, which based on the performance analysis presented in this paper provides a substantially improved detection performance while fulfilling the coverage requirements of Re1-15 preambles. On the other hand, the physical uplink shared channel (PUSCH) resource allocation is based on block interlace frequency division multiple access (B-IDFMA). This design is characterized by the number of interlaces allocated within the transmission BW and it should be properly defined for the multiple physical layer numerologies supported in NR systems. The NR-U PUSCH performance results provided in this paper show that the B-IFDMA design yields the best performance for the case of one interlace allocation compared to contiguous resource allocation for all numerologies. Additionally, it is shown that the non-B-IFDMA PUSCH design outperforms the NR-U design for larger number of allocated interlaces. There is thus a trade-off between the frequency diversity gain achieved by the sparse PRB distribution in NR-U and the corresponding channel estimation challenges impacting the demodulation performance.

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.

In this article, we study the joint communication and sensing (JCAS) paradigm in the context of millimeter-wave (mm-wave) mobile communication networks. We specifically address the JCAS challenges stemming from the full-duplex operation in monostatic orthogonal frequency-division multiplexing (OFDM) radars and from the co-existence of multiple simultaneous beams for communications and sensing purposes. To this end, we first formulate and solve beamforming optimization problems for hybrid beamforming based multiuser multiple-input and multiple-output JCAS systems. The cost function to be maximized is the beamformed power at the sensing direction while constraining the beamformed power at the communications directions, suppressing interuser interference and cancelling full-duplexing related self-interference (SI). We then also propose new transmitter and receiver beamforming solutions for purely analog beamforming based JCAS systems that maximize the beamforming gain at the sensing direction while controlling the beamformed power at the communications direction(s), cancelling the SI as well as eliminating the potential reflection from the communication direction and optimizing the combined radar pattern (CRP). Both closed-form and numerical optimization based formulations are provided. We analyze and evaluate the performance through extensive numerical experiments, and show that substantial gains and benefits in terms of radar transmit gain, CRP, and SI suppression can be achieved with the proposed beamforming methods.

This article presents two novel digital predistortion (DPD) based architectures that jointly mitigate the inphase/quadrature (IQ) modulator impairments and the power amplifier (PA) nonlinear distortion in wireless transmitters. The proposed architectures are multibit cartesian and complex delta-sigma modulator-based joint DPDs, called CDSM-JDPD and CXDSM-JDPD, respectively, which enable using low-cost digital-to-analog converters (DACs) while offering versatile linearization capabilities to combat the coexisting distortions of the PA and the IQ modulator. The proposed approach alleviates the need for reverse modeling and implementation of extra hardware to separately deal with frequency-dependent IQ impairments. Moreover, the CXDSM-JDPD enhances the linearization performance and relaxes the high oversampling ratio (OSR) requirement by quantizing the signal more efficiently. Furthermore, the presented concepts inherently support the use of low-resolution DACs, which offers a tremendous advantage in designing and implementing low-cost and energy-efficient radio transmitters. Extensive set of hardware-in-the-loop RF verification measurements with a commercial PA are provided, including two timely 5G New Radio (NR) scenarios at NR bands n3 and n78, while covering channel bandwidths up to 100 MHz and varying the OSR and the DAC bit resolution. The obtained results demonstrate the excellent linearization capabilities of the proposed solutions and their superiority compared to other DSM-based DPD approaches.

Orthogonal frequency-division multiplexing (OFDM) has been selected as a baseline waveform for long-term evolution (LTE) and fifth-generation new radio (5G NR). Fast-convolution (FC)-based frequency-domain signal processing has been recently considered as an effective tool for transmitter and receiver side subband filtering of OFDM-based waveforms. However, for the original continuous FC-based model, the filtering can, in general, be configured in time-direction only with the granularity of half subframe, corresponding to 7, 14, or 28 symbols with 15kHz, 30kHz, or 60kHz subcarrier spacing, respectively. In this paper, we present a symbol-synchronous FC-processing scheme flexibly allowing filter re-configuration with the time resolution equal to one OFDM symbol while supporting tight carrier-wise filtering for 5G NR in mixed-numerology scenarios with adjustable subcarrier spacings, center frequencies, and subband bandwidths, as well as providing co-existence with LTE. Proposed approach segments each stream of time-domain OFDM symbols into overlapping processing blocks of fixed size. Symbol synchronous processing is achieved by dynamically adjusting the overlap between the processing blocks while aligning the payload part of the processing blocks with the boundaries of the OFDM symbols. The proposed scheme is demonstrated to support the envisioned use cases of 5G NR and provide a flexible starting point for sixth generation (6G) development.

The employment of passive reflectors enables the millimeter-wave automotive radars to detect an approaching vehicle in non-line-of-sight conditions. In this paper, the installation of such reflectors above the sidewalk at an intersection is proposed and studied, avoiding pedestrians’ blockage and road dust effect at ground level. Through the analysis of the backscattering power, it is shown that the suggested scheme may detect an approaching vehicle in the blind zone at distances of 30…50 m to the intersection point. Additionally, the analysis shows that efficient operation is highly dependent on the spatial orientation and size of the reflector. Even a few degrees rotation may change the detecting range by several meters. In turn, the larger area of the reflector may cover longer detecting distances, improving the radar scheme’s overall performance. It is also shown that further performance enhancement can be achieved by employing a C-type radar, contributing an extra 5 dB to the backscattering power relative to an A-type radar. However, despite these improvements, the strongest scattering centre of the detectable vehicle is systematically identified to the bumper zone.

Since the beginning of the fifth generation (5G) standardization process, positioning has been considered as a key element in future cellular networks. In order to perform accurate positioning, solutions for estimating and processing location-related measurements such as direction of arrival (DoA) and time of arrival (ToA) for various use-cases need to be developed. In this paper, building on the existing 5G new radio (NR) specifications and millimeter wave frequencies, we propose a novel estimation and tracking solution of the DoA and ToA such that only analog/radio frequency (RF) beamforming-based observations are utilized. In addition to the proposed extended Kalman filter (EKF)-based estimation and tracking approach, we derive Cramér-Rao lower bounds (CRLBs) for the considered RF multi-beam system, and propose an information-based criterion for selecting the necessary beams for the estimation process in order to provide highly accurate performance with feasible computational complexity. The performance of the proposed method is evaluated using extensive ray-tracing simulations and numerical evaluations, and the results are compared with other estimation and beam-selection approaches. Based on the obtained results, beam-selection at the receiver can have a significant impact on the DoA and ToA estimation performance as well as on the subsequent positioning accuracy. Finally, we demonstrate the highly accurate performance of the methods when extended to joint multi-receiver-based device positioning and clock synchronization.

Fingerprinting is a widely used technique in indoor positioning, mainly due to its simplicity. Usually, this technique is used with the deterministic k - Nearest Neighbors (k-NN) algorithm. Utilizing a neural network model for fingerprinting positioning purposes can greatly improve the prediction speed compared to the k-NN approach, but requires a voluminous training dataset to achieve comparable performance. In many indoor positioning datasets, the number of samples is only at a level of hundreds, which results in poor performance of the neural network solution. In this work, we develop a novel algorithm based on a transfer learning approach, which combines samples from 15 different Wi-Fi RSS indoor positioning datasets, to train a single convolutional neural network model, which learns the common patterns in the combined data. The proposed model is then fine-tuned to optimally fit the individual databases. We show that the proposed solution reduces the positioning error by up to 25% compared to the benchmark model while reducing the number of outlier predictions.

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.

Integrated communications, localization and sensing is one of the most addressed technologies considered for future mobile communications systems. In this context, a user equipment (UE)-centric mobile radar has been proposed to introduce improved situational awareness, and consequently potential improvement in network performance. In this paper, we derive an extended Kalman probability hypothesis density (EK-PHD) filter with a novel feature model, for a mobile radar based environment mapping, where range-angle detections are used to track map objects over time for dynamic map construction. In order to evaluate the performance of the proposed filtering approach, we employ a realistic ray-tracing-based simulation setup, which models the full transmission chain from the transmitted IQ-samples to mapping results. Besides this, a simplified measurement model considering solely single-bounce specular reflections is exploited for providing further insight into the filter performance. The obtained results show that the proposed EK-PHD filter is able to provide high-quality mapping results, reaching around 10 cm landmark estimation accuracy in the considered millimeter wave simulation setup.

Cellular network based connectivity for high speed trains (HSTs) is subject to large carrier frequency offset (CFO) due to high Doppler shifts. Large CFO will cause losing orthogonality between OFDM subcarriers which leads to significant performance loss. In this paper, we compare two Doppler estimation methods for HST links to compensate and remove CFO effect in the receiver in the context of 5G New Radio (NR) and long term evolution (LTE) systems. The first considered method to estimate Doppler shifts in LTE systems is based on the cyclic prefix (CP). The second method considered in NR system context is based on the phase tracking reference signal (PTRS). Simulation results shown that NR PTRS based method has higher estimation accuracy compared to LTE CP based method. Moreover, NR PTRS based method has higher signal to noise ratio (SNR) gain to achieve considered link performance target which is set to 70% of the maximum achievable throughput in this study. Additionally, the uplink data channel performance studies shown that, for systems using two demodulation reference signal (DMRS) per subframe for channel estimation, LTE CP based method can support only QPSK modulation scheme. In this case, a significant performance improvement is observed when the number of DMRS symbols per subframe is increased up to four, while almost the same performance is observed in NR systems for both slot patterns. Therefore, NR systems using PTRS based method with two DMRS configuration per subframe can be used with lower system overhead. In addition, block error rate (BLER) performance results show that NR PTRS based method has superior performance compared to LTE CP based method. Overall, these results demonstrate that NR PTRS based Doppler estimation method is more suitable in HST use cases.

This article contributes to the experimentation of joint vehicular communications and radio-based sensing using a fifth-generation (5G) New Radio (NR) waveform. Firstly, simulations are carried out to observe the effect of using default (communication-purpose) 5G NR waveforms for sensing, and they indicate high side-lobes in the range profile due to the existence of unused communication subcarriers within the frames of the 5G NR waveform. These can be filled with optimized frequency-domain symbols to minimize the side-lobes. As the main result, these observations are validated through over-the-air measurements with practical 5G NR waveforms, operating at the mm-wave frequency of 27.7 GHz. For this, an outdoor environment is mapped with both the default 5G NR waveform and the optimized waveform, and the latter showcases considerable improvement in the mapping image due to side-lobe suppression.

The millimeter wave (mmWave) device-to-device air interface not only supports a direct wireless connectivity among devices, but it also offers an improved beamforming capability to obtain the direction information among the vehicles and devices for positioning. Both features serve as the key physical layer components for communications and positioning in the industrial Internet of things (IIoT) systems. Exploiting both accurate beamforming and wide bandwidth in a mmWave network, high-accuracy positioning is achievable, which can be then facilitated for location-aware communications, for instance. However, the uncertainty of anchors' locations in the industrial environment highly degrades the achievable positioning accuracy if left without proper consideration. In order to resolve such challenge, this paper presents a cooperative positioning system (CPS), where the locations of all the vehicles and anchors can be jointly estimated based on acquired location-related measurements (LRMs). Furthermore, the positioning performance is evaluated under random trajectories and different geometric relationships between the vehicles and the anchors. We show that, the proposed positioning solution is capable of resolving the aforementioned challenge by simultaneously tracking the mobile vehicles while mapping the locations of the static anchors. Utilizing the LRMs from both time and angular domains, the achieved positioning accuracy in both 2D and vertical plane is demonstrated based on extensive numerical simulations. Last but not least, the impact of different numbers of the mobile vehicles on the overall positioning performance is also investigated.

A common design of the random access procedure on the physical random access channel (PRACH) is required for the diverse usage scenarios in the fifth generation new radio (5G NR) mobile networks. Based on the latest 3GPP specifications and evaluation assumptions agreed for Release 16, the 2 step-RACH (2SR) enhancement, composed of the denoted MsgA and MsgB, not only reduces the latency but also the control-signalling overhead due to the reduced number of messages transmitted. The channel structure of MsgA comprises RACH preamble and data in the physical uplink shared channel (PUSCH) while MsgB combines the random access response and the contention resolution. This procedure should operate in local area (LA), medium range (MR) and wide area (WA) cells despite the lack of time alignment (TA) in the PUSCH part of MsgA. The demodulation performance degradation observed without time offset compensation at the base station (gNB), specially for MR or WA cells, highlight that practical gNB implementations relying in MAC control element-based TA command for PUSCH time alignment are not conceivable for 2SR. Furthermore, in the case that all preambles from multiple users (UEs) trying to perform the initial access are mapped to the same PUSCH physical resources, the associated data parts overlap and may result in unsuccessful decoding. There is therefore a trade-off between the collision probability of the PUSCH part of MsgA and the resource overhead for 2SR. This paper addresses the channel structure design of this procedure for the preamble and data parts of MsgA together with the receiver processing framework. The performance results suggest that using lower payload sizes provide higher resource utilization and allow more UEs to be multiplexed within the same PUSCH occasion. In addition, using different DMRS ports for UEs sharing same physical resources decrease the probability of failure in the decoding of the data part of MsgA while reduces the resource overhead for 2SR.

Integrating sensing and communications is becoming a rising trend in the architecture design of the foreseeable mobile communications system, which could be driven by multifold applications and scarce spectrum resources. Regarding the demand for the economic surveillance solution in the secondary airports, the inborn imaging function in the 5G networks could be a promising candidate. This paper investigates the feasibility and capability of using 5G uplink and downlink reference signals for imaging purposes. An ambiguity function-based signal processing method is proposed in this paper to elaborate the imaging functionality in the 5G networks. The 5G signal-based imaging idea is validated with a realistic ray-tracing channel model generated from a simulated 3D airport model. Our method empowers the imaging functionality of the wireless communications system solely without the aid of external signal resources. Different from the conventional synthetic-aperture radar processing, our methods are adjusted for unevenly allocated reference signal symbols, which causes mirror images problem. The mirror images are quantified in the simulation result, and the mitigation strategies such as lower flight speed and narrower beam are proposed to resolve the problem.

In this paper, new digital predistortion (DPD) solutions for power amplifier (PA) linearization are proposed, with particular emphasis on reduced processing complexity in future 5G and beyond wideband radio systems. The first proposed method, referred to as the spline-based Hammerstein (SPH) approach, builds on complex spline-interpolated lookup table (LUT) followed by a linear finite impulse response (FIR) filter. The second proposed method, the spline-based memory polynomial (SMP) approach, contains multiple parallel complex spline-interpolated LUTs together with an input delay line such that more versatile memory modeling can be achieved. For both structures, gradient-based learning algorithms are derived to efficiently estimate the LUT control points and other related DPD parameters. Large set of experimental results are provided, with specific focus on 5G New Radio (NR) systems, showing successful linearization of multiple PA samples as well as a 28 GHz active antenna array, incorporating channel bandwidths up to 200 MHz. Explicit performance-complexity comparisons are also reported between the SPH and SMP DPD systems and the widely-applied ordinary memory-polynomial (MP) DPD solution. The results show that the linearization capabilities of the proposed methods are very close to that of the ordinary MP DPD, particularly with the proposed SMP approach, while having substantially lower processing complexity.

The$\kappa $-$\mu $/ inverse gamma and$\eta $-$\mu $/ inverse gamma composite fading models are presented and extensively investigated in this paper. We derive closed-form expressions for the fundamental statistics of the$\kappa $-$\mu $/ inverse gamma composite fading model, such as the probability density function (PDF), cumulative distribution function (CDF). Additionally, we solve the associated integral that is commonly used to obtain the moment generating function (MGF) of statistical distributions to provide an MGF-type function which is valid for performance analysis over the specified parameter space. Analytic expressions for the PDF, higher order moments and AF are also derived for the$\eta $-$\mu $/ inverse gamma composite fading model, while infinite series expressions are obtained for the corresponding CDF and MGF-type function. The suitability of the new models for characterizing composite fading channels is demonstrated through a series of extensive field measurements for wearable, cellular, and vehicular communications. For all of the measurements, two propagation geometry problems with special relevance to the two new composite fading models, namely the line-of-sight (LOS) and non-LOS (NLOS) channel conditions, are considered. It is found that both the$\kappa $-$\mu $/ inverse gamma and$\eta $-$\mu $/ inverse gamma composite fading models provide an excellent fit to fading conditions encountered in the field. The goodness-of-fit of these two composite fading models is also evaluated and compared using the resistor-average distance. As a result, it is shown that the$\kappa $-$\mu $/ inverse gamma composite fading model provides a better fit compared to the$\eta $-$\mu $/ inverse gamma composite fading model when strong dominant signal components exist. On the contrary, the$\eta $-$\mu $/ inverse gamma composite fading model outperforms the$\kappa $-$\mu $/ inverse gamma composite fading model when there is no strong dominant signal component and/or the parameter$\eta $is not equal to unity, indicating that the scattered wave power of the in-phase and quadrature components of each cluster of multipath are not identical.

Device authentication is essential for securing Internet of things. Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits intrinsic and unique hardware impairments as the device identifier. The existing RFFI literature focuses on experimental exploration but comprehensive modelling is missing. This paper systematically models impairments of transmitter and receiver in narrowband systems and carries out extensive experiments and simulations to evaluate their effects on RFFI. The modelled impairments include oscillator imperfections, imbalance of inphase (I) and quadrature (Q) branches of mixers and power amplifier (PA) nonlinearity. We then propose a convolutional neural network-based RFFI protocol. We carry out experimental measurements over three months and demonstrate that oscillator imperfections are not suitable for RFFI due to their unpredictable time variation caused by temperature change. Our simulation results show that our protocol can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy, namely 99% and 89%, respectively. We also show that the RFFI has some tolerance on different receiver imbalances during training and classification. Specifically, the accuracy is shown to degrade less than 20% when the residual receiver's gain and phase imbalances are small. Based on the experimental and simulation results, we made recommendations for designing a robust RFFI protocol, namely compensate carrier frequency offset and calibrate IQ imbalances of receivers.

Improved mobile device battery lifetime and latency minimization are critical requirements for enhancing the mobile broadband services and user experience. Long-term evolution (LTE) networks have adopted discontinuous reception (DRX) as the baseline solution for prolonged battery lifetime. However, in every DRX cycle, the mobile device baseband processing unit monitors and decodes the control signaling, and thus, all instances without any actual data allocation leads to unnecessary energy consumption. This fact together with the long start-up and power-down times can prevent adopting frequent wake-up instants, which, in turn, leads to considerable latency. In this work, a novel wake-up scheme is described and studied, to tackle the trade-off between latency and battery lifetime in future 5G networks, seeking thus to facilitate an always-available experience, rather than always-on. Analytical and simulation-based results show that the proposed scheme is a promising approach to control the user plane latency and energy consumption, when the device is operating in the power saving mode. The aim of this article is to describe the overall wake-up system operating principle and the associated signaling methods, receiver processing solutions and essential implementation aspects. Additionally, the advantages compared to DRX-based systems are shown and demonstrated, through the analysis of the system energy-efficiency and latency characteristics, with special emphasis on future 5G-grade mobile devices.

