Search

Positioning in indoor scenarios using signals of opportunity is an effective solution enabling accurate and reliable performance in Global Navigation Satellite System (GNSS)-obscured scenarios. Despite the availability of numerous fingerprinting datasets utilizing various wireless signals, the challenge of device heterogeneity and sample density remains an unanswered issue. To address this gap, this work introduces TUJI1, an anonymized IEEE 802.11 Wireless LAN (Wi-Fi) fingerprinting dataset collected using 5 different commercial devices in a fine-grained grid. The dataset contains the matched fingerprints of Received Signal Strength Indicator (RSSI) measurements with the corresponding coordinates, split into training and testing subsets for effortless and fair reproducibility.

Integrated Access and Backhaul (IAB) technology promises to facilitate cost-effective deployments of 5G New Radio (NR) systems operating in both sub-6 GHz and millimeter-wave (mmWave) bands. As full-duplex wireless systems are in their infancy, initial deployments of IAB networks may need to rely on half-duplex operation to coordinate transmissions between access and backhaul links. However, the use of half-duplex operation not only makes the scheduling of links in the IAB networks interdependent, but also the number of their feasible combinations grows exponentially with the network size, thereby posing challenges to the efficient design of such systems. In this paper, by accounting for mmWave radio characteristics, we propose a joint resource allocation and link scheduling framework to enhance the user equipment (UE) throughput in multi-hop in-band IAB systems. We keep the problem in the form of linear programming type for the feasibility of the practical applications. We show that the increased number of uplink and downlink transmission time interval (TTI) configurations does not result in improved UE throughput as compared to two-TTI configuration. Further, we demonstrate that in-band IAB systems tend to be backhaul-limited, and the utilization of multi-beam functionality at the IAB-donor alleviates this limitation by doubling the average UE throughput. Finally, we show that the use of proportional-fair allocations allows the average UE throughput to be improved by around 10% as compared to the max-min allocations.

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 the hardware impairments. These impairments 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 the so-called RiFyFi_VDG, referring to Radio Frequency Fingerprint Virtual Database Generator, and explore individually the impairment impact on the classification accuracy to highlight the most relevant impairment. Different transmission scenarios are then explored, such as the impact of the data type (being a preamble or a payload) and the data size. Design rules of real databases are finally drawn for the different scenarios. We found out that the power amplifier imperfections play the biggest role in RFF accuracy and that average accuracy levels of 94% can be reached when combining the various hardware impairments at the transmitter.

Abstract Wireless communication and radars will play a crucial role for autonomous vehicles in the nearest future. However, the blockage caused by surrounding cars can degrade communication performance, while automotive radars are never aimed to operate in such conditions. Therefore, in this paper, the authors propose the concept of near‐ground propagation, reducing the blockage effect in the road traffic conditions. Specifically, the radio waves may freely propagate under the blocking car's bottom if the antennas are placed as low as possible to the road. Based on the measured and modelled results presented in the paper, it may be claimed that near‐ground communication and radar sensing are feasible and may combat even heavily obstructed cases. Nevertheless, some challenges associated with antenna locations were encountered. For example, it was discovered that antenna height at 0.5 m acts less effectively against blockage than at 0.3 m. Next, the 27 dB excess loss at the 0.5 m antenna height in the radar deployment is larger than 17 dB at 0.3 m. In its turn, the higher ground clearance of the blocking vehicle positively affects the near‐ground performance. Additionally, the signal propagation at the grazing angle crucially reduces the relevant losses.

Driven by the extended applications and scarce spectrum resources, integrating the radio sensing functions into the future mobile system has been a consensus between the stakeholders. This paper demonstrates the feasibility of joint localization and imaging functions by exploiting the reference signal in the radio frame defined in the mobile system. Essentially, the subspace-based algorithms are adopted to jointly estimate the angle and distance and to enable the localization function for uplink and downlink signals and the derivation of theoretical performance boundaries. The vector antenna and virtual array concepts are introduced in the downlink scenario to enhance the angle estimation resolution. The uplink sounding reference signal is exploited to enable the imaging function as analog to synthetic-aperture radar (SAR). The joint localization and imaging performances are verified with a realistic ray-tracing channel based on the 3D ground and buildings model of Muret airport in France. The simulation results show that the reference signal can provide acceptable localization accuracy and target-distinguishing capability by adopting a virtual array and joint time-spatial smoothing in downlink and uplink, respectively. The joint localization and imaging results prove that the future mobile network is potentially a viable infrastructure to provide economic surveillance solutions for airports, particularly for secondary airports that are not well equipped with dedicated surveillance systems.

