96 research outputs found

    Review of Recent Trends

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    This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

    High mobility in OFDM based wireless communication systems

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    Orthogonal Frequency Division Multiplexing (OFDM) has been adopted as the transmission scheme in most of the wireless systems we use on a daily basis. It brings with it several inherent advantages that make it an ideal waveform candidate in the physical layer. However, OFDM based wireless systems are severely affected in High Mobility scenarios. In this thesis, we investigate the effects of mobility on OFDM based wireless systems and develop novel techniques to estimate the channel and compensate its effects at the receiver. Compressed Sensing (CS) based channel estimation techniques like the Rake Matching Pursuit (RMP) and the Gradient Rake Matching Pursuit (GRMP) are developed to estimate the channel in a precise, robust and computationally efficient manner. In addition to this, a Cognitive Framework that can detect the mobility in the channel and configure an optimal estimation scheme is also developed and tested. The Cognitive Framework ensures a computationally optimal channel estimation scheme in all channel conditions. We also demonstrate that the proposed schemes can be adapted to other wireless standards easily. Accordingly, evaluation is done for three current broadcast, broadband and cellular standards. The results show the clear benefit of the proposed schemes in enabling high mobility in OFDM based wireless communication systems.Orthogonal Frequency Division Multiplexing (OFDM) wurde als Übertragungsschema in die meisten drahtlosen Systemen, die wir täglich verwenden, übernommen. Es bringt mehrere inhärente Vorteile mit sich, die es zu einem idealen Waveform-Kandidaten in der Bitübertragungsschicht (Physical Layer) machen. Allerdings sind OFDM-basierte drahtlose Systeme in Szenarien mit hoher Mobilität stark beeinträchtigt. In dieser Arbeit untersuchen wir die Auswirkungen der Mobilität auf OFDM-basierte drahtlose Systeme und entwickeln neuartige Techniken, um das Verhalten des Kanals abzuschätzen und seine Auswirkungen am Empfänger zu kompensieren. Auf Compressed Sensing (CS) basierende Kanalschätzverfahren wie das Rake Matching Pursuit (RMP) und das Gradient Rake Matching Pursuit (GRMP) werden entwickelt, um den Kanal präzise, robust und rechnerisch effizient abzuschätzen. Darüber hinaus wird ein Cognitive Framework entwickelt und getestet, das die Mobilität im Kanal erkennt und ein optimales Schätzungsschema konfiguriert. Das Cognitive Framework gewährleistet ein rechnerisch optimales Kanalschätzungsschema für alle möglichen Kanalbedingungen. Wir zeigen außerdem, dass die vorgeschlagenen Schemata auch leicht an andere Funkstandards angepasst werden können. Dementsprechend wird eine Evaluierung für drei aktuelle Rundfunk-, Breitband- und Mobilfunkstandards durchgeführt. Die Ergebnisse zeigen den klaren Vorteil der vorgeschlagenen Schemata bei der Ermöglichung hoher Mobilität in OFDM-basierten drahtlosen Kommunikationssystemen

    Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Advanced receiver structures for mobile MIMO multicarrier communication systems