In this paper, we present a novel nonlinear digital self-interference canceller algorithm, its implementation details on a software-defined radio (SDR) platform, and performance results of real-time full-duplex experiments on both device and link level. The canceller algorithm is based on an augmented Hammerstein model, with a nonlinear part modeling the transmitter non-idealities followed by a linear filter to model the self-interference (SI) channel. The nonlinear part includes a spline-based model for the nonlinear power amplifier, a polynomial model for baseband nonlinearities, as well as models for I/Q mismatch and LO leakage. The canceller is implemented on an FPGA as a part of an OFDM transceiver testbed for real-time measurements. Extensive real-time measurements show excellent performance: (1) the digital canceller, together with an RF isolator, can suppress the SI to within 1-2 dB's of the receiver noise floor, with total SI suppression of up to 103 dB; (2) digital cancellation of up to 46 dB is evidenced, which is among the highest real-time cancellations in literature; (3) system-level measurements with OFDM signals demonstrate the benefit of utilizing the proposed canceller in a two-way communication scenario, showing up to 90 % increase in sum-rate compared to half-duplex communication.

Joint communication and sensing (JCAS) is an emerging technology for managing efficiently the scarce radio frequency (RF) spectrum, and is expected to be a key ingredient in beyond fifth-generation (5G) networks. We consider a JCAS system, where the full-duplex radar transceiver and the communication transmitter are the same device, and pursue orthogonal frequency-division multiplexing (OFDM) waveform optimization by jointly minimizing the lower bounds of delay and Doppler estimation. This is attained by filling the empty subcarriers within the OFDM frame with optimized samples while reallocating a proportion of the communication subcarriers’ power, which essentially controls the fairness between the two functionalities. Both communication and filledradar subcarriersare used for radar processing. The optimized sample values are found analytically, and a computationally feasible algorithm is presented for this task. We also address how the peak-to-average power ratio of the waveform can be controlled and minimized along the optimization process. The results are then numerically evaluated in 5G New Radio (NR) network context, which indicate a trade-off between the minimization of the lower bounds. The main-lobe width and the peak side-lobe level (PSL) of the range and velocity profiles of the radar image are also analyzed. An inverse relation between the lower bounds and the PSLs is observed, while the main-lobe width can be minimized simultaneously. The trade-off between communication and sensing is investigated, which indicates that the lower bounds can be improved at the cost of the communication capacity. Moreover, over-the-air RF measurements are carried out with unoptimized and optimized 5G NR waveforms at the 28 GHz mm-wave band, to validate the range profile’s PSL improvement in an outdoor mapping scenario, depicting considerable performance gain.

Orthogonal frequency-division multiplexing (OFDM) has been selected as the basis for the fifth-generation new radio (5G NR) waveform developments. However, effective signal processing tools are needed for enhancing the OFDM spectrum in various advanced transmission scenarios. In earlier work, we have shown that fast-convolution (FC) processing is a very flexible and efficient tool for filtered-OFDM signal generation and receiver-side subband filtering, e.g., for the mixed-numerology scenarios of the 5G NR. FC filtering approximates linear convolution through effective fast Fourier transform (FFT)-based circular convolutions using partly overlapping processing blocks. However, with the continuous overlap-and-save and overlap-and-add processing models with fixed block-size and fixed overlap, the FC-processing blocks cannot be aligned with all OFDM symbols of a transmission frame. Furthermore, 5G NR numerology does not allow to use transform lengths shorter than 128 because this would lead to non-integer cyclic prefix (CP) lengths. In this article, we present new FC-processing schemes which solve or avoid the mentioned limitations. These schemes are based on dynamically adjusting the overlap periods and extrapolating the CP samples, which make it possible to align the FC blocks with each OFDM symbol, even in case of variable CP lengths. This reduces complexity and latency, e.g., in mini-slot transmissions and, as an example, allows to use 16-point transforms in case of a 12-subcarrier-wide subband allocation, greatly reducing the implementation complexity. On the receiver side, the proposed scheme makes it possible to effectively combine cascaded inverse and forward FFT units in FC-filtered OFDM processing. Transform decomposition is used to simplify these computations, leading to significantly reduced implementation complexity in various transmission scenarios. A very extensive set of numerical results is also provided, in terms of the radio-link performance and associated processing complexity.

The large available bandwidths at millimeter-wave (mmW) frequencies enable very high data rates and reduced latencies while can also facilitate high-resolution radio-based sensing. In this paper, we address the problem of providing the communications and sensing functionalities simultaneously at the same frequencies, with specific emphasis on the emerging 5G New Radio (NR) networks. To this end, a novel RF beamforming design and optimization approach is proposed, for dual-functional joint radar-communication systems, providing multiple simultaneous transmit beams to support efficient beamformed communications while an additional beam simultaneously senses the environment around the base-station. The proposed beamforming approach jointly optimizes the transmitter and receiver beamforming weights in order to maximize the sensing performance and mitigate the possible interference stemming from the communication beam, while guaranteeing also the target beamforming gain for the communications link. The performance of the proposed approach is assessed through comprehensive numerical evaluations, demonstrating that substantial gains and benefits can be achieved compared to more ordinary beamforming approaches.

This paper discusses waveform design for a joint radar and communication system, where the radar transceiver and the communication transmitter are considered to be the same full-duplex base station. The downlink orthogonal frequency-division multiplexing (OFDM) waveform of the communication system is used also for sensing. Thus, unused subcarriers within the OFDM symbols are exploited for radar purposes and filled up with optimized complex-valued data, so as to minimize the lower bounds of the variances of the delay and Doppler estimates of radar target parameters, while maintaining an acceptable level of performance for the communication system. The results indicate that significant improvements can be made for the radar system, but in compensation, the power allocated for the communication subcarriers needs to be reduced. Thus, a trade-off between the converged systems allows both to operate together with reduced performance degradation.

Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to specific traffic models. In this paper, a novel concept of wake-up scheduling is introduced to further improve the energy-efficiency of mobile devices and to deal with realistic traffic. The main idea is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically. For this, a proactive wake-up scheduler is proposed to take online decisions based on traffic prediction. Towards this end, a framework to predict packet arrivals based on recurrent neural networks is developed. Numerical results show that for given delay requirements of video, audio streaming, and mixed traffic flow, the proactive wake-up scheduler reduces the power consumption of the baseline wake-up scheme without scheduler by up to 36%, 28% and 9%, respectively.

High-level synthesis (HLS) aims to improve the productivity of digital logic design over traditional register-transfer level (RTL) methods. This paper shows that HLS can replace RTL when implementing a complex data path oriented signal processing algorithm under strict throughput constraints. Our system is a nonlinear spline-based Hammerstein self-interference (SI) canceller for full-duplex transceiver capable of achieving high SI suppression, while maintaining low computational complexity. The achieved suppression of the SI is superb 45 dB, while consuming 29 026 of the available LUTs, 17992 of registers, and 655 of the DSP slices on Kintex-7 XC7K410T FPGA. Our paper also compares the usability of two commercial HLS tools that were used in this work.

This paper proposes a cyclic prefix (CP) free spectral efficiency frequency division multiplexing (SEFDM) wireless signal transmission and reception method based on alignment signal (AS). The method needs the cooperation of both the transmission and reception sides. At the transmitter, time-domain AS is designed to prevent inter-symbol interference (ISI) caused by multipath propagation in the received signal. At the receiver, the channel circularity convolution providing processing is used to enable high accurate frequency domain one-tap equaliser. The extensive computer simulation results under the 5G new radio (5G-NR) channel model, TDL-D, show that the AS aided SEFDM has similar bit error rate performance as CP-SEFDM without energy and latency cost on CP. The AS-SEFDM capability to mitigate the ISI and to enable the one-tap equaliser in SEFDM systems makes it a promising technique for future wireless communication systems.

The 5th generation mobile networks introduce large bandwidths with extended beamforming capabilities, which results in increased spatial selectivity of received channel state information. A channel chart is a map of the radio geometry that surrounds the base station and it can be generated in an unsupervised manner from the received channel state information without any knowledge of actual measurement locations. In this work, we generate channel charts for multiple base stations using multidimensional scaling and combine them for a better shape of the radio geometry. The combined chart cannot be directly applied for absolute positioning, but it can be extended. Extension is performed with affine and conformal mappings to the charts. The method for generating and combining the charts as well the method of extension to absolute positioning is explained. Evaluations are performed for two different scenarios, one of which is an open space scenario, and the other utilizes ray-tracing data. Finally, the charts are presented and analyzed together with positioning estimation results for the considered scenarios.

Cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) is a widely adopted modulation scheme for broadband wireless communication systems. The use of CP makes the scheme robust and facilitates simple and effective channel equalization since it makes the frequency-selective multipath fading to appear as flat-fading at subcarrier level. However, CP causes overhead in the spectrum efficiency and power efficiency. Various schemes have been proposed in the literature to avoid the use of CP or other forms of guard intervals (GIs) between OFDM symbols, but none of those has been fully satisfying. One recent proposal is based on adding a so-called alignment signal to GI-less OFDM signal in such a way that (i) intersymbol interference is avoided and (ii) the multipath channel effect appears as the cyclic convolution between the effective channel impulse response and each OFDM symbol. While assuming channel knowledge on the transmitter side, this approach allows basic OFDM signal processing to be used on the receiver side for channel estimation, equalization, and synchronization. In this work, we provide enhancements to the alignment signal structure and generation, which provide clear improvement in the link performance and reduce the complexity of transmitter processing. Also, new insights about the characteristics of alignment signal based CP-free OFDM waveforms are provided.

In cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM)-based radio access, the coexistence of different technologies without precise time-frequency synchronization is limited due to high out-of-band (OOB) emissions. Therefore, the spectrum enhancement techniques play a key role in relaxing the synchronization and power control requirements. This allows a higher degree of opportunistic spectrum use with minimized interference. In addition, all the transmitting devices have to fulfill specific transmitted signal quality requirements, including the maximum OOB radiated signal power. With the orthogonal frequency-division multiplexing (OFDM)-based radio access, some additional signal processing for improved spectrum containment is commonly needed to achieve these requirements. The filtering and time-domain windowing are two fundamentally different approaches for spectrum enhancement. The filtered OFDM (F-OFDM) provides better spectrum localization than the time-windowing schemes [such as windowed overlap-add (WOLA)], with the cost of higher complexity. This article introduces low-complexity solutions for spectrally enhanced narrowband OFDM transmitters based on the use of lookup tables (LUTs). The proposed LUT approach, requiring only memory units and a low number of additions, allows to avoid all computationally expensive operations in online transmitter processing, as it builds the transmitted signal by summing the stored partial waveforms optimized offline. In certain cases, completely multiplication- and summation-free designs are possible. The transmitters of narrowband Internet of Things (NB-IoT) devices are natural applications for the proposed LUT approach, as they require additional digital baseband signal processing to reach the emission requirements. It is shown that the proposed LUT schemes can provide significant savings in real-time computations of NB-IoT devices, while fulfilling the 3GPP requirements.

Recently, the concept of wake-up radio based access has been considered as an effective power saving mechanism for 5G mobile devices. In this article, the average power consumption of a wake-up radio enabled mobile device is analyzed and modeled by using a semi-Markov process. Building on this, a delay-constrained optimization problem is then formulated, to maximize the device energy-efficiency under given latency requirements, allowing the optimal parameters of the wake-up scheme to be obtained in closed form. The provided numerical results show that, for a given delay requirement, the proposed solution is able to reduce the power consumption by up to 40% compared with an optimized discontinuous reception (DRX) based reference scheme.

Recently, discontinuous reception mechanisms (DRX) and wake-up schemes (WuS) have been proposed to enhance the energy efficiency of 5G mobile devices and prolong the battery lifetime. The existing DRX and WuS use commonly pre-configured parameters that cannot be adjusted dynamically. In this paper, a novel wake-up scheduling (WuSched) concept is introduced to further improve the energy efficiency of WuS-enabled mobile devices while controlling the buffering delay in a dynamic manner. The main idea of WuSched is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically based on actual traffic arrivals. For this purpose, two different optimization approaches of the wake-up scheduling concept are proposed, analyzed, and compared, namely offline and online wake-up schedulers (WuSched-Offline and WuSched-Online). First, the WuSched-Offline is analyzed analytically for Poisson traffic arrivals and optimized (offline) to balance the average delay and power consumption. Second, the WuSched-Online is proposed to take online decisions based on traffic prediction, which is able to deal with general and more complex traffic models. Towards this end, we develop a framework for the prediction of packet arrivals based on recurrent neural networks. Numerical results show that both wake-up schedulers outperform the ordinary WuS-based system where wake-up scheduler is not deployed. In particular, for predefined delay requirements of video streaming, audio streaming, and mixed traffic flow, the WuSched-Online reduces the power consumption of the baseline WuS by up to 36%, 28% and 9%, respectively. Results also show that the WuSched-Offline has slightly better energy efficiency than the WuSched-Online in the case of Poisson packet arrivals, as it is optimized for that, while its power consumption is slightly higher than that of the WuSched-Online scheduler for realistic traffic scenarios.

The limitations of state-of-the-art cellular modems prevent achieving low-power and low-latency Machine Type Communications (MTC) based on current power saving mechanisms alone. Recently, the concept of wake-up scheme has been proposed to enhance battery lifetime of 5G devices, while reducing the buffering delay. The existing wake-up algorithms use static operational parameters that are determined by the radio access network at the start of the user's session. In this paper, the average power consumption of the wake-up enabled MTC TIE is modeled by using a semi-Markov process and then optimized through a delay-constrained optimization problem, by which the optimal wake-up cycle is obtained in closed form. Numerical results show that the proposed solution reduces the power consumption of an optimized Discontinuous Reception (DRX) scheme by up to 40% for a given delay requirement.

With the emergence of new 5G radio networks, high-accuracy positioning solutions are becoming extensively more important for numerous 5G-enabled applications and radio resource management tasks. In this paper, we focus on 5G mm-wave systems, and propose a method for high-accuracy estimation of the User Equipment (UE) position and antenna orientation. Based on the sparsity of the mm-wave channel, we utilize a compressive sensing approach for estimating the departure and arrival angles as well as the time-of-arrival for each observed radio propagation path. After this, in order to obtain statistical descriptions of the unknown parameters, we analytically derive a set of sampling distributions, which enable utilization of an iterative Gibbs sampling method. As shown by the obtained simulation results, the proposed method is able to achieve centimeter-level positioning accuracy with degree-level orientation accuracy, even in the absence of a line-of-sight path.

High accuracy positioning enabled by 5G cellular networks will play a crucial role in the robot-based industrial applications, where the vertical accuracy will be as significant as the 3D accuracy. Aiming at target applications relying on flying robots in industrial environments, this paper presents and formulates two positioning algorithms when the location uncertainty of the access nodes (ANs) is taken into consideration. The first algorithm is a low-complexity geometry-based 3D positioning algorithm that utilizes both time-of-arrival and angle-of-arrival measurements. The second algorithm relies on extended Kalman Filter (EKF)-based positioning, by mapping the ANs' location uncertainty into the measurement noise statistics. The performance of the two proposed method is studied in terms of 3D and vertical positioning accuracy, sensitivity to location uncertainty of the ANs, and computational complexity in indoor scenarios. Based on the conducted complexity analysis, the proposed geometry-based algorithm is computationally more efficient than the EKF-based algorithm. In addition, the proposed geometry-based positioning method demonstrates a higher robustness against a high location uncertainty of ANs than the considered EKF-based method.

Massive Machine Type Communication (mMTC) technologies are key to addressing communication requirements of various emerging IoT applications. In this work, a multisite mMTC test network is designed and implemented in order to investigate the long-term communication quality and sensor data of LoRa and NB-IoT. This is essential for remote network maintenance operations, because it allows distinguishing communication quality defects from software/hardware failures. Moreover, it provides additional information about the communication link quality for network management. The test network is geographically scattered over a large area in Finland and experimented both in private and public networks. Measurements on LoRaWAN test networks revealed that higher SNRs and RSSIs are generally achieved for devices with lower spreading factors. Comparison of NB-IoT and LoRaWAN shows that NB-IoT has better communication quality performance. Overall, this paper provides first long-term multi-site and multi-technology mMTC measurements and corresponding performance analysis that are not available in the existing literature.

This paper presents long term evolution (LTE) uplink measurements taken with two drones operating in a public cellular network in rural environment. Three similar measurement scenarios with drone flight altitudes of 50 m and 100 m above ground level are studied with different measurement software and equipment. Four different key performance indicators (KPIs) are presented in the paper: Physical Resource Block (PRB) usage, Modulation and Coding Scheme (MCS) class, throughput and transmission power. These together help to analyse the overall interference behaviour, which is an essential part of the paper. The results show that aerial UEs add minor interference to the network, which decrease the MCS class of the ground level UEs and slightly increase their transmission power. The resulting data rates are roughly the same as them operating in the same cell because of increased PRB amount per user equipment (UE). This effect is similar to that of a ground level UE switching cell in the cell edge area to another cell. In addition, ground level cell edge area UE performs slightly poorer in comparison with a UE near the serving cell antenna when drones are utilized. Nevertheless, two drones operating in the air with smart phones connected to them do not have a critical effect on the performance of the normal ground level UEs from the throughput point of view, but slightly increase the resource utilization.

In this paper, we exploit the 3D-beamforming features of multiantenna equipment employed in fifth generation (5G) networks, operating in the millimeter wave (mmW) band, for accurate positioning and tracking of users. We consider sequential estimation of users' positions, and propose a two-stage extended Kalman filter (EKF) that is based on reference signal received power (RSRP) measurements. In particular, beamformed downlink (DL) reference signals (RSs) are transmitted by multiple base stations (BSs) and measured by user equipments (UEs) employing receive beamforming. The so-obtained beam-RSRP (BRSRP) measurements are reported to the BSs where the corresponding directions of departure (DoDs) are sequentially estimated by a novel EKF. Such angle estimates from multiple BSs are subsequently fused on a central entity into 3D position estimates of UEs by means of another (second-stage) EKF. The proposed positioning scheme is scalable since the computational burden is shared among different network entities, namely transmission/reception points (TRPs) and 5G-NR Node B (gNB), and may be accomplished with the signalling currently specified for 5G. We assess the performance of the proposed algorithm on a realistic outdoor 5G deployment with a detailed ray tracing propagation model based on the METIS Madrid map. Numerical results with a system operating at 39 GHz show that sub-meter 3D positioning accuracy is achievable in future mmW 5G networks.

Low-power and low-latency communication features are vital to extend 5G mobile devices functionalities beyond those of the current networks, and to introduce innovative services and applications. On the other hand, limitations of state-of-the-art cellular modules prevent designing and facilitating such features based on current power saving mechanisms alone. In this paper, a new wake-up signaling for 5G control plane is introduced, aiming to reduce energy consumption of cellular module in downlink. Performance of the proposed scheme in terms of false alarm and misdetection rates are investigated and evaluated. The obtained numerical results show that such a signaling can reduce power consumption of discontinuous reception (DRX) by up to 30%, at the cost of negligible increase in signaling overhead.

Mobile device batteries have severely limited capacities, due to constraints on size and weight of mobile devices. Therefore, energy efficiency of mobile devices plays an important role in their usability. Different power measurement studies show that cellular subsystem of mobile device is one of the major contributors to its energy consumption. Thus, reducing energy consumption of cellular subsystem is paramount. In this paper, a new concept of pre-grant message for control plane is introduced, aiming to reduce energy consumption of cellular subsystem in downlink, while discontinuous reception is activated. Performance of the proposed scheme in terms of false alarm and misdetection are investigated, and evaluated in AWGN and Rayleigh fading channel. The obtained numerical results show that such a signaling can reduce power consumption of mobile device by up to 70% for bursty data applications, at the cost of negligible increments in signaling overhead.