In this paper, a novel waveform with low peak-to-average power ratio (PAPR) and high robustness against phase noise (PN) is presented. It follows the discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) signal model. This scheme, called 3MSK, is inspired by continuous-phase frequency shift keying (CPFSK), but it uses three frequencies in the baseband model – specifically, 0 and $\pm f_{symbol}/4$ , where $f_{symbol}$ is the symbol rate – which effectively constrains the phase transitions between consecutive symbols to 0 and $\pm \pi /2$ rad. Motivated by the phase controlled model of modulation, different degrees of phase continuity can be achieved, allowing to reduce the out-of-band (OOB) emissions of the transmitted signal, while supporting receiver processing with low complexity. Furthermore, the signal characteristics are improved by generating an initial time-domain constant envelope signal at higher than the symbol rate. This helps to reach smooth phase transitions between 3MSK symbols, while the information is encoded in the phase transitions. Also the possibility of using excess bandwidth is investigated by transmitting additional non-zero frequency bins outside the active frequency bins of the basic DFT-s-OFDM model, which provides the capability to greatly reduce the PAPR. The most critical tradeoffs of the oversampled schemes are that improved PAPR is achieved with the cost of somewhat reduced link performance and, in case of excess band, also the spectrum efficiency is reduced. Due to the fact that the information is encoded in the phase transitions, a receiver model that tracks the phase variations without needing reference signals is developed. To this end, it is shown that this new modulation is well-suited for non-coherent receivers, even under strong phase noise (PN) conditions, thus allowing to reduce the overhead of reference signals. Evaluations of this physical-layer modulation and waveform scheme are performed in terms of transmitter metrics such as PAPR, OOB emissions and achievable output power after the power amplifier (PA), using a practical PA model. Finally, coded radio link evaluations are also provided, demonstrating that 3MSK has a similar bit error rate (BER) performance as that of traditional quadrature phase-shift keying (QPSK), but with significantly lower PAPR, higher achievable output power, and the possibility of using non-coherent receivers.

Abstract In time division duplexing based mobile networks, under certain atmospheric ducting conditions, the uplink reception may be interfered by the downlink transmissions of remote base-stations (gNBs) located hundreds of kilometers away. This paper addresses such remote interference problem in a 5G new radio (NR) macro deployment context. Specifically, two potential reference signal (RS) designs for remote interference management (RIM) are described. The first signal structure, denoted as the one OFDM symbol (1OS) based RIM-RS, is building on the channel state information reference signals of 5G NR. The second candidate is referred to as the two OFDM symbol based RIM-RS design, which builds on the design principles of LTE RIM-RS. The achievable detection performance is evaluated by introducing enhanced receiver algorithms together with three feasible propagation delay based gNB grouping and corresponding RIM-RS transmissions schemes. The performance results in terms of the receiver processing gain highlight that the improved detection algorithm assures sufficient performance to detect the remote interference for both RIM-RSs with all evaluated frequency domain comb-like patterns. The benefit of grouping corresponding RIM-RS transmissions from gNBs located on the same area is greater when using same frequency domain resources per transmitted sequence in practical interference scenarios. Furthermore, applying a common base sequence for all gNBs within a group allows to identify the group based on detected sequence and enables adaptive RIM mitigation schemes. On the other hand, it is shown that the 1OS RIM-RS provides smaller overhead and can be frequency multiplexed with the physical downlink shared channel, which opens up the possibility of using gNB group wise 1OS RIM-RS also for UE interference measurements.

The physical-layer radio access of 5G New Radio (NR) and other modern wireless networks builds on the cyclic prefix (CP) orthogonal frequency-division multiplexing (OFDM), known to suffer from the high peak-to-average power ratio (PAPR) challenge. In this article, novel PAPR reduction methods are developed, referred to as the iterative clipping and weighted error filtering (ICWEF) approach. To this end, clipping noise is separated from the data signal in frequency domain and properly tailored frequency-selective clipping noise filtering is adopted to control the tradeoff between PAPR reduction and transmitted signal quality. Furthermore, as 5G NR networks support adopting different OFDM numerologies at different bandwidth parts within one channel bandwidth, the ICWEF approach is also extended to take into account and suppress the resulting inter-numerology interference-something that most existing state-of-the-art methods do not consider. To facilitate comprehensive performance evaluations, a software-defined radio based prototyping testbed including a high-power base station power amplifier is also developed and used for assessing the performance of PAPR reduction solutions. The proposed ICWEF-based PAPR reduction concept is thereon thoroughly validated with extensive numerical and experimental results and shown to outperform the existing state-of-the-art reference solutions.