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    Beyond third generation (3G) and fourth generation (4G) wireless communication systems are targeting far higher data rates, spectral efficiency and mobility requirements than existing 3G networks. By using multiple antennas at the transmitter and the receiver, multiple-input multiple-output (MIMO) technology allows improving both the spectral efficiency (bits/s/Hz), the coverage, and link reliability of the system. Multicarrier modulation such as orthogonal frequency division multiplexing (OFDM) is a powerful technique to handle impairments specific to the wireless radio channel. The combination of multicarrier modulation together with MIMO signaling provides a feasible physical layer technology for future beyond 3G and fourth generation communication systems. The theoretical benefits of MIMO and multicarrier modulation may not be fully achieved because the wireless transmission channels are time and frequency selective. Also, high data rates call for a large bandwidth and high carrier frequencies. As a result, an important Doppler spread is likely to be experienced, leading to variations of the channel over very short period of time. At the same time, transceiver front-end imperfections, mobility and rich scattering environments cause frequency synchronization errors. Unlike their single-carrier counterparts, multi-carrier transmissions are extremely sensitive to carrier frequency offsets (CFO). Therefore, reliable channel estimation and frequency synchronization are necessary to obtain the benefits of MIMO OFDM in mobile systems. These two topics are the main research problems in this thesis. An algorithm for the joint estimation and tracking of channel and CFO parameters in MIMO OFDM is developed in this thesis. A specific state-space model is introduced for MIMO OFDM systems impaired by multiple carrier frequency offsets under time-frequency selective fading. In MIMO systems, multiple frequency offsets are justified by mobility, rich scattering environment and large angle spread, as well as potentially separate radio frequency - intermediate frequency chains. An extended Kalman filter stage tracks channel and CFO parameters. Tracking takes place in time domain, which ensures reduced computational complexity, robustness to estimation errors as well as low estimation variance in comparison to frequency domain processing. The thesis also addresses the problem of blind carrier frequency synchronization in OFDM. Blind techniques exploit statistical or structural properties of the OFDM modulation. Two novel approaches are proposed for blind fine CFO estimation. The first one aims at restoring the orthogonality of the OFDM transmission by exploiting the properties of the received signal covariance matrix. The second approach is a subspace algorithm exploiting the correlation of the channel frequency response among the subcarriers. Both methods achieve reliable estimation of the CFO regardless of multipath fading. The subspace algorithm needs extremely small sample support, which is a key feature in the face of time-selective channels. Finally, the Cramér-Rao (CRB) bound is established for the problem in order to assess the large sample performance of the proposed algorithms.reviewe

    Doppler compensation algorithms for DSP-based implementation of OFDM underwater acoustic communication systems

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    In recent years, orthogonal frequency division multiplexing (OFDM) has gained considerable attention in the development of underwater communication (UWC) systems for civilian and military applications. However, the wideband nature of the communication links necessitate robust algorithms to combat the consequences of severe channel conditions such as frequency selectivity, ambient noise, severe multipath and Doppler Effect due to velocity change between the transmitter and receiver. This velocity perturbation comprises two scenarios; the first induces constant time scale expansion/compression or zero acceleration during the transmitted packet time, and the second is time varying Doppler-shift. The latter is an increasingly important area in autonomous underwater vehicle (AUV) applications. The aim of this thesis is to design a low complexity OFDM-based receiver structure for underwater communication that tackles the inherent Doppler effect and is applicable for developing real-time systems on a digital signal processor (DSP). The proposed structure presents a paradigm in modem design from previous generations of single carrier receivers employing computationally expensive equalizers. The thesis demonstrates the issues related to designing a practical OFDM system, such as channel coding and peak-to-average power ratio (PAPR). In channel coding, the proposed algorithms employ convolutional bit-interleaved coded modulation with iterative decoding (BICM-ID) to obtain a higher degree of protection against power fading caused by the channel. A novel receiver structure that combines an adaptive Doppler-shift correction and BICM-ID for multi-carrier systems is presented. In addition, the selective mapping (SLM) technique has been utilized for PAPR. Due to their time varying and frequency selective channel nature, the proposed systems are investigated via both laboratory simulations and experiments conducted in the North Sea off the UK’s North East coast. The results of the study show that the proposed systems outperform block-based Doppler-shift compensation and are capable of tracking the Doppler-shift at acceleration up to 1m /s2.EThOS - Electronic Theses Online ServiceIraqi Government's Ministry of Higher Education and Scientific ResearchGBUnited Kingdo