This paper considers direction-finding in millimeter wave (mmW) fifth generation (5G) networks by means of beam-based downlink (DL) reference signal received power (RSRP) measurements and subsequent reporting. In particular, we propose two methods that allow user equipments (UEs) to select, in an independent and dynamic manner, the most-relevant beam-RSRP (BRSRP) measurements as a trade-off between angle-related information and load of the feedback channel. A likelihood ratio (LR)-test is derived in which the hypothesis for “noise-only” BRSRP measurement is compared to that of “reference signal (RS)-plus-noise” observations, under a given significance level. A power threshold based method is also proposed in which the BRSRP measurements are compared to a threshold proportional to the noise power. Such a noise variance is estimated at each UE independently. The performance of the proposed beam selection schemes is assessed by means of an extended Kalman filter (EKF) tracking the direction of departure (DoD) of the line-of-sight (LoS) path between base stations (BSs) and a UE. Extensive numerical results are provided on a realistic mmW 5G outdoor deployment scenario operating at 39 GHz and with a ray-tracing propagation model based on the METIS Madrid grid.

This paper describes implementation details of a digital-intensive phase modulator architecture that does not require a frequency synthesizer to cover a wide carrier frequency range. The phase modulator operation is based on toggling the output accurately during the sample period to generate the phase-modulated signal. The toggling instants within the sample period are calculated by DSP solvers that utilize linear interpolation. The interpolation effectively multiplies the phase signal sample rate by the modulator phase resolution, which enables wider signal bandwidth and a completely digital method of defining the transmitter carrier frequency. The phase modulator concept is verified by implementing it as a part of an outphasing transmitter in 28 nm CMOS. With a constant sample rate of 1.5 GHz and without any predistortion, the transmitter achieves better than -28 dBc ACLR with 100 MHz aggregated LTE downlink signal between 0.8-2.0 GHz carrier frequency.

This paper examines one source of spectral degradation in polar and multilevel outphasing transmitters. The degradation is caused by the amplitude signal appearing at the transmitter output as a baseband component, in addition to the desired RF signal. This baseband component contains sampling images and quantization noise across the spectrum. Thus, it adds noise at the signal band where it cannot be filtered and limits the achievable ACLR, particularly in wideband LTE and 5G systems. We analyze the origin of this phenomenon and related effects of system and signal parameters, and propose three design solutions for eliminating or alleviating the problem. Our analysis and simulations demonstrate that using a voltage-subtracting power combiner cancels the described degradation, potentially leading to significant improvement in spectral performance.

Due to non-negligible amount of energy consumption of state-of-the-art small cells at idle mode, energy efficiency of the overall network may decrease with densification. Therefore, energy efficiency in ultra-dense networks (UDNs) is one of the key challenges for mobile network operators (MNOs) to reduce their operative expenditure (OPEX), and to mitigate the carbon footprint. Low-power and low-cost dense networks are vital to extend next generation cellular network functionalities by improving network capacity in hotspot areas, and to deploy networks in short time periods. In energy harvesting networks, access points (APs) may perform both backhaul and access link data communication, simultaneously, removing UDN dependency on optical distribution network and electrical grid. In this paper, different power modes and essential signaling for operation of such APs are introduced, aiming to reduce energy consumption of UDNs by integrating energy harvesters into APs, equipped with wireless backhaul.

Novel composite fading models were recently proposed based on inverse gamma distributed shadowing conditions. These models were extensively shown to provide remarkable modeling of the simultaneous occurrence of multipath fading and shadowing phenomena in emerging wireless scenarios such as cellular, off-body and vehicle-to-vehicle communications. Furthermore, the algebraic representation of these models is rather tractable, which renders them convenient to handle both analytically and numerically. Based on this, the present contribution analyzes the ergodic capacity over the recently proposed $\kappa-\mu$ / inverse gamma composite fading channels, which were shown to characterize excellently multipath fading and shadowing in line-of-sight communication scenarios, including realistic vehicular communications. Novel analytic expressions are derived which are subsequently used in the analysis of the corresponding system performance. In this context, the offered results are compared with respective results from cases assuming conventional fading conditions, which leads to the development of numerous insights on the effect of the multipath fading and shadowing severity on the achieved capacity levels. It is expected that these results will be useful in the design of timely and demanding wireless technologies such as wearable, cellular and inter-vehicular communications.

In this article, the adoption and performance of a constant envelope (CE) type spatial precoder is addressed in large-scale multiuser MIMO based cellular network. We first formulate an efficient computing solution to obtain the antenna samples of such CE precoder. We then evaluate the achievable CE precoder based multiuser downlink (DL) system performance and compare it with the corresponding performance of more ordinary zero-forcing (ZF) spatial precoder. We specifically also analyze how realistic highly nonlinear power amplifiers (PAs) affect the achievable DL performance, as the individual PA units in largearray or massive MIMO systems are expected to be small, cheap and operating close to saturation for increased energy-efficiency purposes. It is shown that the largely reduced peak-to-average power ratio (PAPR) of the PA input signals in the CE precoder based system allows for pushing the PA units harsher towards saturation, while allowing to reach higher signal-to-interference-plus-noise ratio (SINRs) at the intended receivers compared to the classical ZF precoder based system. The obtained results indicate that the CE precoder can outperform the ZF precoder by up to 5-6 dBs, in terms of the achievable SINRs, when the PA units are pushed towards their saturating region. Such large gains are a substantial benefit when seeking to improve the spectral and energy-efficiencies of the mobile cellular networks.

The expected fifth generation (5G) networks allow for highly accurate direction of arrival and time of arrival (ToA) estimation, thus providing a convenient environment for device positioning, if designed properly. However, utilizing ToA measurements for positioning requires a tight synchronization not only between the target devices but also among the network elements. In this paper, we propose a joint positioning and synchronization solution building on the premises of the envisioned cmWave-based 5G ultra-dense networks and time-varying clock models. In addition to device location estimates, also relative clock offsets and skews are estimated and tracked within the proposed extended Kalman filter based solutions, which can be further used by a network operator in synchronizing the active network elements and devices within the network. Based on extensive simulations and numerical evaluations, accurate positioning performance can be achieved while tracking the clock parameters under time-varying clock errors.

This paper investigates the 5G new radio (NR) uplink (UL) performance with CP-OFDM and DFT-s-OFDM based waveforms. The effects of highly non-linear PA behavior, inter-allocation interference, and UL multi-user MIMO on the relative performance of these waveforms are addressed. It is shown that with relaxed EVM and inband emission requirements for CP-OFDM, the coverage limited operation can be improved to achieve better link budget than DFT-s-OFDM based UL without performance penalty in the multi-user uplink. For high-throughput user equipment, the assumed highly non-linear PA behavior restricts the CP-OFDM based transmit power, which may limit the coverage compared to DFT-s-OFDM based waveform in UL without multi-user MIMO support. In UL multi-user MIMO scenario the CP-OFDM based waveform provides clearly better link performance and achieves better link budget than DFT-s-OFDM based waveform. Within the multi-user MIMO UL scenario, the requirement for substantially better PA linearity in high-throughput cases is also observed.

By the introduction of the fifth generation (5G) mobile communication networks and its physical layer entitled as new radio (NR), the question of link performance in coexistence scenario between the 5G NR and fourth generation (4G) mobile communication networks based on long term evolution (LTE) has been raised. In this paper, we evaluate the uplink (UL) performance of 5G NR and LTE links operating within a common channel. The need for subcarrier shift in 5G NR UL similar to LTE UL is addressed and analyzed, and the effect of guard band (GB) in frequency domain between 5G NR and LTE is studied. It is shown that with a single physical resource block GB no subcarrier shifting is required in 5G NR, as long as the power control accuracy is sufficient, in which case LTE performance is unaffected. From the 5G NR performance point of view no GB is required.

This paper studies the performance of Received Signal Strength (RSS)-based fingerprinting positioning methods under different attack scenarios. We discuss different attack models and we compare the accuracy of a commonly used RSS fingerprinting algorithm with a robust version relying on access node visibility, with respect to those attacks. The results show that the robust fingerprinting method outperforms the traditional fingerprinting method for a particular group of attacks, for two attack types the accuracy improvement, in terms of Root Mean Square Error (RMSE), can yield factor two. RSS-based fingerprinting methods are most susceptible to jamming of access nodes and least vulnerable to random removal of access nodes.

Energy-constrained wireless networks and devices are mainly powered by batteries, which have severely limited capacities, demanding to be regularly recharged or replaced. Thus energy conservation plays a pivotal role in the operational lifetime of such networks. In this paper, the concept of a wireless powered wake-up receiver is studied, aiming to reduce energy consumption of the wireless node. The proposed wireless powered wake-up receiver scheme can be utilized for a range of energy-constrained wireless applications such as wireless sensor actuator networks, machine-to-machine communications, and the Internet-of-Things. Preliminary numerical results show that such a scheme can reduce energy consumption of wireless nodes considerably, at the cost of an extra low-power low-cost wake-up receiver.

The η-μ / inverse gamma and κ-μ / inverse gamma distributions were recently introduced as particularly flexible and tractable composite fading models that provide accurate characterization of multipath and shadowing effects, which are encountered simultaneously during wireless transmission in emerging communication scenarios such as off-body, cellular and vehicular-to-vehicular communications. The present contribution analyzes the symbol error rate performance of digital communications over these fading channels. To this end, we derive novel analytic expressions for the symbol error rate of multiple amplitude based modulated systems under these fading conditions, which are subsequently used in the analysis of the corresponding system performance. In this context, numerous insights are developed on the effect of different fading conditions on the corresponding error rate, which are expected to be useful in the design of timely and demanding wireless technologies such as wearable, cellular and vehicular communication systems.

Future cellular systems are expected to revolutionize today's industrial ecosystem by satisfying the stringent requirements of ultra-high reliability and extremely low latency. Along these lines, the core technology to support the next-generation factory automation deployments is the use of millimeter-wave (mmWave) communication that operates at extremely high frequencies (i.e., from 10 to 100 GHz). However, characterizing the radio propagation behavior in realistic factory environments is challenging due to shorter mmWave wavelengths, which make channel properties be sensitive to the actual topology and size of the surrounding objects. For these reasons, this paper studies the important mmWave channel properties for two distinct types of factories, namely, light industry and heavy industry. These represent the extreme cases of factory classification based on the level of technology, the density and the size of the equipment, and the goods produced. Accordingly, we assess the candidate mmWave frequencies of 28 and 60 GHz for licensed-and unlicensed-band communication, respectively. After analyzing the signal propagation (e.g., in terms of path loss) and the line-of-sight (LoS) probability, our understanding is that in a factory automation environment the presence of metallic equipment and various objects produces many dissimilarities in the mmWave channel properties, thus making them difficult to describe with conventional empirical or stochastic models. Our findings suggest that the deployment of the practical mmWave systems in indoor industrial environments should not therefore rely on past propagation studies available in the literature blindly but might take into account more accurate and reliable evaluation of the environment that is possible with ray-based simulations.

We study positioning of high-speed trains in 5G new radio (NR) networks by utilizing specific NR synchronization signals. The studies are based on simulations with 3GPP-specified radio channel models including path loss, shadowing and fast fading effects. The considered positioning approach exploits measurement of Time-Of-Arrival (TOA) and Angle-Of-Departure (AOD), which are estimated from beamformed NR synchronization signals. Based on the given measurements and the assumed train movement model, the train position is tracked by using an Extended Kalman Filter (EKF), which is able to handle the non-linear relationship between the TOA and AOD measurements, and the estimated train position parameters. It is shown that in the considered scenario the TOA measurements are able to achieve better accuracy compared to the AOD measurements. However, as shown by the results, the best tracking performance is achieved, when both of the measurements are considered. In this case, a very high, sub-meter, tracking accuracy can be achieved for most (>75%) of the tracking time, thus achieving the positioning accuracy requirements envisioned for the 5G NR. The pursued high-accuracy and high-availability positioning technology is considered to be in a key role in several envisioned HST use cases, such as mission-critical autonomous train systems.

This paper analyzes the performance of linearly precoded time division duplex based multi-user massive MIMO downlink system under joint impacts of channel non-reciprocity (NRC) and imperfect channel state information. We consider a generic and realistic NRC model that accounts for transceiver frequency-response as well as mutual coupling mismatches at both user equipment (UE) and base station (BS) sides. The analysis covers two most prominent forms of linear precoding schemes, namely, zero-forcing (ZF) and maximum-ratio transmission (MRT), and assumes that only the statistical properties of the beamformed channel are used at the UE side to decode the received signal. Under the approximation of i.i.d. Gaussian channels, closed-form analytical expressions are derived for the effective signal to interference and noise ratios (SINRs) and the corresponding capacity lower bounds. The expressions show that, in moderate to high SNR, the additional interference caused by imperfect NRC calibration can degrade the performance of both precoders significantly. Moreover, ZF is shown to be more sensitive to NRC than MRT. Numerical evaluations with practical NRC levels indicate that this performance loss in the spectral efficiency can be as high as 42% for ZF, whereas it is typically less than 13% for MRT. It is also shown that due to the NRC, the asymptotic large-antenna performance of both precoders saturate to an identical finite level. The derived analytical expressions provide useful tools and valuable technical insight, e.g., into calculating the NRC calibration requirements in BSs and UEs for any given specific performance targets in terms of effective SINR or the system capacity bound.

Large databases with Received Signal Strength (RSS) measurements are essential for various use cases in mobile wireless communications and navigation, including radio resource management algorithms and network-based localization. Because of the constantly increasing number of radio transmitters with various wireless technologies and with the advent of 5G cloud computing and Internet of Things (IoT), the required size of the RSS databases are becoming unmanageably large. Thus, the requirements for the bandwidth and data rates for accessing the memory might become too costly. Therefore, in order to reduce the size of the RSS database, while maintaining the data quality, we have previously proposed the method of spectrally compressed RSS images, which are able to achieve considerable data compression of up to 70 percent. In this paper, we deeply analyze the process of spectral compression and introduce error sources, which affect the compression performance. Based on the analysis, we propose a novel theoretical framework and methods to optimize the performance of the spectral compression. In addition, we derive the Cramer-Rao Lower Bound (CRLB) for the RSS-based localization error and compare the CRLB between separate baseline localization approaches. The theoretical analysis is justified and compared with experimental RSS measurements taken from several multi-storey buildings.

We analyze an inband full-duplex access node that is serving mobile users while simultaneously connecting to a core network over a wireless backhaul link, utilizing the same frequency band for all communication tasks. Such wireless self-backhauling is an intriguing option for the next generation wireless systems since a wired backhaul connection might not be economically viable if the access nodes are deployed densely. In particular, we derive the optimal transmit power allocation for such a system in closed form under quality-of-service (QoS) requirements, which are defined in terms of the minimum data rates for each mobile user. For comparison, the optimal transmit power allocation is solved also for two reference scenarios: a purely half-duplex access node, and a relay-type full-duplex access node. Based on the obtained expressions for the optimal transmit powers, we then show that the systems utilizing a full-duplex capable access node have a fundamental feasibility boundary, meaning that there are circumstances under which the QoS requirements cannot be fulfilled using finite transmit powers. This fundamental feasibility boundary is also derived in closed form. The feasibility boundaries and optimal transmit powers are then numerically evaluated in order to compare the different communication schemes. In general, utilizing the purely full-duplex access node results in the lowest transmit powers for all the communicating parties, although there are some network geometries under which such a system is not capable of reaching the required minimum data rates. In addition, the numerical results indicate that a full-duplex capable access node is best suited for relatively small cells.

A novel reduced-complexity digital predistortion (DPD) solution is presented in this paper. The proposed DPD can suppress the unwanted distortions due to power amplifier (PA) nonlinearity and I/Q modulator impairments in direct conversion transmitters using reduced-bandwidth filtered basis functions. Moreover, the DPD parameter estimation is based on very simple decorrelation based closed-loop processing and reduced-bandwidth observation, thus further reducing the overall complexity. The proposed DPD can be used in large array or massive MIMO systems with large number of radio transceivers and PAs, where reducing the complexity of the DPD processing is very critical.

In this paper, a self-backhauling radio access system is studied and analyzed. In particular, we consider a scenario where a full-duplex access node is serving mobile users simultaneously in uplink and downlink, while also maintaining a wireless backhaul connection. The full-duplex capability of the access node, together with large antenna arrays, allows it to do all of this using the same center frequency. The minimum transmit powers for such a system are solved in a closed form under the condition that certain Quality of Service (QoS) requirements, defined in terms of minimum uplink and downlink data rates, are fulfilled. It is demonstrated with numerical results that, by using the derived expressions for the optimal transmit powers, the probability of fulfilling the QoS requirements is greatly increased, while simultaneously the overall transmit power usage of the system is significantly reduced when compared to a benchmark scheme.

The upcoming fifth generation (5G) radio networks will be the game changer of future societies. In addition to obvious improvements in wireless communications, 5G enables also highly accurate user equipment (UE) positioning that is carried out on the network side. Such a solution provides ubiquitous positioning services without draining the batteries of the UEs. In this paper, we concentrate on positioning methods that suits the future needs of automotive transportation and intelligent transportation system (ITS). In particular, we demonstrate how the location estimates can be obtained in 5G ultra-dense networks (UDNs) efficiently and even in a proactive manner where the UE locations can be predicted to some extent. Numerical performance analysis will then illustrate that the proposed 5G-based network-centric positioning solutions are well-suited for car and traffic applications, providing even sub-meter range positioning accuracy.

For the 5G new radio physical layer the CP-OFDM waveform has been chosen as the baseline for communications below 40 GHz. The requirement for multicarrier waveforms used for uplink is to achieve similar coverage as achieved by SC-FDMA in LTE uplink. In this paper, multiple candidate waveforms with enhanced CP-OFDM processing proposed for 5G incorporating realistic 3GPP compliant power amplifier model and peak clipping are evaluated in uplink transmission, and compared against SC-FDMA in terms of maximum average power amplifier output power and coded block error rate. It is shown that multicarrier waveforms have minor disadvantage in single-PRB transmission, but as the allocation size increases to encounter frequency selective fading the multicarrier waveforms provide similar or even improved link budget compared to SC-FDMA uplink. This implies that given the expected cell edge throughput requirements for 5G mobile broadband services and expected power amplifier development, enhanced CP-OFDM waveforms can achieve the uplink coverage requirement.

In this paper, subband filtered CP-OFDM and windowed CP-OFDM waveforms proposed for 5G new radio (NR) are compared to LTE-like downlink and uplink waveforms under different power amplifier (PA) models. The effects of subband filtering or windowing are evaluated in terms of out-of-band and inband emissions, average error vector magnitude (EVM) and maximum uplink PA output power when increasing the bandwidth efficiency from 90% to 97.2%. The evaluations include recently proposed PA models by 3GPP TSG-RAN WG1 for downlink and uplink. It is shown that the cost of increased bandwidth efficiency in fullband downlink transmission is mainly complexity increase required by the steeper channelization filter. On the other hand, the increased EVM caused by channel filter induced inter-symbol-interference may limit the usability of modulation 256-QAM and above. In uplink with fullband transmission using 64-QAM modulation, the spectral containment is not an issue because the required backoff with highly non-linear PA model limits the Tx power to a very low level independently of the maximum allocation size. In the 1 PRB uplink, increased bandwidth utilization decreases the maximum PA output power while increased backoff improves the inband ACLR.