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.

This paper describes and investigates a novel concept of frequency-domain spectral shaping (FDSS) with spectral extension for the uplink (UL) coverage enhancement in 5G New Radio (NR), building on discrete Fourier transform spread orthogonal frequency-domain multiplexing (DFT-s-OFDM). The considered FDSS concept is shown to have large potential for reducing the peak-to-average-power ratio (PAPR) of the signal, which directly impacts the feasible maximum transmit power under practical nonlinear power amplifiers (PAs) while still meeting the radio frequency (RF) emission requirements imposed by the regulations. To this end, the FDSS scheme with spectral extension is formulated, defining filter windows that fit to the 5G NR spectral flatness requirements. The PAPR reduction capabilities and the corresponding maximum achievable transmit powers are evaluated for a variety of bandwidth allocations in the supported 5G NR frequency ranges 1 and 2 (FR1 and FR2) and compared to those of the currently supported waveforms in 5G NR, particularly $\pi /2$ -BPSK with FDSS without spectral extension and QPSK without FDSS. Furthermore, an efficient receiver structure capable of reducing the noise enhancement in the equalization phase is proposed. Finally, by evaluating the link-level performance, together with the transmit power gain, the overall coverage enhancement gains of the method are analyzed and provided. The obtained results show that the spectrally-extended FDSS method is a very efficient solution to improve the 5G NR UL coverage clearly outperforming the state-of-the-art, while being also simple in terms of computational complexity such that the method is implementation feasible in practical 5G NR terminals.

The extension from centimeter wave frequencies to millimeter wave (mmWave) frequencies has triggered an enormous transformation in terms of radio access architecture for future wireless networks, and it has therefore empowered unlimited opportunities for the user-oriented services and applications. Besides mmWave as a driving element, beamforming (BF) will be incorporated as a key enabling technology for the future wireless networks. In this paper, we propose a positioning-aided beamforming (PA-BF) framework for enhanced downlink communications in a cloud-oriented mmWave mobile networks. We show that the proposed PA-BF achieves a higher effective transmit ratio that is equivalent to a lower initial access latency than the conventional codebook-based BF, which in turn manifests its capability to support high-velocity mobile users. We also analyze the impact of positioning accuracies on the performance of PA-BF and discuss the trade-offs between different BF strategies with varied system parameters. Our simulation results demonstrate that, with a narrow beam phased array and reasonably good positioning accuracy, the PA-BF framework is capable of achieving higher spectral efficiency than the considered codebook-based BF especially at higher velocities.

In wireless communications, higher transmission power enables higher coverage or higher data rate. However, due to hardware limitations, achieving high power efficiency becomes challenging. The main issue is that at high power region close to power amplifier (PA) saturation point the highly non-linear response of the PA leads to significant spectral regrowth. In such a case, waveforms with inherently good spectral containment allow for more spectral degradation and can be seen as the most effective solution for the problem. In this study, a fifth-generation new radio (5G NR) user equipment (UE) transmit power is improved by utilizing fast-convolution filtered orthogonal-frequency-division-multiplexing (FC-F-OFDM) waveform, which has an excellent spectral containment performance. A novel method is proposed for improving the peak-to-average-power ratio (PAPR) of FC-F-OFDM waveform, based on applying clipping before FC processing and allocating the clipping noise that stems from the applied clipping, over not only on active band, but a wider band consisting of both the in-band and guard-band regions. An accurate transmitter chain simulator including a measured memory-polynomial model of a practical PA is used to evaluate a wide set of different subcarrier spacings and channel bandwidths. Then, to validate the numerical results, a software-defined radio (SDR) based testbed is created and the modeled PA is used in this testbed. Weighted overlap-and-add (WOLA) based OFDM, also with clipping, is used as a reference in both the numerical evaluations and in measurements. For both waveforms, the transmitted signal quality, out-of-band emissions, and maximum PA output powers are measured under 5G NR specifications and results for different subcarrier spacings and channel bandwidths are provided to prove the benefits and robustness of the presented FC-F-OFDM approach.