    Efficient implementation of channel estimation algorithm for beamforming

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    Abstract. The future 5G mobile network technology is expected to offer significantly better performance than its predecessors. Improved data rates in conjunction with low latency is believed to enable technological revolutions such as self-driving cars. To achieve faster data rates, MIMO systems can be utilized. These systems enable the use of spatial filtering technique known as beamforming. Beamforming that is based on the preacquired channel matrix is computationally very demanding causing challenges in achieving low latency. By acquiring the channel matrix as efficiently as possible, we can facilitate this challenge. In this thesis we examined the implementation of channel estimation algorithm for beamforming with a digital signal processor specialized in vector computation. We present implementations for different antenna configurations based on three different approaches. The results show that the best performance is achieved by applying the algorithm according to the limitations given by the system and the processor architecture. Although the exploitation of the parallel architecture was proved to be challenging, the implementation of the algorithm would have benefitted from the greater amount of parallelism. The current parallel resources will be a challenge especially in the future as the size of antenna configurations is expected to grow.Keilanmuodostuksen tarvitseman kanavaestimointialgoritmin tehokas toteutus. Tiivistelmä. Tulevan viidennen sukupolven mobiiliverkkoteknologian odotetaan tarjoavan merkittävästi edeltäjäänsä parempaa suorituskykyä. Tämän suorituskyvyn tarjoamat suuret datanopeudet yhdistettynä pieneen latenssiin uskotaan mahdollistavan esimerkiksi itsestään ajavat autot. Suurempien datanopeuksien saavuttamiseksi voidaan hyödyntää monitiekanavassa käytettävää MIMO-systeemiä, joka mahdollistaa keilanmuodostuksena tunnetun spatiaalisen suodatusmenetelmän käytön. Etukäteen hankittuun kanavatilatietoon perustuva keilanmuodostus on laskennallisesti erittäin kallista. Tämä aiheuttaa haasteita verkon pienen latenssivaatimuksen saavuttamisessa. Tässä työssä tutkittiin keilanmuodostukselle tarkoitetun kanavaestimointialgoritmin tehokasta toteutusta hyödyntäen vektorilaskentaan erikoistunutta prosessoriarkkitehtuuria. Työssä esitellään kolmea eri lähestymistapaa hyödyntävät toteutukset eri kokoisille antennikonfiguraatioille. Tuloksista nähdään, että paras suorituskyky saavutetaan sovittamalla algoritmi järjestelmän ja arkkitehtuurin asettamien rajoitusten mukaisesti. Vaikka rinnakkaisarkkitehtuurin hyödyntäminen asetti omat haasteensa, olisi algoritmin toteutus hyötynyt suuremmasta rinnakkaisuuden määrästä. Nykyinen rinnakkaisuuden määrä tulee olemaan haaste erityisesti tulevaisuudessa, sillä antennikonfiguraatioiden koon odotetaan kasvavan

    On Efficient Signal Processing Algorithms for Signal Detection and PAPR Reduction in OFDM Systems

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    The driving force of the study is susceptibility of LS algorithm to noise. As LS algorithm is simple to implement, hence it’s performance improvement can contribute a lot to the wireless technology that are especially deals with high computation. Cascading of AdaBoost algorithm with LS greatly influences the OFDM system performance. Performance of Adaptive Boosting based symbol recovery was investigated on the performance of LS, MMSE, BLUE were also compared with the performance of AdaBoost algorithm and MMSE has been found the higher computational complexity. Furthermore, MMSE also requires apriori channel statistics and computational complexity O(5N3) of the MMSE increases exponentially as the number of carrier increases. For the Adaboost case the computational complexity calculation is little different.Therefore, in the training stage of the AdaBoost algorithm, the computational complexity is only O(nT M) Furthermore, as it is a classification algorithm so in the receiver side we will require a separate de-mapper (or decoder) to get the desired data bits, i.e., a. SAS aided DCT based PAPR reduction 1326 and b. SAS aided DCT based PAPR reduction. A successive addition subtraction preprocessed DCT based PAPR reduction technique was proposed. Here, the performance of proposed method was compared with other preexisting techniques like SLM and PTS and the performance of the proposed method was seen to outperform specially in low PAPR region. In the proposed PAPR reduction method, the receiver is aware of the transmitted signal processing, this enables a reverse operation at the receiver to extract the transmit data. Hence the requirement of sending extra information through extra subcarrier is eliminated. The proposed method is also seen to be spectrally efficient. In the case of PTS and SLM it is inevitable to send the side information to retrieve the transmit signal. Hence, these two methods are spectrally inefficient. Successive addition subtraction based PAPR reduction method was also applied to MIMO systems. The performance of the SAS based PAPR reduction method also showed better performance as compared to other technique. An extensive simulation of MIMO OFDM PAPR reduction was carried out by varying the number of subcarriers and number of transmitter antennas. A detailed computational complexity analysis was also carried out. BATE aided SDMA multi user detection. A detailed study of SDMA system was carried out with it’s mathematical analysis.Many linear and non linear detectors like ML, MMSE, PIC, SIC have been proposed in literature for multiuser detection of SDMA system. However, except MMSE every receivers other are computational extensive. So as to enhance the performance of the MMSE MUD a meta heuristic Bat algorithm was incorporated in cascade with MMSE

    Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO

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    This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision. Codes will be open to all on https://gaozhen16.github.io/ soo

    Receiver design for nonlinearly distorted OFDM : signals applications in radio-over-fiber systems

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    Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201
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