Cognitive radio (CR) is considered an important solution to the current spectral scarcity, which is expected to be a significant issue in the next generation of wireless communication systems, namely, 5G. Wideband spectrum sharing and sensing constitute highly desirable features of CR systems as they aim to increase the probability of identifying available spectral bands, which ensures a more efficient resource utilization. This paper proposes an efficient frequency-domain cyclic prefix autocorrelation-based wideband spectrum sensing and sharing method that can provide accurate detection of orthogonal frequency-division multiplexing (OFDM)-based primaries in wideband CR systems. Novel analytic expressions are derived for the corresponding threshold, probability of false alarm, and probability of detection in the presence of noise uncertainty (NU) and frequency selectivity. The derived models are validated by extensive comparisons with respective results from computer simulations. It is demonstrated that the introduced autocorrelation-based sensing method is able to counteract NU and the frequency-selective multipath channel effects in realistic wideband communication scenarios. Furthermore, the method facilitates partial band sensing, allowing the sensing of weak OFDM-type primary user (PU) signals in channels, which are partly overlapped by other strong PU or CR transmissions. This is considered a crucial element in practical spectrum sharing scenarios. Since the proposed sensing method makes use of sparsity in the spectral domain, it can be technically considered as compressed sensing method. The flexibility of this approach supports robust wideband multi-mode, multi-channel sensing with low complexity. Finally, it is shown that the offered results are particularly useful in the context of spectrum sharing as their high performance and reduced complexity can enable the co-existence of non-exhaustive yet highly efficient algorithms.

This paper investigates the application of fast-convolution (FC) filtering schemes for flexible and effective waveform generation and processing in the fifth generation (5G) systems. FC-based filtering is presented as a generic multimode waveform processing engine while, following the progress of 5G new radio standardization in the Third-Generation Partnership Project, the main focus is on efficient generation and processing of subband-filtered cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM) signals. First, a matrix model for analyzing FC filter processing responses is presented and used for designing optimized multiplexing of filtered groups of CP-OFDM physical resource blocks (PRBs) in a spectrally well-localized manner, i.e., with narrow guardbands. Subband filtering is able to suppress interference leakage between adjacent subbands, thus supporting independent waveform parametrization and different numerologies for different groups of PRBs, as well as asynchronous multiuser operation in uplink. These are central ingredients in the 5G waveform developments, particularly at sub-6-GHz bands. The FC filter optimization criterion is passband error vector magnitude minimization subject to a given subband band-limitation constraint. Optimized designs with different guardband widths, PRB group sizes, and essential design parameters are compared in terms of interference levels and implementation complexity. Finally, extensive coded 5G radio link simulation results are presented to compare the proposed approach with other subband-filtered CP-OFDM schemes and time-domain windowing methods, considering cases with different numerologies or asynchronous transmissions in adjacent subbands. Also the feasibility of using independent transmitter and receiver processing for CP-OFDM spectrum control is demonstrated

In this paper, we maximize the energy efficiency (EE) of full-duplex (FD) two-way relay (TWR) systems under non-ideal power amplifiers (PAs) and non-negligible transmission-dependent circuit power. We start with the case where only the relay operates full duplex and two timeslots are required for TWR. Then, we extend to the advanced case, where the relay and the two nodes all operate full duplex, and accomplish TWR in a single timeslot. In both cases, we establish the intrinsic connections between the optimal transmit powers and durations, based on which the original non-convex EE maximization can be convexified and optimally solved. Simulations show the superiority of FD-TWR in terms of EE, especially when traffic demand is high. The simulations also reveal that the maximum EE of FD-TWR is more sensitive to the PA efficiency, than it is to self-cancellation. The full FD design of FD-TWR is susceptible to traffic imbalance, while the design with only the relay operating in the FD mode exhibits strong tolerance.

In this paper, we address the prospects and key enabling technologies for highly efficient and accurate device positioning and tracking in fifth generation (5G) radio access networks. Building on the premises of ultra-dense networks as well as on the adoption of multicarrier waveforms and antenna arrays in the access nodes (ANs), we first formulate extended Kalman filter (EKF)-based solutions for computationally efficient joint estimation and tracking of the time of arrival (ToA) and direction of arrival (DoA) of the user nodes (UNs) using uplink reference signals. Then, a second EKF stage is proposed in order to fuse the individual DoA and ToA estimates from one or several ANs into a UN position estimate. Since all the processing takes place at the network side, the computing complexity and energy consumption at the UN side are kept to a minimum. The cascaded EKFs proposed in this article also take into account the unavoidable relative clock offsets between UNs and ANs, such that reliable clock synchronization of the access-link is obtained as a valuable by-product. The proposed cascaded EKF scheme is then revised and extended to more general and challenging scenarios where not only the UNs have clock offsets against the network time, but also the ANs themselves are not mutually synchronized in time. Finally, comprehensive performance evaluations of the proposed solutions on a realistic 5G network setup, building on the METIS project based outdoor Madrid map model together with complete ray tracing based propagation modeling, are provided. The obtained results clearly demonstrate that by using the developed methods, sub-meter scale positioning and tracking accuracy of moving devices is indeed technically feasible in future 5G radio access networks operating at sub-6 GHz frequencies, despite the realistic assumptions related to clock offsets and potentially even under unsynchronized network elements.

This paper is devoted to the end-to-end performance analysis, optimal power allocation (OPA), and energy-efficiency (EE) optimization of decode-and-forward (DF)-based full-duplex relaying (FDR) and half-duplex relaying (HDR) systems. Unlike existing analyses and works that assume simplified transmission over symmetric fading channels, we consider the more realistic case of asymmetric multipath fading and shadowing conditions. To this end, exact and asymptotic analytic expressions are first derived for the end-to-end outage probabilities (OPs) of the considered DF-FDR set ups. Based on these expressions, we then formulate the OPA and EE optimization problems under given end-to-end target OP and maximum total transmit power constraints. It is shown that OP in FDR systems is highly dependent upon the different fading parameters and that OPA provides substantial performance gains, particularly, when the relay self-interference (SI) level is strong. Finally, the FDR is shown to be more energy-efficient than its HDR counterpart, as energy savings beyond 50% are feasible even for moderate values of the SI levels, especially at larger link distances, under given total transmit power constraints and OP requirements.

In this paper, we develop new models and methods for exploring multidimensional design spaces associated with digital predistortion (DPD) systems. DPD systems are important components for power amplifier linearization in wireless communication transceivers. In contrast to conventional DPD implementation methods, which are focused on optimizing a single objective — most commonly, the adjacent channel power ratio (ACPR) — without systematically taking into account other relevant metrics, we consider DPD system implementation in a multiobjective optimization context. In our targeted multiobjective context, trade-offs among power consumption and multiple DPD performance metrics are jointly optimized subject to performance constraints imposed by the given modulation scheme. Through synthesis and simulation results, we demonstrate that DPD systems derived through our design space exploration techniques exhibit significantly improved trade-offs among multidimensional implementation criteria, including energy consumption, ACPR, and symbol error-rate. Additionally, we perform experiments using three different LTE modulation schemes, and we demonstrate that our multiobjective optimization approach significantly enhances system adaptivity in response to changes in the employed modulation scheme.

In high-density low-bitrate Internet-of-Things (IoT) use case of 5G networks, the terminals and sensors are to be of extremely low-cost and low energy-consuming. Typically, the analog-to-digital converters (ADCs) dominate the power-budget of receiver chains, in particular if the quantization resolution is high. Hence, receiver architectures deploying 1-bit ADCs are of high interest towards realizing low-cost, high energy-efficiency device solutions. In this paper, we study the waveform design and optimization for a narrowband low-bitrate massive MISO downlink targeting to achieve rates higher than 1 bits/sec (per real-dimension) where the terminal receivers adopt only simple 1-bit quantization (per real-dimension) with oversampling. In this respect, first we show that for a particular precoder structure, the overall link is equivalent to that of an AWGN SISO with controlled intersymbol interference (ISI). The filter design problem for generating the desired ISI in such SISO links has been studied in previous works, however, the only known method in literature is a computationally demanding brute force search method. As a novel contribution, we develop models and tools that elaborate on the conditions to be satisfied for unique detection and existence of solution for the filter coefficients. Then, as a concrete example, the developed models and tools are utilized to show that in the absence of noise, five-times oversampling is required for unique detection of 16-QAM input alphabet. Building on these findings, we then develop novel algorithms that can efficiently design the filter coefficients. Examples and simulations are provided to elaborate on filter coefficient design and optimization, and to illustrate good SER performance of the MISO link with 1-bit receiver even at SNRs down to 5 dB.

In this paper, we present a class D power amplifier (PA) design in 28 nm CMOS for a multilevel outphasing transmitter. For increased output power, the design consists of eight unit PAs with cascoded output stages. In order to improve back-off efficiency from conventional outphasing, the PAs are switched on and off in pairs for different amplitude levels, which is challenging to implement with cascoded class D. As a solution, we introduce a new on/off switching method based on logic gates utilizing two square wave voltages to produce either a similar square wave or a constant voltage. This method enables a higher level of integration by using low-voltage digital signals for on/off control, while eliminating the timing mismatch between output transistors caused by a level shifter. The simulated peak output power of the PA is 32.4 dBm, and its peak efficiency is 34.1%.

Energy-efficient, reliable and scalable machine-to-machine (M2M) communications is the key technical enabler of Internet-of-Things (IoT) networks. Furthermore, as the number of populated devices is constantly increasing, self-sustaining or energy-autonomous IoT nodes are a promising prospect receiving increasing interest. In this paper, the feasibility and fundamental limits of energy harvesting based M2M communication systems are studied and presented. The derived theoretical bounds are effectively based on the Shannon theorem, combined with selected propagation loss models, assumed link nonidealities, as well as the given energy harvesting and storage capabilities. Fundamental limits and available operational time of the communicating nodes are derived and analyzed, together with extensive numerical results evaluated in different practical scenarios for low power sensor type communication applications.

Cognitive radio (CR) has received increasing attention and is considered an important solution to the spectral crowding problem. The main idea behind CR technology is to utilize the unused spectral resources which are determined to be available for secondary user by effective spectrum sensing techniques. However, CR technology significantly depends on the spectrum sensing techniques which are applied to detect the presence of primary user (PU) signals. This paper focuses on detecting OFDM primaries using novel frequency-domain cyclic prefix (CP) autocorrelation based compressive spectrum sensing algorithms. To counteract the practical wireless channel effects, frequency domain approaches for PU signal detection are developed. The proposed spectrum sensing method eliminates the effects of both noise uncertainty and frequency selective channels. Using the frequency domain autocorrelation approach results in highly increased flexibility, facilitating robust wideband multi-mode, multi-channel sensing with low complexity. It also allows to sense weak PU signals which are partly overlapped by other strong PU or CR transmissions.

In this paper we consider transmit (Tx) and receive (Rx) beamforming schemes based on the location of the device. In particular, we propose a design methodology for the Tx/Rx beamforming weight-vectors that is based on the departure and arrival angles of the line-of-sight (LoS) path between access-nodes (ANds) and user-nodes (UNds). A network-centric extended Kalman filter (EKF) is also proposed for estimating and tracking the directional parameters needed for designing the Tx and Rx beamforming weights. The proposed approach is particularly useful in 5G ultra-dense networks (UDNs) since the high-probability of LoS condition makes it possible to design geometric beams at both Tx and Rx in order to increase the signal-to-interference-plus-noise ratio (SINR). Moreover, relying on the location of the UNd relative to the ANds makes it possible to replace fullband uplink (UL) reference signals, commonly employed for acquiring the channel-state-information-at-transmitter (CSIT) in time-division-duplex (TDD) systems, by narrowband UL pilots. Also, employing the EKF for tracking the double-directional parameters of the LoS-path allows one to reduce the rate at which UL reference signals are transmitted. Consequently, savings in terms of time-frequency resources are achieved compared to beamforming schemes based on full-band CSI. Extensive numerical results are included using a realistic ray-tracing based system-level simulator in ultra-dense 5G network context. Results show that position based beamforming schemes outperform those based on full-band CSI in terms of mean user-throughput even for highly mobile users.

Energy detection has been for long constituting the most popular sensing method in RADAR and cognitive radio systems. The present paper investigates the sensing behaviour of an energy detector over Hoyt fading channels, which have been extensively shown to provide rather accurate characterization of enriched multipath fading conditions. To this end, a simple series representation and an exact closed-form expression are firstly derived for the corresponding average probability of detection for the conventional single-channel communication scenario. These expressions are subsequently employed in deriving novel analytic results for the case of both collaborative detection and square-law selection diversity reception. The derived expressions have a relatively tractable algebraic representation which renders them convenient to handle both analytically and numerically. As a result, they can be utilized in quantifying the effect of fading in energy detection based spectrum sensing and in the determination of the trade-offs between sensing performance and energy efficiency in cognitive radio communications. Based on this, it is shown that the performance of the energy detector depends highly on the severity of fading as even slight variations of the fading conditions affect the value of the average probability of detection. It is also clearly shown that the detection performance improves substantially as the number of branches or collaborating users increase. This improvement is substantial in both moderate and severe fading conditions and can practically provide full compensation for the latter cases.

Forthcoming fifth generation (5G) mobile networks need to support a wide variety of services, from mobile broadband (MBB) communications supporting up to 100 Gbps throughput and millisecond class latency to ultra-low power machine-to-machine (M2M) communications supporting very low bit rates and tens of years operation with single battery or operation together with new energy harvesting solutions. In this paper, we concentrate on the MBB aspect and consider two new OQAM-OFDM based waveform candidates proposed for 5G communications while noting the strict time latency requirements. Also, we compare different preamble designs used for channel estimation and show that some of the pseudo-pilot power maximizing interference approximation method based preamble designs are not suitable for communications with any nonlinear power amplifier (PA) model and that an ad-hoc random OQAM based preamble design performs well compared to specific optimized preamble designs.

The present work quantifies the effects of asymmetric fading conditions on differentially modulated amplify-and-forward relaying systems. To this end, novel bit error rate expressions are derived for the case that the source-relay and relay-destination links experience non-line-of-sight multipath fading whilst the source-destination link is subject to: multipath fading, shadowing, and composite fading. Simple and tight approximate and asymptotic expressions are also derived, leading to useful insights into the system design. It is shown that the incurred performance variations range from one to few orders of magnitude compared to the standard case of symmetric Rayleigh scenarios, which verifies the importance to account for fading conditions realistically. In addition, differential phase-shift keying is shown to provide adequate performance in severe fading conditions in the moderate and high-signal-to-noise ratio regimes.

Cooperative communication has been shown to provide significant increase of transmission reliability and network capacity while expanding coverage in cellular networks. The present work is devoted to the investigation of the end-to-end performance and power allocation of a maximum-ratio-combining based regenerative multi-relay cooperative network over non-homogeneous scattering environment, which is the realistic case in many practical wireless communication scenarios. Novel analytic expressions are derived for the end-to-end symbol-error-rate of both M-ary phase-shift keying and M-ary quadrature amplitude modulation over independent and non-identically distributed generalized fading channels are given by exact analytic expressions that involve the Lauricella function and can be readily evaluated with the aid of a proposed computing algorithm. Simple analytic expressions are also derived for the corresponding symbol-error-rate at asymptotically high signal-to-noise ratios. The derived expressions are corroborated with respective results from computer simulations and are subsequently employed in formulating a sum-power optimization problem that enhances the system performance under total sum-power constraint within the multi-relay cooperative system. It is also shown that asymptotically optimum power allocation provides substantial performance gains over the corresponding equal power allocation, particularly, when the source-relay and relay-destination paths are highly unbalanced.

This paper addresses receiver (RX) signal processing in multiuser multiple-input multiple-output (MU-MIMO) systems. We focus on uplink orthogonal frequency-division multiple access (OFDMA)-based MU-MIMO communications under in-phase/quadrature (I/Q) imbalance in the associated radio frequency electronics. It is shown in the existing literature that transceiver I/Q imbalances cause cross-talk of mirror-subcarriers in OFDM systems. As opposed to typically reported single-user studies, we extend the studies to OFDMA-based MU-MIMO communications, with simultaneous user multiplexing in both frequency and spatial domains, and incorporate also external interference from multiple sources at RX input, for modeling challenging conditions in increasingly popular heterogeneous networks. In the signal processing developments, we exploit the augmented subcarrier processing, which processes each subcarrier jointly with its counterpart at the image subcarrier, and jointly across all RX antennas. Furthermore, we derive an optimal augmented linear RX in terms of minimizing the mean-squared error. The novel approach integrates the I/Q imbalance mitigation, external interference suppression, and data stream separation of multiple UEs into a single processing stage, thus avoiding separate transceiver calibration. Extensive analysis and numerical results show the signal-to-interference-plus-noise ratio (SINR) and symbol-error rate (SER) behavior of an arbitrary data stream after RX spatial processing as a function of different system and impairment parameters. Based on the results, the performance of the conventional per-subcarrier processing is heavily limited under transceiver I/Q imbalances, and is particularly sensitive to external interferers, whereas the proposed augmented subcarrier processing provides a high-performance signal processing solution being able to detect the signals of different users as well as suppress the external interference efficiently. Finally, we also extend the studies to massive MIMO framework, with very large antenna systems. It is shown that, despite the huge number of RX antennas, the conventional linear processing methods still suffer heavily from I/Q imbalances while the augmented approach does not have such limitations.

The present work is devoted to the comprehensive analysis of the detrimental effects of noise uncertainty in wide-band multimode subband based spectrum sensing over frequency selective channels. Unlike existing analyses that are mostly limited to single-band detection, an analytical model is firstly formulated for the case of wide-band multimode subband based spectrum sensing, considering both noise uncertainty and frequency selectivity. This model is also extended for the case of detecting a reappearing primary user (PU), as well as for the sensing scenarios where the frequency range of a PU is unknown. Novel closed-form expressions are derived for the corresponding probabilities of false alarm and probabilities of detection. The derived expressions are subsequently employed in quantifying the effects of noise uncertainty and frequency selectivity. A subband based scheme for the sensing of multi-channel primary systems is presented and analyzed. The proposed method is found to exhibit significantly reduced sensing time and greatly improved robustness against noise uncertainty compared to the basic wideband energy detection method.

This work is devoted to the outage probability analysis of full-duplex (FD) regenerative relay systems over multipath fading channels. Unlike the majority of analyses that assume basic symmetric fading conditions, the present work considers asymmetric generalized fading conditions, which are more realistic in practical communications scenarios. To this end, we assume that the source-relay path is subject to κ - μ multipath fading conditions, that can also account for line-of-sight communications, whereas the source-to-destination and relay-to-destination paths are subject to η-μ fading conditions that typically hold for non-line-of-sight communications. Novel analytic expressions are derived for the outage probability (OP) of the considered FD as well as for the corresponding half-duplex (HD) relay case for comparisons. These expressions are given in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. Based on this, they are subsequently employed in analyzing the corresponding performance for different communication scenarios. It is shown that the OP of the FD relay system is highly dependent upon the severity of fading and that its performance outperforms significantly the spectral efficiency increases.