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across time-varying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for run-time adaptation.

Techniques for wireless energy harvesting (WEH) are being emerged as a fascinating set of solutions to extend the lifetime of energy-constrained wireless networks. They are commonly regarded as a key functional technique for almost perpetual communications. With the WEH technology, wireless devices are able to harvest energy from, e.g., different light sources or RF signals broadcast by ambient/dedicated wireless transmitters to support their operation and communications capabilities. The WEH technology will have increasingly wider range of use in upcoming applications for, e.g., wireless sensor networks, machine-to-machine (M2M) communications, and the Internet of Things (IoT). In this paper, the usability and fundamental limits of solar cell or photovoltaic harvesting-based M2M communication systems are studied and presented. The derived theoretical bounds are in essence based on the Shannon capacity theorem, combined with selected propagation loss models, assumed additional link nonidealities, as well as the given energy harvesting and storage capabilities. Fundamental performance limits and available capacity of the communicating link are derived and analyzed, together with extensive numerical results evaluated in different practical scenarios, including realistic implementation losses and the state-of-the-art printed supercapacitor performances. In particular, low-power sensor-type communication applications using passive wake-up radio (WuR)-assisted operation are addressed in this paper. The results show the benefits of using passive WuR, especially when the number of nodes is small. Moreover, the presented analysis principles and results establish clear feasibility regions and performance bounds for WEH-based low rate M2M communications in the future IoT networks.

Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases—fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23–57%) and improved robustness against PL model fitting errors.

The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.

Reconfigurable antenna systems have gained much attention for potential use in the next generation wireless systems. However, conventional direction-of-arrival (DoA) estimation algorithms for antenna arrays cannot be used directly in reconfigurable antennas due to different design of the antennas. In this paper, we present an adjacent pattern power ratio (APPR) algorithm for two-port composite right/left-handed (CRLH) reconfigurable leaky-wave antennas (LWAs). Additionally, we compare the performances of the APPR algorithm and LWA-based MUSIC algorithms. We study how the computational complexity and the performance of the algorithms depend on number of selected radiation patterns. In addition, we evaluate the performance of the APPR and MUSIC algorithms with numerical simulations as well as with real world indoor measurements having both line-of-sight and non-line-of-sight components. Our performance evaluations show that the DoA estimates are in a considerably good agreement with the real DoAs, especially with the APPR algorithm. In summary, the APPR and MUSIC algorithms for DoA estimation along with the planar and compact LWA layout can be a valuable solution to enhance the performance of the wireless communication in the next generation systems.

The exponential growth of mobile data in macronetworks has driven the evolution of communications systems toward spectrally efficient, energy efficient, and fast local area communications. It is a well-known fact that the best way to increase capacity in a unit area is to introduce smaller cells. Local area communications are currently mainly driven by the IEEE 802.11 WLAN family being cheap and energy efficient with a low number of users per access point. For the future high user density scenarios, following the 802.11 HEW study group, the 802.11ax project has been initiated to improve the WLAN system performance. The 3GPP LTE-advanced (LTE-A) also includes new methods for pico and femto cell's interference management functionalities for small cell communications. The main problem with LTE-A is, however, that the physical layer numerology is still optimized for macrocells and not for local area communications. Furthermore, the overall complexity and the overheads of the control plane and reference symbols are too large for spectrally and energy efficient local area communications. In this paper, we provide first an overview of WLAN 802.11ac and LTE/LTE-A, discuss the pros and cons of both technology areas, and then derive a new flexible TDD-based radio interface parametrization for 5G local area communications combining the best practices of both WiFi and LTE-A technologies. We justify the system design based on local area propagation characteristics and expected traffic distributions and derive targets for future local area concepts. We concentrate on initial physical layer design and discuss how it maps to higher layer improvements. This paper shows that the new design can significantly reduce the latency of the system, and offer increased sleeping opportunities on both base station and user equipment sides leading to enhanced power savings. In addition, through careful design of the control overhead, we are able to improve the channel utilization when compared with LTE-A.