This article investigates novel adaptive self-interference cancellation solutions and the total integrated cancellation performance of a mobile single-antenna inband full-duplex transceiver. First, novel self-adaptive digital self-interference cancellation algorithms are described, with an emphasis on tracking of time-varying self-interference coupling channel in a mobile device as well as on structural ability to suppress also nonlinear self-interference with highly nonlinear mobile power amplifiers. This leads to an advanced self-adaptive nonlinear digital canceller which utilizes a novel orthogonalization procedure for nonlinear basis functions, together with low-cost LMS-based parameter learning. The achievable self-interference cancellation performance is then evaluated with actual RF measurements using mobile device scale RF components, in particular a highly nonlinear PA. The measurements also incorporate a novel self-adaptive RF cancellation circuit in order to realistically assess the total integrated cancellation performance. The reported results show that highly efficient self-interference cancellation can be achieved also in a mobile device, despite a heavily nonlinear PA and limited computing and hardware resources. The proposed cancellation solutions, when integrated together, show that 100 dB of self-interference can be cancelled using a 20 MHz LTE waveform, while the SI can be attenuated by over 110 dB with a narrower bandwidth of 1.4 MHz, all measured at 2.4 GHz ISM band. Furthermore, these results are achieved using a highly nonlinear transmitter power amplifier and fully adaptive canceller structures which can track a rapidly changing coupling channel in a mobile full-duplex device.

In this paper, we study the effects of effective channel non-reciprocity in coordinated TDD multi-cell MIMO network based on weighted sum rate (WSR) maximization. More specifically, we focus on UE transceiver non- reciprocity while the base stations (BS) are assumed to be perfectly calibrated, and both centralized and decentralized beamforming schemes are considered. In the centralized scheme, the cost function is constructed in a central controller using antenna specific UL pilots for channel estimation from all the connected BSs in the network. Then, even though the transceiver frequency response (FR) mismatches at the UE side corrupt the effective channel reciprocity, it is shown to have only a trivial impact on the WSR objective in such centralized case. However, when decentralized beamforming is deployed, the optimization is carried out in each BS and the corresponding cost function and optimization process depend on information acquired by over-the-air signaling between the BSs and all the users using precoded UL pilots. In this case, it is then shown that the transceiver FR mismatches at the UE side can cause severe performance degradation and even influence the convergence properties of the sum-rate optimization process. Further insight is provided for improving the performance by modifying the weight calculations in the optimization process and connecting users with good cell separations. Then a convergence-aware processing algorithm is also proposed to improve the performance of the decentralized scheme under UE transceiver non-reciprocity. Numerical experiments demonstrate that efficient processing algorithms for calibrating UE transceiver mismatches to be less than -30dB to -35dB are, in general, required in the decentralized system in order to achieve performance close to the ideal case without any RF imperfections.

In this paper we study the problem of identifying active subcarriers in an OFDM signal from compressive measurements sampled at sub-Nyquist rate. The problem is of importance in Cognitive Radio systems when secondary users (SUs) are looking for available spectrum opportunities to communicate over them while sensing at Nyquist rate sampling can be costly or even impractical in case of very wide bandwidth. We first study the effect of timing offset and derive the necessary and sufficient conditions for signal recovery in the oracle-assisted case when the true active sub-carriers are assumed known. Then we propose an Orthogonal Matching Pursuit (OMP)- based joint sparse recovery method for identifying active subcarriers when the timing offset is known. Finally we extend the problem to the case of unknown timing offset and develop a joint dictionary learning and sparse approximation algorithm, where in the dictionary learning phase the timing offset is estimated and in the sparse approximation phase active subcarriers are identified. The obtained results demonstrate that active subcarrier identification can be carried out reliably, by using the developed framework.

It is commonly expected that network densification will play an important role in achieving the capacity demands of 5G communication networks. While densification is introduced to improve the spectral efficiency and area-capacity, it also results in an infrastructure that is perfectly suitable for user node (UN) positioning. However, so far this compelling opportunity has not been clearly recognized in the literature. In this paper, we therefore propose to make "always on" positioning an integral part of 5G networks such that highly accurate user location estimates are available at any given moment but without draining the UN batteries. We furthermore propose an extended Kalman filter (EKF) that tracks the user location based on the fusion of direction of arrival (DoA) and time of arrival (ToA) estimates obtained at the access nodes (ANs) of the 5G network. Since ToA estimates are typically not useful for positioning unless the UN is synchronized with the network, we include a realistic clock model within the DoA/ToA EKF. This addition makes it possible to estimate the offset of the imperfect UN clock, along with the UN position. In an extensive analysis that is based on specific 5G simulation models, we then quantify the enormous potential of high accuracy positioning in 5G networks, in general, and the proposed DoA/ToA EKF, in particular. Moreover, we demonstrate that the proposed DoA/ToA EKF substantially outperforms the classical DoA-only EKF and is furthermore also able to handle practically extremely relevant situations where the DoA-only EKF fails to position the UN.

Cognitive radio is regulated to permit efficient access to leftover licensed bands by unlicensed users in an opportunistic manner. Spectrum sensing is aimed to protect the primary users from interferences of the secondary users. Energy detection constitutes the most dominant method for spectrum sensing in cognitive radio and many other systems owing to its non-coherent structure as well as its simplicity and applicability. The sensing capability of wireless detectors in unreliable environments is challenged by adverse effects of multipath fading. Motivated by this, in the presented work the performance analysis of the energy detector is investigated over η-μ and α-μ fading channels. Novel closed-form expressions are derived for the detector using the area under the receiver operating characteristic curve (AUC) measure. The derived analytical results are verified by numerical computations and Monte Carlo simulations.

Energy detection constitutes a popular sensing approach thanks to its relatively satisfactory performance at low complexity requirements. Its efficiency can be practically enhanced by employing diversity schemes which are also capable of providing adequate mitigation of multipath fading effects. Based on this, the present work is devoted to the analysis of energy detection based spectrum sensing over generalized multipath fading channels using square law combining. Unlike the traditional evaluation based on the receiver operating characteristic (ROC) curves, the present analysis is based on the area under ROC curve (AUC), which is a particularly accurate performance measure that is used widely in natural sciences and engineering. To this end, novel closed-form expressions are firstly derived for the conventional AUC over the generalized κ − μ fading channels. These results are subsequently employed for deriving closed-form expressions for the case of square law combining. It is shown that the corresponding performance is, as expected, highly dependent upon the severity of fading and is improved substantially as the number of branches increase. In this context, it is also shown that using up to five branches ensures rather acceptable performance even at non-high signal-to-noise ratio values. Furthermore, the offered results have a relatively convenient algebraic representation and can be useful in analyses relating to cognitive radio and RADAR systems.

Spectrum scarcity has become a critical concern in wireless communication systems due to the limited availability of frequency spectrum. Hence, cognitive radio (CR) has been introduced as a solution for more effective use of the spectrum resources. Spectrum sensing (SS) is one of the key elements in the implementation of effective and reliable CR systems. Energy detection (ED) based SS is the most common sensing algorithm due to its low complexity. The main drawback of ED based SS is that it is highly dependent on the precise knowledge of the receiver noise variance. Hence, the performance of the ED algorithm is degraded significantly, when there is noise uncertainty in the estimation of the noise variance. In this study, we apply a recently proposed enhanced ED based algorithm to the sensing of Wireless Microphone (WM) signals, demonstrating robustness to noise uncertainty in real-time testing with actual WM signals. This so-called Max-Min ED algorithm is based on subband division of a wideband signal using an analysis filter bank (AFB) and utilizing the difference of maximum and minimum subband energies as the test statistic. Following the introduction of analytical models and scenarios of ED based SS algorithms, the sensing algorithms are implemented and tested using National Instruments (NI) Universal Software Radio Peripheral (USRP) and the NI-LabVIEW software platform, together with the necessary toolboxes.

A fast and reliable detection scheme is essential in several wireless applications such as radar and cognitive radio systems. Energy detection is such a method as it does not require a priori information of the received signal while it exhibits low implementation complexity and costs. Since the detection capability of ED is largely affected by the effects of multipath fading, this paper is devoted to a thorough analysis of energy detection based spectrum sensing over generalized fading conditions. To this end, analytical expressions are firstly derived using the area under the receiver operating characteristic curve (AUC) under additive white Gaussian noise. This analysis is subsequently extended to the case of generalized fading conditions characterized by k — μ and η — μ fading distributions. The offered results are novel and are employed in analyzing the corresponding performance. It is shown that fading phenomena result to detrimental effects on the performance of spectrum sensing since the deviation between severe and non-severe conditions is rather substantial.

Radio frequency (RF) front-ends constitute a fundamental part of both conventional and emerging wireless communication systems. However, in spite of their importance they are often assumed ideal, although they are practically subject to certain detrimental impairments, such as amplifier nonlinearities, phase noise and in phase and quadrature (I/Q) imbalance (IQI). The present work is devoted to the quantification and evaluation of the RF IQI effects in the context of realistic wireless vehicle-to-vehicle (V2V) communications over double-Nakagami-m fading channels. Novel closed form expressions are derived for the corresponding outage probability for the case of ideal transmitter (TX) and receiver (RX), ideal TX and I/Q imbalanced RX, I/Q imbalanced TX and ideal RX, and joint I/Q imbalanced TX/RX. The offered analytic results have a relatively convenient algebraic representation and their validity is extensively justified through comparisons with respective results from computer simulations. Based on these, it is shown that cascaded fading results to considerable degradations in the system performance and that assuming ideal RF front-ends at the TX and RX induces non-negligible errors in the outage probability evaluation that can exceed 20% in several V2V communication scenarios.

This work is devoted to the error rate and energy-efficiency analysis of regenerative cooperative networks in the presence of multipath fading and spatial correlation. To this end, exact analytic expressions are firstly derived for the symbol-error-rate of M-ary quadrature amplitude modulation in a dual-hop decode-and-forward relay system under spatially correlated Nakagami-m fading channels and maximum ratio combining at the destination. The derived expressions are subsequently employed in quantifying the energy consumption of the considered system, incorporating both transmit energy and the energy consumed by the transceiver circuits. The overall energy consumption is also minimized for certain quality-of-service requirements and it is shown that depending on the degree of spatial correlation, severity of fading, transmission distance, and relay location, a substantial overall energy reduction is sought when compared to conventional direct transmission.

The present work is devoted to the analytic performance evaluation of cooperative spectrum sensing (CSS) over generalized fading channels. The proposed analysis is based on the efficient Gaussian-Finite-Mixture (GFM) that allows the derivation of a simple and accurate closed-form expression for the average probability of energy detection (ED) under different fading environments. Capitalizing on this, we derive generalized closed-form expressions for the global probabilities of detection for the CSS with two main hard centralized fusion rules, namely, the AND and the OR rules. The efficiency and usefulness of the proposed expressions is justified by comparing the corresponding complementary receiver operating characteristic (ROC) curves for both multipath and composite multipath/shadowing fading channels, which are otherwise particularly difficult to obtain. The offered analytic results are corroborated by respective results from computer simulations and it is shown that the corresponding performance depends significantly on both the severity of fading and the involved number of users in the collaborative network.

Statistical distributions have been extensively used in modeling fading effects in conventional and modern wireless communications. In the present work, we propose a novel κ - μ composite shadowed fading model, which is based on the valid assumption that the mean signal power follows the inverse gamma distribution instead of the lognormal or commonly used gamma distributions. This distribution has a simple relationship with the gamma distribution, but most importantly, its semi heavy-tailed characteristics constitute it suitable for applications relating to modeling of shadowed fading. Furthermore, the derived probability density function of the κ - μ / inverse gamma composite distribution admits a rather simple algebraic representation that renders it convenient to handle both analytically and numerically. The validity and utility of this fading model are demonstrated by means of modeling the fading effects encountered in body centric communications channels, which have been known to be susceptible to the shadowing effect. To this end, extensive comparisons are provided between theoretical and respective real-time measurement results. It is shown that these comparisons exhibit accurate fitting of the new model for various measurement set ups that correspond to realistic communication scenarios.

In this paper we propose a new composite fading model which assumes that the mean signal power of an η — μ signal envelope follows an inverse gamma distribution. The inverse gamma distribution has a simple relationship with the gamma distribution and can be used to model shadowed fading due to its semi heavy-tailed characteristics. To demonstrate the utility of the new η — μ / inverse gamma composite fading model, we investigate the characteristics of the shadowed fading behavior observed in body centric communications channels which are known to be susceptible to shadowing effects, particularly generated by the human body. It is shown that the η — μ / inverse gamma composite fading model provided an excellent fit to the measurement data. Moreover, using Kullback-Leibler divergence, the η — μ / inverse gamma composite fading model was found to provide a better fit to the measured data than the k — μ / inverse gamma composite fading model, for the communication scenarios considered here.

In this article, we focus on the RF and digital front-end design and implementation challenges associated with future 5G radio access systems with special emphasis on spectrally contained waveforms and small-cell system scenarios. In general, filter bank based multicarrier (FBMC) type of techniques have various potential benefits due to their excellent spectral containment compared to classical OFDM(A). However, these spectrally contained waveforms lose their intriguing spectral properties when power amplifier (PA) nonlinearities are considered. Flexible and efficient digital predistortion (DPD) algorithms are thus considered an interesting solution in order to restore the spectral containment of such advanced 5G waveforms. Noncontiguous spectrally-agile transmission is another key feature of future 5G systems for increasing data rates and spectral allocation flexibility. However, the PA nonlinearities impose even more severe challenges in such noncontiguous transmission scenarios due to the resulting spurious intermodulation emissions that can easily violate the emission limits or even desensitize the own receiver in frequency division duplexing based systems. Furthermore, at the network deployment level, different small-cell concepts are expected to play a major role in future 5G networks. Unlike the ordinary macro base-stations, the digital computing capabilities in small-cell base-stations are much more limited. Furthermore, they should also adopt lower-cost and small-size analog RF components, while still maintaining high energy-efficiency. The afore-mentioned constraints, along with advanced 5G waveforms, call for flexible and low-complexity DPD solutions, a challenge addressed in this article. We report novel DPD methods with built-in capability to direct the linearization performance to pre-defined frequencies or subbands in a flexible manner, and demonstrate their good performance and complexity benefits in the context of non-contiguous FBMC transmission.

Filter bank multicarrier (FBMC) is considered as one of the strong 5G waveform candidates due to better spectral containment compared to orthogonal frequency division multiplexing (OFDM). However, when considering the spectral leakage effects appearing in practical devices due to transmitter non-idealities, such as power amplifier (PA) nonlinearity and inphase-quadrature (I/Q) imbalance, the nice spectral properties of FBMC may be severely degraded, thus questioning the additional complexity needed to generate such waveforms in baseband. In this paper, digital pre-distortion (DPD) is investigated as a practical solution for both of these problems. We extend the study also to potential applications of spectrum sensing based cognitive radio in future wireless communications. We utilize the benefits of the analysis filter bank of FBMC receiver in wideband multichannel spectrum sensing. In this context, the power leakage due to transmitter non-idealities also makes it difficult to identify spectral gaps amongst relatively strong primary transmissions. It is demonstrated that effective DPD greatly facilitates both spectrum sensing and spectrum utilization functions and thus enhances the overall spectral efficiency in opportunistic dynamic spectrum use scenarios.

The deployment of future 5G ultra-dense small cell networks provides unprecedented opportunities to create an advanced localization system that meets the demands of future location-based services and functionalities. In this paper, we present technical enablers for obtaining location information of user nodes (UNs) in a network-centric manner. More specifically, we focus on signal properties, access node (AN) hardware and AN deployments in the envisioned 5G systems. Moreover, we provide illustrative examples of the expected localization performance and indicate how to efficiently predict the UN location. Finally, we offer insights into the utilization of location-awareness and location prediction, and show that it provides substantial benefits compared to existing radio networks.

This paper presents a novel RF circuit architecture for self-interference cancellation in inband full-duplex radio transceivers . The developed canceller is able to provide wideband cancellation with waveform bandwidths in the order of 100 MHz or beyond and contains also self-adaptive or self-healing features enabling automatic tracking of time-varying self-interference channel characteristics. In addition to architecture and operating principle descriptions, we also provide actual RF measurements at 2.4 GHz ISM band demonstrating the achievable cancellation levels with different bandwidths and when operating in different antenna configurations and under low-cost highly nonlinear power amplifier. In a very challenging example with a 100 MHz waveform bandwidth, around 41 dB total cancellation is obtained while the corresponding cancellation figure is close to 60 dB with the more conventional 20 MHz carrier bandwidth. Also, efficient tracking in time-varying reflection scenarios is demonstrated.

This paper investigates the achievable throughput of a multi-antenna two-hop relay link under hybrid full/half-duplex operation. The analysis is facilitated by realistic waveform simulations, which explicitly model all the essential circuit impairments occurring in the relay transceiver together with degrading channel estimation and self-interference cancellation. The obtained results indicate that pure full-duplex operation does not ensure optimal performance but additional half-duplex transmission periods are usually needed to maximize the end-to-end throughput. Especially, it is shown that the estimation of the self-interference channel within the relay should be performed when the source is not transmitting anything while also the source should be allowed to transmit alone to avoid making the first hop a bottleneck. These findings form a solid basis for optimizing the full-duplex MIMO relay deployments in future mobile networks.

The projected growth of mobile data traffic requires the 5G wireless systems to support at least 1000× larger area throughput than the existing 4G solutions. This requires ultra- dense local area networks combined with fast and flexible time division duplex based access to provide wide bandwidths for multi-gigabit peak data rates. In this paper, we focus on the system level performance characterization and evaluations of the recently proposed 5GETLA radio interface for centimeter-wave communications. We study the effects of interference tiers on the throughput and SINR distributions and show that broadcasted, omnidirectional information should be minimized and preferably combined with learning spectral reuse and interference canceling receivers. We also demonstrate the low latency provided by the new 5G radio interface design.

Channel reciprocity can be used for providing sufficient key generation in time division duplex (TDD) system. However, in practice, its application is limited by the hardware fingerprint deviation (HFD) problem. In this paper, we propose a novel real-time transform that can cope with this problem in time- varying TDD channel without any calibration period or feedback loops. More specifically, a log-domain differential (LDD) transform is developed and the resulting performance is analyzed in terms of mean square error (MSE) between receptions at Alice and Bob and effective signal to error ratio (ESER). The analysis shows that the proposed transform can eliminate the impact of HFD, yet its performance is very sensitive to channel noise and moving speed. For this purpose, an enhanced version is proposed including an efficient noise reduction technique and the impact of mobility on parameter design is also analyzed. Numerical results show that the proposed LDD advanced transform provides performance comparable to the ideal case without HFD, and thus, can be used to form a simple, practical and flexible solution for secret key generation in time-varying TDD channel.

This paper studies the idea and performance of cyclostationary spectrum sensing in cognitive full-duplex radios, when secondary transmission and spectrum sensing are done simultaneously at the same channel. The paper starts by briefly introducing the ideas of cognitive full-duplex radio and the cyclostationary spectrum sensing in the presence of self-interference. The idea of changing the cyclic features of the secondary signal is then proposed and its impact on the spectrum sensing under self-interference is analysed. The cyclic features can be changed by changing the length of the cyclic prefix of the OFDM signal or by changing the amount of subcarriers. The effects of both approaches are evaluated with comprehensive performance simulations. It is shown that changing the cyclic features of the secondary signal can provide significant improvements in the sensing results of the primary signal, and that in general, reliable inchannel spectrum sensing while transmitting is feasible. This can enable enhance coexistence mechanisms, e.g., for LTE-Unlicensed technology at ISM band.

This paper is devoted to the analytic investigation of a maximum-ratio-combining based regenerative multi-relay cooperative wireless network over non-homogeneous scattering environments. Such propagation conditions are rather realistic as they are encountered often in practical wireless transmission scenarios. Novel analytic expressions are derived for the symbol-error-rate of M-ary phase shift keying (M-PSK) over independently and non-identically distributed fading channels. The derived expressions are based on the moment-generating-function (MGF) approach and are given in closed-form in terms of the generalized Lauricella series. A simple algorithm for computing this special function is also proposed while the offered results are validated extensively through comparisons with respective results from computer simulations. Based on this, they are particularly useful in the analytic performance evaluation of such cooperative systems. To this end, it is shown that the performance of the cooperative system is significantly affected, as expected, by the number of employed relays as well as by the value of the involved fading parameters η and μ.

The present paper analyzes the outage probability of full-duplex (FD) regenerative relay systems over multipath fading channels. Unlike the majority of investigations that assume basic symmetric fading conditions, this analysis considers asymmetric generalized fading conditions, which are more realistic as they are encountered more often in practical wireless transmissions. To this end, it is assumed that the source-relay and source-destination links are subject to k -μ multipath fading conditions, which can represent generalized line-of-sight communication scenarios; on the contrary, the relay-to-destination link is subject to η-μ fading conditions that typically represent generalized non-line-of-sight communication scenarios. Novel analytic expressions are derived for the outage probability (OP) of the considered FD relaying system. These expressions are given in closed-form and have a relatively tractable algebraic form which renders them convenient to handle both analytically and numerically. To this effect, they are subsequently employed in analyzing the corresponding performance for various communication scenarios. It is shown that the OP of the FD relay system is, as expected, highly dependent upon the severity of fading, the relay self-interference and the interference from the direct link. Furthermore, it is shown that at relatively high average signal-to-noise ratio values, the outage probability at low fading severity and at high relay self interference outperforms the respective performance for the case of high fading severity, but with low relay self-interference. Based on this, the offered results can be useful in the design and deployment of future full-duplex based cooperative communication systems.

IEEE 802.11ah task group is working on a new amendment of the IEEE 802.11 standard, suitable for high density WLAN networks in the sub 1 GHz band. It is expected to be the prevalent standard in many Internet of Things (IoT) and Machine to Machine (M2M) applications where it will support long-range and energy-efficient communication in dense network environments. Therefore, significant changes in the legacy 802.11 standards have been proposed to improve the network performance in high contention scenarios, most important of which is the Restricted Access Window (RAW) mechanism described in the amendment. In this paper we analyze the performance of the RAW mechanism in the Non-Cross Slot Boundary case under various possible holding schemes. We propose new holding schemes as well as a new grouping scheme for RAW mechanism based on backoff states of the stations. The proposed schemes are shown to improve the saturation throughput and energy efficiency of the network through extensive simulations. These schemes can therefore be adapted in practical deployment scenarios of the IEEE 802.11ah use cases to improve the overall network performance. Overall, these advanced features make 802.11ah standard a true IoT-enabling technology towards seamless integration of massive amount of connected devices in the future.

Cognitive radio is an emerging wireless technology that is capable of efficiently coordinating the use of the currently scarce spectrum resources, and spectrum sensing constitutes its most crucial operation. This paper proposes wideband multichannel spectrum sensing methods utilizing fast Fourier transform or filter-bank-based methods for spectrum analysis. Fine-grained spectrum analysis facilitates optimal energy detection in practical scenarios where the transmitted signal, channel frequency response, and/or receiver frequency response do not follow the commonly assumed boxcar model, which typically assumes, among other things, narrow-band communications with flat spectral characteristics. Such sensing schemes can be tuned to the spectral characteristics of the target primary user signals, allowing simultaneous sensing of multiple target primary signals with low additional complexity. This model is also extended to accounting for the specific scenario of detecting a reappearing primary user during secondary transmission, as well as in spectrum sensing scenarios where the frequency range of a primary user is unknown. Novel analytic expressions are derived for the corresponding probability of false alarm and probability of detection in each case, while the useful concept of the area under the receiver operating characteristics curve is additionally introduced as a single scalar metric for evaluating the overall performance of the proposed spectrum sensing algorithms and scenarios. The derived expressions have a rather simple algebraic representation, which renders them convenient to handle both analytically and numerically. The offered results are also extensively validated through comparisons with respective results from computer simulations and are subsequently employed in evaluating each technique analytically, which provides meaningful insights that are anticipated to be useful in future deployments of cognitive radio systems.

Sectorized antennas are a promising class of antennas for enabling direction-of-arrival (DoA) estimation and successive transmitter localization. In contrast to antenna arrays, sectorized antennas do not require multiple transceiver branches and can be implemented using a single RF front-end only, thus reducing the overall size and cost of the devices. However, for good localization performance the underlying DoA estimator is of uttermost importance. In this paper, we therefore propose a novel high performance DoA estimator for sectorized antennas that does not require cooperation between the transmitter and the localizing network. The proposed DoA estimator is broadly applicable with different sectorized antenna types and signal waveforms, and has low computational complexity. Using computer simulations, we show that our algorithm approaches the respective Cramer-Rao lower bound for DoA estimation variance if the signal-to-noise ratio (SNR) is moderate to large and also outperforms the existing estimators. Moreover, we also derive analytical error models for the underlying DoA estimation principle considering both free space as well as multipath propagation scenarios. Furthermore, we also address the fusion of the individual DoA estimates into a location estimate using the Stansfield algorithm and study the corresponding localization performance in detail. Finally, we show how to implement the localization in practical systems and demonstrate the achievable performance using indoor RF measurements obtained with practical sectorized antenna units.

Accurate fading characterization and channel capacity determination are of paramount importance in both conventional and emerging communication systems. The present work addresses the non-linearity of the propagation medium and its effects on the channel capacity. Such fading conditions are first characterized using information theoretic measures, namely, Shannon entropy, cross entropy and relative entropy. The corresponding effects on the channel capacity with and without power adaptation are then analyzed. Closed-form expressions are derived and validated through computer simulations. It is shown that the effects of nonlinearities are significantly larger than those of fading parameters such as the scattered-wave power ratio, and the correlation coefficient between the in-phase and quadrature components in each cluster of multipath components.

In this paper, we present a novel digital predistortion (DPD) solution based on a direct learning approach, which is capable of reducing the unwanted emissions resulting from the power amplifier (PA) at any prespecified frequency located in the transmitter's out-of-band or spurious domain. The proposed scheme is based on evaluating the power spectral density (PSD) of the PA output signal and optimizing the DPD coefficients iteratively in order to minimize the output PSD around the prespecified frequency. To highlight the feasibility of the proposed implementation, the predistortion processing is kept as simple as possible, deploying quasi-memoryless polynomial models. Efficient mitigation of unwanted emissions around the target frequency is demonstrated via simulations and actual RF measurements, in both single- and dual-carrier waveform scenarios, using memoryless and memory-based PAs. The proposed DPD solution could be potentially employed in applications such as mobile devices utilizing noncontiguous multicarrier transmission, where the intermodulation spurs may overlap with the device's own receiver band, or could be potentially violating the spurious emission limits. Another target application is cognitive radio, where the PA may produce unwanted emissions that are interfering with primary-user transmissions. To the best of the authors' knowledge, there does not exist a similar technique in the open literature, and thus, the purpose of this paper is to encourage scientific discussions and technological innovations toward the creation of relatively low-complexity frequency-optimized predistortion techniques employed against selected unwanted emissions produced by the transmitter.

Internet of Things (IoT) and Machine-to-Machine (M2M) applications are typically characterized by moderate investment costs to the M2M devices and infrastructure, in addition to the high reliability and energy efficiency requirements. The new Sub-1 GHz WiFi standard, namely the IEEE802.11ah, is being introduced to address these requirements deploying its recently specified MAC and PHY features and mechanisms. In this paper, we present an extensive analysis office 802.11ah network performance by means of realistic system level simulations. In particular, we focus on realistic performance evaluation and enhancement study of the IEEE 802.11ah network when multi-access points (multi-APs) with relatively high number of associated stations (STAs) are considered. The performance evaluation of the multi-AP IEEE 802.11ah network considers one of the main proposed MAC enhancement schemes for collision reduction, namely, the Restricted Access Window (RAW) mechanism. The analysis results confirm the importance of this novel mechanism to improve substantially the overall system performance from both network throughput and energy efficiency perspectives. Overall, the technical findings reported in this article strengthen the prospects of IEEE 802.11ah as one of the key enabling technologies for wide-scale low-cost and energy-efficient M2M deployments and IoT applications in the future.

This work is devoted to the analysis of the performance of energy detection based spectrum sensing in the presence of enriched fading conditions which are distinct for the large number of multipath components and the lack of a dominant components. This type of fading conditions are characterized efficiently by the well known Nakagami-q or Hoyt distribution and the proposed analysis is carried out in the context of the area under the receiver operating characteristics (ROC) curve (AUC). Unlike the widely used probability of detection metric, the AUC is a single metric and has been shown to be rather capable of evaluating the performance of a detector in applications relating to cognitive radio, radar systems and biomédical engineering, among others. Based on this, novel analytic expressions are derived for the average AUC and its complementary metric, average CAUC, for both integer and fractional values of the involved time-bandwidth product. The derived expressions have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. Based on this, they are employed in analyzing the behavior of energy detection based spectrum sensing over enriched fading conditions for different severity scenarios, which demonstrates that the performance of energy detectors is, as expected, closely related to the value of the fading parameter q.

Spurious intermodulation components have recently been identified as a major problem in carrier aggregation mobile transmitters with multi-band power amplifiers (PAs). This article presents novel adaptive digital predistortion (DPD) solutions with reduced complexity in both the predistortion processing and the feedback paths, to tackle this problem. Compared with conventional DPDs which aim to linearize the whole transmit bandwidth, the proposed technique aims at mitigating only those intermodulation components which are most problematic from the spurious emission limit perspective. The proposed technique is verified with extensive simulations in various 3GPP LTE-A carrier aggregation scenarios, showing that the intermodulation spurs can be efficiently mitigated below the spurious emission limit with relatively small back-offs.

This paper presents a software-based implementation for the MIMO transmitter baseband processing conforming to the IEEE802.11ac standard on a DSP core with vector extensions. The transmitter is implemented in four different transmission scenarios, which include 2×2 and 4×4 MIMO configurations, yielding beyond 1Gbps transmit bit rate. The implementation is done for the frequency-domain processing and real-time operation has been achieved when running at a clock frequency of 500MHz. The proposed software solution is evaluated in terms of power consumption, number of clock cycles and memory usage. This SDR based implementation provides improved flexibility and reduced design effort compared to conventional approaches while maintaining energy consumption close to fixed-function hardware solutions.

Power-constrained mobile radio communication transmitters drive their transmit power amplifiers close to their saturation regions, which results in nonlinear intermodulation distortion that is especially harmful in multi-cluster and carrier aggregation transmission scenarios. Digital predistortion is a method for linearizing the transmitter and suppressing the most harmful spurious emissions at the transmitter power amplifier output. This paper describes a programmable implementation of a digital predistortion filter on a heterogeneous Transport Trigger Architecture (TTA) multiprocessor. The predistortion algorithm is based on a parallel Hammerstein polynomial model and the experimental results show that the proposed programmable architecture is capable of linearizing a 20 MHz LTE carrier in realtime with a power consumption that is suitable for mobile devices.

We consider the multiuser frequency allocation problem in single-input single-out put (SISO) LTE-A type uplink with carrier aggregation (CA). The increased bandwidth in LTE-A system allows orthogonal allocation of subcarriers among users. We consider both consecutive and distributed frequency allocation strategies with per user transmit power constraints and more realistic power amplifier models accounting the dependence of the amplifier efficiency on the frequency allocation. A novel binary integer programming with water-filling power allocation is proposed for consecutive frequency allocation. The system level performance is evaluated via computer simulations. The results shed light to the problem of frequency allocation with real user devices both from theoretical and practical points of view.

A novel approach for the frequency response mismatch mitigation of a 4-channel Time-Interleaved ADC (TI-ADC) is proposed which enables the interleaving mismatch identification to be performed in a fully blind online manner. This is accomplished via generating an appropriate complex valued signal from the real valued TI-ADC output signal which allows deploying complex statistical signal processing methods for the mismatch identification in a manner similar to the I/Q imbalance correction. As proof of concept, the compensation architecture is implemented, demonstrated and tested using real RF-sampling 4-channel TI-ADC hardware data, evidencing spur reduction below 80 dBFS.

The current outdoor deployments, dominated by macrocellular layer, inherently lacks the capabilities for supporting the anywhere and anytime services of Beyond 4G (B4G) networks. Hence, new deployment solutions are required that can cost-effectively address the capacity demand of the future and also offer consistently high bit rates and decent quality of service (QoS) throughout the network coverage area. This article looks into an advanced outdoor distributed antenna system (DAS) concept as a potential solution for fulfilling the requirements of 5G era. The investigated DAS solution can dynamically configure itself as a single super microcell or multiple independent small microcells, based on the outdoor traffic conditions. We evaluate the performance of dynamic DAS using two antenna configurations based on; (i) No-tilt (utilizing Omni-directional antenna) and (ii) Extreme tilt (utilizing wide beam directional antenna). Furthermore, the analysis covers the performance evaluation, mainly from an outdoor perspective while taking into account two key performance metrics: coverage and spectral efficiency. The obtained results indicate that the dynamic outdoor DAS concept can offer an efficient and capacity-adaptive solution to provide on-demand outdoor capacity in urban areas. Moreover, the extreme tilt configuration is shown to improve the cell and area spectral efficiency of dynamic DAS, in outdoor dense urban environment, by effectively controlling the inter-cell interference.

Use of energy efficient construction materials is increasing all the time due to more and more tightened building regulations, which aim is to reduce overall energy consumption and thereon e.g. mitigate climate change. Energy efficient building materials and structures improve heat insulation but also change the propagation characteristics of radio signals between outdoor and indoors. This paper examines in details such propagation effects and increased levels of outdoor-indoor attenuation in modern apartment buildings, and their impact on mobile cellular networks. In particular, the aim is to compare external wall attenuations for the modern and older apartment buildings and to assess the impact of increased attenuations for the density and planning of cell sites of mobile cellular macro networks. We also demonstrate and evaluate the opportunities for increased indoor signal coverage by using a dedicated aperture installed in building materials.

In this paper we introduce a new flexible time division duplexing based radio interface for future 5G small cell communications. We describe the benefits of the new design to achieve high energy efficiency, high spectral efficiency and low latency at the same time. We also compare our reference design against LTE-A assuming rank 8 DL SU-MIMO transmission and show that we our design can achieve more than 29% lower total overhead and up to 90% lower round trip time.

In this paper, we propose an efficient transceiver non-reciprocity estimation-compensation framework for precoded TDD multi-user MIMO-OFDM downlink transmission systems. General signal models are first developed to analyze the effects of transceiver non-reciprocity at both the base-station and user equipment sides. The analysis shows that transceiver non-reciprocity at the base-station causes inter-user interference and thus substantial performance degradation, while the impact of transceiver non-reciprocity at the user equipment side can be fairly easily handled with downlink detector processing. Next, an over-the-air (OTA) type pilot-based estimation algorithm is devised for efficient identification of base-station non-reciprocity parameters, which are then used in pre-compensating or precoding the multiuser data properly in the base-station. Compared to the existing work in the literature, the proposed approach does not require the use of feedback signaling or complicated channel matrix decomposition techniques for extracting the non-reciprocity parameters. The resulting link and system performance of the proposed estimation-compensation framework is then evaluated using extensive computer simulations in linear precoded TDD multiuser MIMO-OFDM system context. Based on the obtained results, the proposed estimation-compensation approach can provide a simple, practical and flexible solution to efficiently restore the channel reciprocity with reasonable calibration overhead.

This paper studies the impact of two implementation imperfections, namely, transceiver frequency-response non-reciprocity and antenna mutual coupling mismatches at the base-station and user equipment on the channel reciprocity assumption in TDD systems. Comprehensive signal models are first developed to analyze the joint effects of these two imperfections in the TDD multi-user MIMO-OFDM downlink transmission system context, covering both zero-forcing (ZF) and eigenbeamforming based transmitter processing. The corresponding performance degradation is then evaluated in terms of SINRs and maximum achievable sum-rate. The analysis shows that during downlink transmission, the transceiver non-reciprocity and antenna mutual coupling mismatches at the base-station introduce inter-user interference (IUI) and are the major causes for the resulting performance degradation. Implementation imperfections at user equipment side, in turn, only introduce inter-stream interference (ISI) that can be fairly easily suppressed in detector processing as part of effective precoded channels. In order to achieve throughputs close to the ideal case, transceiver frequency-response mismatches and antenna mutual coupling at the base-station side need to be extremely well calibrated, generally below 35-40dB in terms of relative mismatch levels.

Device to Device (D2D) underlaying cellular networks have drawn a lot of attention recently, in both Long Term Evolution (LTE) standardization as well as academic research, with its huge potential in increasing the system capacity. However, intra-cell interference is quite a challenging issue since D2D user equipment (DUE) may share the same resources with the traditional cellular user equipment (CUE). Moreover, due to the lack of convexity, globally optimal sharing strategy is generally unattainable. In this paper, we apply bipartite matching strategy from graph theory to optimize the resource allocation in D2D underlay network. Two different situations are considered in detail, namely optimal resource allocation with fixed power transmission (FPT) and optimal resource allocation with uplink power controlled transmission (PCT). Practical constraints such as minimum spectral efficiency and maximum transmit power limitations are also considered in the optimization. Simulations and analyses show that the proposed Bipartite Matching based Allocation (BMA) can improve the system capacity significantly comparing to those traditional methods.

The present work is devoted to the analysis of a regenerative multi-node dual-hop cooperative system over enriched multipath fading channels. Novel analytic expressions are derived for the symbol-error-rate for M-ary quadrature modulated signals in decode-and-forward relay systems over both independent and identically distributed as well as independent and non-identically distributed Nakagami-q (Hoyt) fading channels. The derived expressions are based on the moment-generating-function approach and are given in closed-form in terms of the generalized Lauricella series. The offered results are validated extensively through comparisons with respective results from computer simulations and are useful in the analytic performance evaluation of regenerative cooperative relay communication systems. To this end, it is shown that the performance of the cooperative system is, as expected, affected by the number of employed relays as well as by the value of the fading parameter q, which accounts for pre-Rayleigh fading conditions that are often encountered in mobile cellular radio systems.