Techniques for wireless energy harvesting (WEH) are emerging as a fascinating set of solutions to extend the lifetime of energy-constrained wireless networks, and are commonly regarded as a key functional technique for almost perpetual communications. For example, with WEH technology, wireless devices are able to harvest energy from different light sources or Radio Frequency (RF) signals broadcast by ambient or dedicated wireless transmitters to support their operation and communications capabilities. WEH technology will have increasingly wider range of use in upcoming applications such as wireless sensor networks, Machine-to-Machine (M2M) communications, and the Internet of Things. In this paper, the usability and fundamental limits of joint RF and solar cell or photovoltaic harvesting based M2M communication systems are studied and presented. The derived theoretical bounds are in essence based on the Shannon capacity theorem, combined with selected propagation loss models, assumed additional link nonidealities, diversity processing, as well as the given energy harvesting and storage capabilities. Fundamental performance limits and available capacity of the communicating link are derived and analyzed, together with extensive numerical results evaluated in different practical scenarios, including realistic implementation losses and state-of-the-art printed supercapacitor performance figures with voltage doubler-based voltage regulator. In particular, low power sensor type communication applications using passive and semi-passive wake-up radio (WuR) are addressed in the study. The presented analysis principles and results establish clear feasibility regions and performance bounds for wireless energy harvesting based low rate M2M communications in the future IoT networks.

Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heard WiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.

In modern wideband communication receivers, the large input-signal dynamics is a fundamental problem. Unintentional signal clipping occurs, if the receiver front-end with the analog-to-digital interface cannot respond to rapidly varying conditions. This paper discusses digital postprocessing compensation of such unintentional clipping in multiband OFDMA receivers. The proposed method iteratively mitigates the clipping distortion by exploiting the symbol decisions. The performance of the proposed method is illustrated with various computer simulations and also verified by concrete laboratory measurements with commercially available analog-to-digital hardware. It is shown that the clipping compensation algorithm implemented in a turbo decoding OFDM receiver is able to remove almost all the clipping distortion even under significant clipping in fading channel circumstances. That is to say, it is possible to nearly recover the receiver performance to the level, which would be achieved in the equivalent nonclipped situation.

Direct-conversion architecture-based orthogonal frequency division multiplexing (OFDM) systems are troubled by impairments such as in-phase and quadrature-phase (I/Q) imbalance and carrier frequency offset (CFO). These impairments are unavoidable in any practical implementation and severely degrade the obtainable link performance. In this contribution, we study the joint impact of frequency-selective I/Q imbalance at both transmitter and receiver together with channel distortions and CFO error. Two estimation and compensation structures based on different pilot patterns are proposed for coping with such impairments. The first structure is based on preamble pilot pattern while the second one assumes a sparse pilot pattern. The proposed estimation/compensation structures are able to separate the individual impairments, which are then compensated in the reverse order of their appearance at the receiver. We present time-domain estimation and compensation algorithms for receiver I/Q imbalance and CFO and propose low-complexity algorithms for the compensation of channel distortions and transmitter IQ imbalance. The performance of the compensation algorithms is investigated with computer simulations as well as with practical radio frequency (RF) measurements. The performance results indicate that the proposed techniques provide close to the ideal performance both in simulations and measurements.

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. The use of multiple antennas poses then big restrictions on the size and cost of individual radio transmitters and receivers, to keep the overall transceiver implementation feasible. This results in various imperfections in the analog radio front ends. One good example is the so-called I/Q imbalance problem related to the amplitude and phase matching of the transceiver I and Q chains. This paper studies the performance of space-time coded (STC) multiantenna OFDM systems under I/Q imbalance, covering both the transmitter and the receiver sides of the link. The challenging case of frequency-selective I/Q imbalances is assumed, being an essential ingredient in future wideband wireless systems. As a practical example, the Alamouti space-time coded OFDM system with two transmit and M receive antennas is examined in detail and a closed-form solution for the resulting signal-to-interference ratio (SIR) at the detector input due to I/Q imbalance is derived. This offers a valuable analytical tool for assessing the I/Q imbalance effects in any STC-OFDM system, without lengthy data or system simulations. In addition, the impact of I/Q imbalances on the channel estimation in the STC-OFDM context is also analyzed analytically. Furthermore, based on the derived signal models, a practical pilot-based I/Q imbalance compensation scheme is also proposed, being able to jointly mitigate the effects of frequency-selective I/Q imbalances as well as channel estimation errors. The performance of the compensator is analyzed using extensive computer simulations, and it is shown to virtually reach the perfectly matched reference system performance with low pilot overhead.