Direct-conversion radio receivers can offer highly integrated low-cost hardware solutions for cognitive radio (CR) devices. Such receivers are, however, also very sensitive to various radio frequency (RF) impairments such as IQ imbalance, which can considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and hence neglect the impacts of such practical RF hardware limitations. In this article, we study energy detection (ED) based spectrum sensing in both single-channel and multi-channel direct-conversion receiver scenarios impaired by IQ imbalance. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form for both receiver scenarios. The analytical results, confirmed through extensive simulations, show that while the single-channel receiver scenario is fairly robust to IQ imbalance, the wideband multi-channel sensing receiver is very sensitive to the image channel crosstalk induced by IQ imbalance. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to ideal RF receiver case, and the exact performance depends on the image channel power level and IQ imbalance values. In order to prevent such degradation in the ability to identify free spectrum, a waveform level interference cancellation method is then proposed to mitigate the image channel crosstalk. The optimum cancellation coefficient yielding interference-free signal is first derived, being also complemented with a practical coefficient sample estimator. An explicit condition is also derived for which the proposed cancellation scheme deploying the practical coefficient sample estimator provides performance gain in the sensing decisions compared to uncompensated energy detection. Extensive computer simulations with various signal and imbalance conditions are provided which demonstrate that the proposed enhanced energy detection can suppress the image channel crosstalk efficiently, yielding detection and false alarm probabilities essentially identical to those of an ideal RF receiver.

This paper addresses the modeling and cancellation of self-interference in full-duplex direct-conversion radio transceivers, operating under practical imperfect radio frequency (RF) components. First, detailed self-interference signal modeling is carried out, taking into account the most important RF imperfections, namely, transmitter power amplifier nonlinear distortion as well as transmitter and receiver IQ mixer amplitude and phase imbalances. The analysis shows that after realistic antenna isolation and RF cancellation, the dominant self-interference waveform at the receiver digital baseband can be modeled through a widely linear transformation of the original transmit data, opposed to classical purely linear models. Such widely linear self-interference waveform is physically stemming from the transmitter and receiver IQ imaging and cannot be efficiently suppressed by classical linear digital cancellation. Motivated by this, novel widely linear digital self-interference cancellation processing is then proposed and formulated, combined with efficient parameter estimation methods. Extensive simulation results demonstrate that the proposed widely linear cancellation processing clearly outperforms the existing linear solutions, hence enabling the use of practical low-cost RF front ends utilizing IQ mixing in full-duplex transceivers.

As the duplexing distances in emerging wireless systems are getting more and more narrow, achieving sufficient isolation between transmit and receive chains using radio frequency (RF) filtering alone becomes increasingly complex. Particularly challenging problem in this context is the spectral regrowth of nonlinear power amplifiers (PAs) in the transmit chain, and other transmitter out-of-band (OOB) emissions, which can heavily desensitize the receiver chain. In this letter, we first carry out detailed modeling of transmitter OOB emissions due to practical wideband PAs with memory effects. Stemming from this modeling, and using the known digital transmit data inside the transceiver as reference, we then propose an efficient nonlinear digital cancellation technique to suppress the transmitter OOB emissions in the receiver path. The proposed technique is verified and analyzed using extensive computer simulations, rendering excellent suppression properties, hence enabling sufficient TX-RX isolation in frequency division duplexing (FDD) transceivers without any extra analog/RF filtering or PA linearization.

The nonlinear behavior of power amplifier (PA) in orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) system introduces in-band and adjacent channel interferences. The power in the adjacent channels is an important issue in the CR because it can act as interference to primary users. In this paper, we derive a closed-form expression for the leakage power at adjacent channels when CR devices use OFDM modulation. Moreover, the PA nonlinearity creates in-band distortion that produces intercarrier interference and reduces the secondary system capacity. To control this effect, we derive the optimum power scaling factor for the input signal that maximizes the secondary user rate. The optimization process is executed by considering nonlinear effects on signal-to-interference-and-noise ratio under the constraints of the powers in the adjacent channels. The power in the adjacent channels should not exceed the interference temperature limit. The analytical results are verified by simulation studies.

This paper is devoted to the derivation of novel analytic expressions and bounds for a family of special functions that are useful in wireless communication theory. These functions are the well-known Nuttall Q-function, incomplete Toronto function, Rice Ie-function, and incomplete Lipschitz-Hankel integrals. Capitalizing on the offered results, useful identities are additionally derived between the above functions and Humbert, Φ1, function as well as for specific cases of the Kampé de Fériet function. These functions can be considered as useful mathematical tools that can be employed in applications relating to the analytic performance evaluation of modern wireless communication systems, such as cognitive radio, cooperative, and free-space optical communications as well as radar, diversity, and multiantenna systems. As an example, new closed-form expressions are derived for the outage probability over nonlinear generalized fading channels, namely, α-η-μ, α-λ-μ, and α-κ-μ as well as for specific cases of the η-μ and λ-μ fading channels. Furthermore, simple expressions are presented for the channel capacity for the truncated channel inversion with fixed rate and corresponding optimum cutoff signal-to-noise ratio for single-antenna and multiantenna communication systems over Rician fading channels. The accuracy and validity of the derived expressions is justified through extensive comparisons with respective numerical results.

Despite the intensive recent research on wireless single-channel full-duplex communications, relatively little is known about the transceiver chain nonidealities of full-duplex devices. In this paper, the effect of nonlinear distortion occurring in the transmitter power amplifier (PA) and the receiver chain is analyzed, beside the dynamic range requirements of analog-to-digital converters (ADCs). This is done with detailed system calculations, which combine the properties of the individual electronics components to jointly model the complete transceiver chain, including self-interference cancellation. They also quantify the decrease in the dynamic range for the signal of interest caused by self-interference at the analog-to-digital interface. Using these system calculations, we provide comprehensive numerical results for typical transceiver parameters. The analytical results are also confirmed with full waveform simulations. We observe that the nonlinear distortion produced by the transmitter PA is a significant issue in a full-duplex transceiver and, when using cheaper and less linear components, also the receiver chain nonlinearities become considerable. It is also shown that, with digitally intensive self-interference cancellation, the quantization noise of the ADCs is another significant problem.

This paper addresses the analysis of oscillator phase-noise effects on the self-interference cancellation capability of full-duplex direct-conversion radio transceivers. Closed-form solutions are derived for the power of the residual self-interference stemming from phase noise in two alternative cases of having either independent oscillators or the same oscillator at the transmitter and receiver chains of the full-duplex transceiver. The results show that phase noise has a severe effect on self-interference cancellation in both of the considered cases, and that by using the common oscillator in upconversion and downconversion results in clearly lower residual self-interference levels. The results also show that it is in general vital to use high quality oscillators in full-duplex transceivers, or have some means for phase noise estimation and mitigation in order to suppress its effects. One of the main findings is that in practical scenarios the subcarrier-wise phase-noise spread of the multipath components of the self-interference channel causes most of the residual phase-noise effect when high amounts of self-interference cancellation is desired.

The aim of this work is the formulation and derivation of the η-μ/Inverse Gaussian composite distribution which corresponds to a physical fading model. The η-μ distribution is a generalized small-scale fading model which accounts effectively for non-line-of-sight scenarios and includes as special cases the widely known Nakagami-m, Rayleigh, Hoyt and one sided Gaussian distributions. Similarly, the inverse Gaussian (IG) distribution is a convenient model which was recently shown to characterize shadowing more efficiently than the widely used gamma distribution. To this effect, the proposed η-μ/IG model provides an overall efficient characterization of multipath and shadowing effects which typically occur simultaneously. The offered modelling accuracy is achieved thanks to the remarkable flexibility of its parameters. This is also verified by the fact that the proposed model is capable of providing good fittings to experimental data that correspond to realistic wireless communication scenarios while they include as special cases the widely known Nakagami-m/IG, Rayleigh/IG and Hoyt/IG composite fading models. Novel analytic expressions are derived for the envelope and power probability density function (pdf) of the η-μ/IG model. The derived expressions can be utilized in various studies in radio communications, free space optical communications and ultrasound imaging, among others. Indicatively, an analytic expression is derived for the outage probability (OP) of η-μ/IG fading channels.

Direct-conversion radio receivers can provide highly-integrated and low-cost hardware solutions for cognitive radio (CR) devices. However, such receiver structures are also known to suffer from various RF circuit imperfections, especially IQ imbalance. When studying the performance of various spectrum sensing methods, most existing work in literature neglect the effects of such RF imperfections. In this paper, we analyze the performance of a well-known eigenvalue based sensing method, namely maximum-to-minimum eigenvalue test, in the challenging multichannel spectrum sensing context under sensing receiver RF IQ imbalance. It is analytically shown that under RF IQ imbalance, the false alarm probability of the spectrum sensor is always higher compared to ideal RF front-end case. Then, in order to compensate for such degradation in the ability to identify free spectrum, a waveform level interference cancellation solution is deployed to enhance the performance, and optimum cancellation coefficient together with a practical sample estimator are formulated. Extensive computer simulations are provided to illustrate the degradation of false alarm probability in the presence of practical IQ imbalance values. Furthermore, the simulations also show that the proposed interference cancellation method can efficiently suppress the IQ imbalance effects, achieving identical false alarm probability with the ideal RF front-end case.

This article proposes a reconfigurable wideband transfer function design for quadrature ΣΔ A/D converters combined with digital post-compensation of frequency-selective mirror-frequency interference (MFI) induced by the inherent mismatches between the in-phase (I) and quadrature (Q) rails of the receiver. When receiving wideband waveforms, e.g., similar to LTE signals, it is crucial for the compensation performance to be able to consider also frequency variations in the image rejection. In addition to compensating the MFI originating from inside the quadrature ΣΔ modulator (QΣΔM), the digital compensation considers also the interference generated at the preceding receiver front-end components, such as an I/Q mixer. The performance of this approach is illustrated with a 20 MHz reception bandwidth, being a practical example of requirements for modern communications waveforms. This concrete example is given with a two-stage QΣΔM giving eighth order noise-shaping.

In this paper, we first derive the approximate symbol-error-rate (SER) for multi-node dual-hop cooperative relay network employing decode-and-forward (DF) protocol with a maximum ratio combining (MRC) at the destination terminal over generalized Nakagami-q (Hoyt) fading channels at high signal to noise ratio (SNR) using the moment generating function (MGF) approach. Using this approximated SER expression, optimal power allocation (OPA) scheme under different Hoyt parameters (qij) is developed and investigated. Finally, simulation results are presented to illustrate the performance improvement and power savings due to OPA in the considered cooperative system.

In this paper we study multi-user interference alignment at system level. Interference alignment (IA) is a quite recently proposed transmission strategy with varying precoding solutions. In IA, precoders and receivers are designed cooperatively between transmission points such that interference is aligned in a smaller vector space at each receiver than it would be without IA. One known problem for all IA schemes is that they are prone to external interference, interference from transmission points that are outside of the cooperation set. In this paper, we evaluate the system level performance of a multi-user IA scheme with a 3GPP LTE-Advanced compliant system level simulator. We found that IA is very prone to external interference and that a receiver that is cancelling also external interference is outperforming IA.

The aim of this work is the proposition of the κ-μ/Inverse Gaussian distribution which corresponds to a physical fading model. This is a composite distribution which is based on the κ-μ multipath model and the recently proposed Inverse Gaussian shadowing model. The former is a generalised model which includes as special cases the widely known Nakagamim, Rayleigh, Rice and one sided Gaussian distributions and accounts particularly for Non-Line-of-Sight communications. The latter is a convenient model which was recently shown to characterize shadowing effect more accurately than the widely used gamma distribution. As a result, the proposed composite model provides an overall efficient characterisation of multipath and shadowing effects which typically occur simultaneously. The offered modelling accuracy is achieved thanks to the remarkable flexibility of its parameters which is verified by the fact that the model is capable of providing good fittings to measurement data from realistic communication scenarios. Novel analytic expressions are derived for the probability density function (pdf) and the moments of the κ-μ/Inverse Gaussian composite fading model which includes as special cases the Nakagami-m/Inverse Gaussian, Rayleigh/Inverse Gaussian and Rice/Inverse Gaussian composite fading distributions. The offered expressions can be readily utilized in the derivation of vital performance measures in radio and free-space-optical communications over multipath/shadowing and medium-to-strong atmospheric turbulence, respectively. In this context, a closed-form expression is derived for the Amount of Fading over κ-μ/Ig fading channels.

In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver broadcast multiple input single output (MISO) channel. With shared data information and shared channel quality indication (CQI), rather than sharing the full channel state information (CSI), the two transmitters jointly send information to multiple receivers by fixing the transmit beamforming weights according to Zero Forcing (ZF) principle. The power allocation is then studied to maximize the sum throughput (capacity), which can be modeled as a non-convex optimization problem. Through an application of Karush-Kuhn-Tucker (KKT) conditions, the nonconvex optimization problem is reformulated as a convex one. Then the closed form solution is derived, which takes a form similar to classic water-filling principle but is also combined with an interesting cooperation feature. Moreover, based on the derived solution, an optimal cooperative power allocation algorithm is presented. Finally, numerical simulation results are given to evaluate and demonstrate the performance of the derived joint power allocation scheme.

In this paper, we consider cooperating secondary users (SUs) that are estimating the location and transmit power of an emitter that may be either a primary user (PU) or another SU in the cognitive radio (CR) context. Since SU devices need to be affordable and portable, their capabilities to estimate e.g. the direction of arrival (DOA) are very limited. In addition, the geographical distribution of SUs is random rather than optimized resulting in very stringent conditions for the localization. We therefore analyze possible benefits and performance of hybrid received signal strength (RSS)- DOA based emitter transmit power and position estimation by means of the Cramer-Rao bound (CRB). We show that the RSS-DOA hybrid approach can lead to significantly lower bounds compared to estimation based on only RSS or DOA. Assuming a special case of two SUs at equal distance from the emitter, we derive the CRBs in closed-form and demonstrate that for certain SU-emitter geometries, the RSS-only and DOA-only CRBs become infinite due to the inability to estimate one of the emitter's coordinates. Since those coordinates are orthogonal for RSS and DOA, the hybrid approach does not suffer from these geometries, resulting in a finite CRB. Results from Monte-Carlo simulations are provided which illustrate that significant theoretic gains are possible also for random SU and emitter placements. The results also show that biggest performance gains from joint processing of RSS and DOA are available when the number of cooperating SUs is fairly small, in the order of 2-5.

It is well known that traditional water-filling provides a closed form solution for capacity maximization in orthogonal frequency division multiplex (OFDM) system. In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver model for OFDM systems. The local full channel state information (CSI) is available at the two transmitters respectively, where each transmitter has an individual power constrain. The transmitters first cooperate by sharing CSI, and then jointly optimize power allocation in the metric of sum throughput, which can be modeled as a convex optimization problem. Through an application of Karush-Kuhn-Tucker (KKT) conditions, the convex optimization problem is reformulated as a simplified convex one. Then the closed form solution is derived, which takes a form similar to classic water-filling principle. Based on the solution, the optimal cooperative power allocation algorithm is constructed, the structure of which can be explained as a cooperative water-filling relative to the traditional water-filling. Finally, numerical simulation is given to evaluate and demonstrate the performance of the optimal cooperative water-filling scheme.

Recently, due to the exponentially increasing overall energy consumption of the wireless networks, much attention has been directed to the energy efficiency metrics and minimization of energy consumption, both in battery powered terminal devices as well as in infrastructure devices. In this article, we present an analysis of energy-optimized cooperative network where nodes in the environment share and coordinate their resources to relay the source signal, and compare the achievable performance with the reference performance of direct transmission alone. To improve the energy-efficiency further, the optimal power allocation (OPA) scheme to minimize the energy consumption under certain quality-of-service (QoS) constraint, namely destination symbol error rate (SER), over the Nakagami-m fading channels is derived. Stemming from these derivations, we show that depending on the fading parameter m, significant performance gain can be achieved over the equal power allocation (EPA) scenario and direct transmission schemes. Moreover, the effect of relay location on the energy efficiency over the Nakagami-m fading environment is investigated.

In this paper, we present a performance comparison between IEEE 802.15.4 which specifies the physical and media access control layers for low-rate wireless personal area networks (LR-WPANs) like ZigBee, and IEEE 802.11ah, a new global WLAN standard using sub-1 GHz frequency band, in terms of throughput and energy consumption. Both standards are targeting low power applications with relatively high number of nodes as in IoT and M2M applications. The simulations results demonstrate the better performance of IEEE 802.11ah throughput mainly in congested networks. However in particular cases, the IEEE 802.15.4 outperforms the IEEE 802.11ah from energy consumption point of view.

A quadrature ΣΔ analog-to-digital converter (ADC) is a promising solution for intermediate frequency digitizing software defined cognitive radio (CR) receivers because of, e.g., multiband capability and power efficiency. However, inherent coefficient mismatches between the in-phase and quadrature rails can severely damage the performance of such receiver by creating mirror-frequency interference (MFI). In this article, a novel frequency-agile and reconfigurable transfer function design, allowing digital post-compensation of the MFI, is proposed. The design is based on a novel closed-form transfer function model for higher-order quadrature ΣΔ modulators (QΣΔMs) under implementation inaccuracies, proposed herein. By doing the compensation in digital domain, it is possible to take into account all error-sources of the receiver chain at once, including, e.g., a quadrature mixer before the ADC. This capability is obtained by preserving the mirror-band signal information and using the noise transfer function of a QΣΔM to remove quantization noise from therein. This is demonstrated in a multiband scenario aimed for CR receivers, where a number of frequency channels can be received and detected in parallel. Practical examples of the transfer function analysis under implementation inaccuracies and the post-compensation performance are given with a two-stage QΣΔM, having stage-orders of four, allowing eighth-order noise shaping.

In-phase/quadrature (I/Q) imbalance degrades heavily the image rejection performance of direct-conversion radios. I/Q imbalance also changes the statistics of the received signal, and in particular makes a circular signal non-circular. This fact has been utilized in compensating receiver I/Q imbalances, utilizing second-order statistics. In this article, we investigate whether moment circularity of order higher than two can be exploited in receiver I/Q imbalance compensation. It is established that the fourth-order moment E[x3x*] is a suitable statistic for measuring the circularity of common communications signals based on complex-valued alphabets such asM-QAM andM-PSK withM>; 2. Two blind algorithms, based on Newton's method, are then proposed for receiver I/Q imbalance compensation. They are shown by simulations to converge faster or, alternatively, to give lower steady-state variance, than the reference methods that are based on second-order statistics.

OFDM and other multicarrier waveforms are in general very sensitive to RF non-idealities, such as phase noise and IQ imbalance, of transmitting and receiving devices. Extensive work has been carried out in the open literature in analyzing the performance of OFDM radio link under such RF impairments in terms of detection error rate and mostly concentrating on one impairment at a time. However, there is only very limited work on analytical investigations of mutual information and rate loss expressions, the heart of communication theory, as functions of RF impairment levels. In this article, we derive two closed-form mutual information expressions, in the form of infinite series representation, for an arbitrary subcarrier of a general OFDM radio link impaired with transceiver phase noise and IQ imbalance in frequency-selective Rayleigh distributed block-fading radio channel, covering both uncorrelated as well as fully correlated mirror subcarrier scenarios. We also show that the mutual information saturates to a finite value due to the inherent RF impairments even in the case that the symbol-to-noise ratio approaches infinity. Extensive comparisons with results obtained from full OFDM radio link simulations are also provided to illustrate and verify the accurate match between analytical and simulated mutual information behavior.

We propose a discrete-time model for phase noise processes occurring in phase locked loop (PLL) based oscillators. By using linear time-invariant analysis in the phase domain, we arrive at a parallel auto-regressive moving-average phase noise model that captures the major noise sources and corresponding loop transfer functions of the PLL circuitry. The acquired model allows us to derive analytically the distribution of the inter-carrier interference (ICI) power in the receiver, shown to be a sum of correlated gamma random variables with a model-specific covariance matrix. Finally, we utilize the derived ICI distribution and proceed to analyze the effects of phase noise and multipath fading on the performance of OFDM radio links. For example, we evaluate the rate loss without ICI compensation or knowledge at the receiver, and illustrate the trade-off introduced by phase noise between the OFDM symbol length and the overhead caused by the cyclic prefix. Throughout the paper, simulations confirm the accuracy of the analytical expressions.

This paper proposes a time-domain digital signal processing method for estimating and mitigating transmitter and receiver oscillator phase-noise effects in OFDM radio systems on receiver side of the link. The idea is based on re-constructing time-domain OFDM signal at the receiver from initially detected symbols and using this as a reference in phase noise estimation. The knowledge of heavily low-pass nature of realistic phase noise processes is then utilized in the estimation process to improve the estimation performance. The algorithm can also be used iteratively, inside individual OFDM symbols, to further improve the accuracy of the obtained phase noise estimates. Performance analysis shows that the proposed algorithm outperforms existing state-of-the-art phase noise mitigation techniques, under both additive white Gaussian noise and extended ITU-R Vehicular A multipath channels.

I/Q signal processing based communication systems suffer from analog front-end (FE) imperfections such as in-phase and quadrature-phase (I/Q) imbalance and carrier frequency offset (CFO). These impairments are commonly encountered in all practical implementations, and severely degrade the obtainable link performance. Moreover, orthogonal frequency division multiplexing (OFDM)-based systems are particularly sensitive to radio frequency (RF) impairments. In this paper, we analyze the impact of transmitter and receiver I/Q imbalance together with channel distortion and CFO error on an ideal transmit signal, and propose low-complexity DSP algorithms and compensation structure for coping with such imperfections. Based on our proposed estimation/compensation structure, we are able to decouple the impairments and process them individually with rather low-complexity. More specifically, we first apply a blind algorithm for receiver I/Q imbalance compensation, followed by an efficient time domain CFO estimator and compensator. The transmitter I/Q imbalance and channel are then equalized jointly, in the frequency domain, with maximum-likelihood (ML) or zero-forcing (ZF) schemes, respectively. The applied algorithms are either blind working without aid of any training symbol or use only one OFDM symbol for impairments estimation, providing an efficient alternative solution with reduced complexity. The computer simulation results indicate a close to ideal performance of ZF scheme, and suggest that additional performance improvement due to frequency diversity can be obtained when ML estimation technique is employed.

In LTE-Advanced, multiuser MIMO has been identified as a key technique for increasing system spectral efficiency in closed-loop MIMO. Transmission parameters like precoding and transmission rate are adapted based on finite rate feedback from the users. Switching between single user and multiuser transmission modes is possible without higher layer signaling, which means that the feedback should be designed to sustain efficiently both single user and multiuser transmissions. In this paper, performance of LTE-Advanced is evaluated with system level simulations. The focus is on studying the viability of an additional multiuser specific channel quality feedback. The baseline in the comparison is single stream single user feedback consisting of a preferred precoding vector and a channel quality feedback. This baseline is compared to feedback schemes that aim at improving the multiuser performance by an additional multiuser-specific CQI. Also, the gain from a user specific scaling for the channel quality feedback performed by the base station is evaluated.

This article addresses the signal distortion caused by receiver phase noise (PN) on OFDM waveforms in direct-conversion radio receivers. A closed-form solution for the observed signal-to-interference-plus-noise ratio (SINR) is derived, describing the level of intercarrier interference (ICI) stemming from PN. Compared to existing literature, the analysis is valid for arbitrary oscillator spectral shape, the only assumption being that reasonably small phase noise values are observed. The analysis results can be used to derive practical circuit-level oscillator design criteria in terms of the allowable PN spectral density. The applicability and validity of the derived analysis are verified with extensive computer simulations.

Maximizing system sum-rate or minimizing error rate with constrained power are the main research focuses in the previous system-oriented power allocation schemes. However, link-oriented power allocation schemes that minimize power consumption with constrained rates for every link, attracted researchers in recent energy efficiency system design. In this paper, a link-oriented optimal power allocation scheme is firstly proposed for physical layer network coding with DeNoise-and-Forward protocol in multicast system, that two sources and two destinations communicating with the assistance of arbitrary number of relays. We also consider a suboptimal power allocation scheme with low complexity, where a single relay is chosen to minimize the power consumption. The numerical simulation shows that the suboptimal power allocation scheme also can achieve relatively low power consumption, even approach to the optimal scheme in some special case.

Building compact and low-cost yet flexible and reconfigurable radios for future wireless systems is generally a challenging task. On one hand, the needs for flexibility and re-configurability prevent using dedicated hardware particularly designed and optimized for only a single application or part of the radio spectrum. And on the other hand, to keep the overall size and cost of the radio equipment feasible, especially in multi-antenna multi-radio scenarios, the cost and size of individual radios are strongly limited. As a result, various imperfections and impairments are expected to take place in the used radio transceivers, especially in the radio frequency (RF) analog electronics. Good examples of such imperfections are, e.g., mirror-frequency interference due to I/Q imbalance, non-linear distortion due to mixer and amplifier nonlinearities, timing jitter and non-linearities in sampling and analog-to-digital (A/D) converter circuits, and oscillator phase noise. These impairments, if not properly understood and taken into account, can easily become a limiting factor to the quality and performance of the radio device and thereon of the whole wireless link. This is even more so, when more complex and more sensitive high-order modulated wideband communications waveforms are being deployed in the future systems. This article gives an overview of the essential RF impairments in modern radio communication context. Furthermore, different conceptual alternatives for reducing the effects of RF impairments in radio transmitters and receivers utilizing digital signal processing are described.

This paper addresses the sampling jitter estimation and cancellation task in direct RF sub-sampling type radios. The proposed jitter estimation method is based on carefully injecting or superimposing an additional known reference signal to the received signal at the sampler input. Proper digital signal processing methods are then devised and applied to estimate the sampling jitter realizations from the obtained jittered samples. Using these jitter estimates, combined with proper jitter modelling, the jitter effects can then be efficiently removed from the actual received signal. Careful performance analysis of the overall estimation-cancellation scheme is also carried out using computer simulations with 3GPP LTE type multicarrier signals, assuming also different amounts of RF filtering prior to RF sub-sampling stage. In the performance simulations, both additive white Gaussian noise and extended ITU-R Vehicular A multipath radio channel types are considered.

In this contribution we introduce a new digital signal processing (DSP)-oriented implementation of a feedforward linearizer. The intermodulation distortion (IMD) signal is regenerated entirely in the digital baseband which gives better control over the linearization process and also improves the efficiency of the overall amplifier. A new scheme to estimate the parameters of the proposed feedforward linearizer is presented which decouples the parameters of the signal and error cancellation loop. We have verified the new DSP oriented feedforward linearizer by means of simulations in the presence of component impairments like, e.g., IQ mismatch. Initial measurements results in a laboratory setup show a reduction of the adjacent channel power of approximately 15 to 20 dB.

This paper studies the impact of one important radio transceiver impairment, namely the I/Q imbalance, on the link performance of spatial multiplexing MIMO-OFDM systems. A closed-form solution for the effective signal-to-interference-plus-noise-ratio (SINR) at the input of the receiver detection stage due to frequency-selective transmitter and receiver I/Q imbalances is derived, taking also the effects of fading multipath radio channel and additive noise into account. As will be shown, the derived SINR can be directly mapped to the achievable detection error rate at high SNR regime, yielding a valuable analytical tool for radio transceiver designers to analyze the imbalance effects at link-level without any actual data simulations. The analytical outcomes are also verified using extensive computer simulations backing up the theoretical studies. In general, the obtained results indicate that I/Q imbalance can easily become a limiting factor to the achievable link performance in future spatial multiplexing MIMO-OFDM systems, and thus should be carefully mitigated using proper digital and/or analog signal processing.

This paper presents a new way to address and mitigate sampling jitter in high-frequency bandpass-sampling OFDM radio receivers. Baseband model for mapping the sampling jitter to certain type of phase noise is first presented, and stemming from this model, state-of-the-art phase noise mitigation techniques are then proposed to remove the jitter- induced signal distortion. Performances of the proposed jitter mitigation techniques are analyzed with extensive computer simulations in high-speed bandpass sampling multicarrier system context. In the link performance simulations, both additive white Gaussian noise (AWGN) and extended ITU-R vehicular A multipath (eVehA) radio channel types are used, combined with realistic sampling clock and jitter modelling.

I/Q imbalance is one of the main practical obstacles in the implementation of direct-conversion receivers. This paper presents novel DSP-based techniques for the estimation and compensation of frequency-selective receiver I/Q imbalances in OFDM systems. The estimation is based on a special pilot or preamble structure, and frequency-domain smoothing is utilized to effectively reduce the effect of noise. Reliable estimation is attained with a minimum of two OFDM symbols. Further, it is shown that estimation of frequency-selective I/Q imbalance and the frequency-selective radio channel can be decoupled, which is beneficial from the computational complexity point of view. Simulation analysis shows impressive performance with fast convergence.

Evolved UTRA (EUTRA) is currently under standardization within 3GPP as a long-term evolution (LTE) of universal terrestrial radio access (UTRA) air interface. Due to the potentially large number of VoIP users served in this system, related control signaling overhead for VoIP transmissions with traditional full dynamic scheduling scheme could become a bottleneck and limit the system capacity to a greater extent. In this paper, we present a semi-persistent packet scheduling scheme for VoIP to explore the capacity within the limitation of control channel resources. System level simulation results show that semi-persistent scheduling scheme can reduce control channel usage without using packet bundling technique. With 6 control channels, around 310 users per sector can be supported in 5 MHz bandwidth with both discontinuous reception (DRX) enabled and without packet bundling.

This paper presents a pilot-based compensation algorithm for mitigation of frequency-selective I/Q imbalances in direct-conversion OFDM transmitters. By deploying a feedback loop from RF to baseband, together with a properly-designed pilot signal structure, the I/Q imbalance properties of the transmitter are efficiently estimated in a subcarrier-wise manner. Based on the obtained I/Q imbalance knowledge, the imbalance effects on the actual transmit waveform are then mitigated by baseband pre-distortion acting on the mirror-subcarrier signals. The compensation performance of the proposed structure is analyzed using extensive computer simulations, indicating that very high image rejection ratios can be achieved in practical system set-ups with reasonable pilot signal lengths.

This paper presents the design of VoIP optimized packet scheduler and performance simulations for supporting voice services over downlink evolved UTRA. An efficient packet scheduler taking into account the traffic characteristics and adopting optimal resource allocation is proposed. Results are given for different control channel number settings and packet bundling options. Simulation results show that downlink EUTRA VoIP capacity is mainly limited by control channel number with full dynamic scheduling and packet bundling can help to increase capacity within the control channel limitation. In summary, with packet bundling technique, around 378 and 488 users per sector can be supported in 5 MHz bandwidth with 6 and 8 control channels respectively.

Gain and phase differences between the analog in-phase (I) and quadrature (Q) branches of a quadrature receiver are unavoidable, and seriously degrade its image rejection capabilities. Furthermore, this so-called I/Q imbalance problem is in general a frequency-dependent phenomenon, which is often ignored in many otherwise excellent work. In this paper, we take this frequency-dependency into account and study a class of I/Q imbalance compensators based on widely linear (WL) processing of the received mismatched signal, under the assumption that the ideal baseband signal is proper (or circular). In other words, the complementary autocorrelation function of the ideal baseband signal is assumed to vanish, which is a valid assumption for most practical communications signals. Under I/Q imbalance the observed baseband equivalent signal becomes improper, and I/Q imbalance compensation can be performed by making the observation proper again. We propose a simple blind (non-data aided) WL compensator structure for suppressing the mirror-frequency interference. It shows impressive performance and has many additional desirable features, such as immunity to channel noise and the fading channel.

In this paper, we consider blind diversity reception and interference rejection in multi-antenna communications context, in terms of maximizing the output signal-to-interference-and-noise ratio (SINR). More specifically, we demonstrate that independent component analysis (ICA), although originally designed for noise-free linear models, is able to provide essentially the best possible output SINR among all linear transformations of received data in noisy linear models. In particular, our experiments indicate that one of the most widely applied ICA algorithms, equivariant adaptive source identification (EASI) algorithm, is, in practice, identical with SINR maximizing generalized eigenfilter in terms of SENR, even though it does not use explicit knowledge of the channel states and noise statistics. We also show that, in a special case of interference-free (that is, noise only) system, the EASI algorithm attains the greatest diversity gain blindly, i.e., performs as a blind maximal ratio combiner (MRC).

The combination of orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) techniques has been widely considered as the most promising approach for building future wireless transmission systems. In such systems, the limited implementation resources for the radio parts cause big restrictions on the size, cost, and quality of the individual radio transceivers. This implies that there are several imperfections at the analog radio front-end stages, which in turn can severely limit the overall system performance. One good example is the so called I/Q imbalance problem related to the amplitude and phase matching of the transceivers I and Q chains. This paper studies the performance of space-time coded (STC)-OFDM systems under I/Q imbalance. As a practical example, a 2 × 1 STC-OFDM system is examined in detail and a closed-form solution for the resulting signal-to-interference ratio (SIR) due to I/Q imbalance at the output of the receiver combining stage is derived. The analytical outcomes are verified using extensive computer simulations, and can easily be extended to multi-antenna receiver cases as well. In general, the obtained results indicate that I/Q imbalance can easily become a limiting factor in practical STC-OFDM systems and should be carefully mitigated using proper digital and/or analog signal processing.

The so-called single-carrier FDMA (SC-FDMA) waveform class, also known as DFT-spread OFDM, is a special form of multicarrier modulation, and has received a lot of interest in 3.9G system context recently. This paper addresses the radio implementation and RF impairment issues related to the reception of SC-FDMA waveforms. The main emphasis is on the so-called I/Q imbalance problem which in general results in imperfect attenuation of the mirror-frequencies. Here, the role of mirror-frequencies and mirror-frequency interference is addressed in detail from the SC-FDMA signals point of view, covering both the localized and distributed transmission modes. Furthermore, under perfect I/Q balance, it is shown that SC-FDMA signals satisfy certain circularity conditions related to the second-order statistics of complex random signals. This circularity, on the other hand, is lost due to I/Q imbalance which can then be used as a basis for developing efficient digital signal processing (DSP)-based algorithms to compensate for the I/Q imbalance effects. Comprehensive system simulations are carried out in both the localized and distributed spectral deployment cases, with and without digital compensation, to illustrate the relative effects of I/Q imbalances and the compensation stage on the overall system performance.

In this article, we devise a blind digital signal processing (DSP) method for mitigating the dynamic offset interference due to self-mixing of radio frequency (RF) interferers in multiantenna diversity receiver context, utilizing the direct-conversion radio architecture. The proposed method is based on independent component analysis (ICA) and does not require any channel state information or knowledge about the physical self-mixing or interference characteristics. Moreover, we demonstrate through computer simulations that the overall achievable signal-to-interference-plus-noise ratio (SINR) obtained using the proposed technique is extremely close to the maximum SINR achievable by any linear method. Thus the ICA processing yields optimum compromise between the processing of noise and offset interference in this sense. We also demonstrate through concrete design examples how the proposed method helps relaxing certain constraints on the receivers' RF components.

When targeting receiver integrability and flexibility, the choice of the front-end architecture is in key position. Instead of the traditional superheterodyne architecture, more recent I/Q or quadrature receiver front-ends are receiving increasingly more interest. One crucial aspect in quadrature receiver front-ends is the amplitude and phase matching of the analog I and Q signal branches. I/Q mismatches result in incomplete image signal or mirror frequency attenuation which must be enhanced using additional analog or digital signal processing. This paper presents and analyzes a novel DSP-based blind (non-data-aided) technique for I/Q imbalance compensation, utilizing a property of the ideal baseband equivalent called circularity. The weights of the compensator are computed directly from the estimates of the second-order moments of the mismatched baseband equivalent received signal. This algorithm is further simplified, with minimal loss in performance, yielding an extraordinarily simple yet effective compensation technique which ideally triples the dB value of the analog front-end image rejection ratio. The algorithms are applicable to any I/Q receiver structure, whether single-channel or multi-channel, and are in fact totally independent of any specific structure or characteristic (other than circularity) of the ideal baseband equivalent signal

The direct-conversion or homodyne receiver principle has received lots of interest in recent years for building flexible and compact radios. The homodyne architecture is, however, found to be rather sensitive to certain non-idealities of the analog front-end components. One big concern here is the amplitude and phase mismatches of the I and Q signal branches, resulting in insufficient rejection of the mirror frequency band. This paper presents an efficient low-order statistics based method (stat) for digital I/Q mismatch compensation, and compares the proposed technique with the blind signal separation (BSS) and hard decision (HD) based compensators proposed earlier by the authors. In addition to algorithm developments, big emphasis in the paper is on performance measurements and verifications carried out using measured true-world receiver front-end signals obtained from a laboratory homodyne measurement system. In general, the obtained results indicate that I/Q imbalance can be efficiently compensated using the proposed ideas. Even with really high-order modulation techniques, such as 64-QAM, close-to additive noise bound compensator performance is demonstrated using the measured signals

This paper investigates the performance of LDPC coded MC-CDMA systems. Turbo multiuser detection is exploited by combining per-user MMSE-PIC detection and LDPC decoder together. In this way, error propagation can generally be averted for lower number of subcarriers. Simulation results show that the proposed scheme achieves about 4 dB coding gain for 256 subcarriers at BER= 10-4. The performance degrades only slightly with smaller spreading factors per MC-CDMA multiplex. Thus, from practical point of view, smaller spreading factors can be utilized to reduce the complexity of the detector as a whole.

This letter addresses the blind signal estimation problem in the so-called conjugate signal model, where the observed signal is a linear combination of the desired signal and its complex conjugate. It will be shown that blind signal recovery in this kind of signal model is feasible using only the second-order statistics of the observed signal, under the assumption of circular or proper complex signals. Furthermore, one practical example application in the field of communications receiver signal processing will be given, where the image signal interference caused by amplitude and phase mismatches of the receiver analog branches is digitally attenuated. In addition to the analytical results and practical implementation algorithms, the efficiency of the proposed estimation concepts in the digital image rejection application is evaluated using computer simulations, showing impressive performance results.

In quadrature receivers, unavoidable imbalances in the analog front-end between the I- and Q-branches result in finite and usually insufficient rejection of the image frequency band. This causes the image signal to appear as interference on top of the desired signal. Both analog and digital techniques to compensate the effects of I/Q imbalance have been presented in the literature. In this paper, we carry out a detailed performance analysis of the interference cancellation based imbalance compensation structure utilizing baseband digital signal processing. Also simulation results are provided for comparison. The results indicate that the interference canceller based solution can offer adequate performance for most communication applications.

Quadrature downconversion to a very low-IF frequency is a promising idea but it has one serious problem. Unavoidable imbalances in the analog front-end between the I- and Q-branches result in finite and usually insufficient rejection of the image frequency band which causes interference. We carry out a signal analysis of an imbalanced low-IF receiver and propose a novel baseband correction structure utilizing an adaptive interference canceller. Theoretical and simulation results are provided in order to evaluate the performance of the proposed idea. The results indicate that the I/Q imbalance can be effectively compensated during the normal operation of the receiver, even in the rapidly changing case, as long as a linear system model for the imbalance is valid.