BER Performance Analysis of MIMO Systems Using Equalization Techniques

The mobile data applications has increased the demand for wireless communication systems offering high throughput, wide coverage, and improved reliability. The main challenges in the design of wireless communication systems are the limited resources, such as constrained transmission power, scarce frequency bandwidth, and limited implementation complexity—and the impairments of the wireless channels, including noise, interference, and fading effects. Multiple-Input Multiple-Output (MIMO) communication has been shown to be one of the most promising emerging wireless technologies that can efficiently boost the data transmission rate, improve system coverage, and enhance link reliability. By employing multiple antennas at transmitter and receiver sides, MIMO techniques enable a new dimension – the spatial dimension – that can be utilized in different ways to combat the impairments of wireless channels. This article focuses on Equalization techniques, for Rayleigh Flat fading channel. Equalization is a well known technique for combating intersymbol interference; moreover equalization is the filtering approach which minimizes the error between actual output and desired output by continuous updating its filter coefficients. In this paper, different equalization techniques are investigated for the analysis of BER in MIMO Systems. In this article we have discussed different types of equalizer like ZF, MMSE, ZF-SIC, MMSE-SIC, ML and Sphere decoder. The results are decoded using the ZF, MMSE, ZF-SIC, MMSE-SIC, ML and Sphere decoder (SD) technique. The successive interference methods outperform the ZF and MMSE however their complexity is higher due to iterative nature of the algorithms. ML provides the better performance in comparison to others. Sphere decoder provides the best performance and the highest decoding complexity as compare to ML. We can clearly observe that Sphere decoder gives us high performance in comparison to ML, MMSE-SC, ZF-SIC, MMSE and ZF.   Keywords: Quadrature Amplitude Modulation (QAM), Quadrature Phase Shift Key (QPSK), Binary Phase Shift Key (BPSK), Minimum mean-squared error (MMSE), Maximum likelihood (ML),Bit error rate (BER), Independent identical distributed (i.i.d. ), Intersymbol interference (ISI). Successive interference cancellation (SIC), Sphere Decoder (SD), zero Forcing (ZF)

New Iterative Frequency-Domain Detectors for IA-Precoded MC-CDMA Systems

The aim of this paper is to design new multi-user receivers based on the iterative block decision feedback equalization concept for MC-CDMA systems with closed-form interference alignment (IA) at the transmitted side. IA is a promising technique that allows high capacity gains in interfering channels. On the other hand, iterative frequency-domain detection receivers based on the IB-DFE concept can efficiently exploit the inherent space-frequency diversity of the MIMO MC-CDMA systems. In IA-precoded based systems the spatial streams are usually separated by using a standard linear MMSE equalizer. However, for MC-CDMA based systems, linear equalization is not the most efficient way of separating spatial streams due to the residual inter-carrier interference (ICI). Therefore, we design new non-linear iterative receiver structures to efficiently remove the aligned interference and separate the spatial streams in presence of residual ICI. Two strategies are considered: in the first one the equalizer matrices are obtained by minimizing the mean square error (MSE) of each individual data stream at each subcarrier, while in the second approach the matrices are computed by minimizing the overall MSE of all data streams at each subcarrier. We also propose an accurate analytical approach for obtaining the performance of the proposed receivers. Our schemes achieve the maximum degrees of freedom provided by the IA precoding, while allowing close-to-optimum space-diversity gain, with performance approaching the matched filter bound

INTERFERENCE MANAGEMENT IN LTE SYSTEM AND BEYOUND

The key challenges to high throughput in cellular wireless communication system are interference, mobility and bandwidth limitation. Mobility has never been a problem until recently, bandwidth has been constantly improved upon through the evolutions in cellular wireless communication system but interference has been a constant limitation to any improvement that may have resulted from such evolution. The fundamental challenge to a system designer or a researcher is how to achieve high data rate in motion (high speed) in a cellular system that is intrinsically interference-limited. Multi-antenna is the solution to data on the move and the capacity of multi-antenna system has been demonstrated to increase proportionally with increase in the number of antennas at both transmitter and receiver for point-to-point communications and multi-user environment. However, the capacity gain in both uplink and downlink is limited in a multi-user environment like cellular system by interference, the number of antennas at the base station, complexity and space constraint particularly for a mobile terminal. This challenge in the downlink provided the motivation to investigate successive interference cancellation (SIC) as an interference management tool LTE system and beyond. The Simulation revealed that ordered successive interference (OSIC) out performs non-ordered successive interference cancellation (NSIC) and the additional complexity is justified based on the associated gain in BER performance of OSIC. The major drawback of OSIC is that it is not efficient in network environment employing power control or power allocation. Additional interference management techniques will be required to fully manage the interference.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Exploiting One-Dimensional Convolutional Neural Networks for Joint Channel Estimation and Signal Detection in Non-Orthogonal Multiple Access Systems

الوصول المتعدد غير المتعامد (NOMA) هو تقنية واعدة للجيل الخامس و الاجيال المستقبلية من شبكات الاتصالات اللاسلكية ، مما يزيد من كفاءة الطيف ويقلل من زمن الوصول. ومع ذلك، يمكن أن يتأثر أداء NOMA بإلغاء التداخل المتتالي غير المثالي (SIC). تم اقتراح تقنيات الذكاء الاصطناعي للمساعدة في الكشف عن الإشارات وتقدير القنوات في أنظمة NOMA. في هذه الدراسة ، نقترح نهجًا جديدًا باستخدام الشبكات العصبية التلافيفية أحادية البعد (1D CNN) لمعالجة قيود المحددة لأنظمة الذكاء الاصطناعي الحالية. على عكس طرق الذكاء الاصطناعي الأخرى التي تعتمد على تبعيات الوقت لتصنيف البيانات ، تستخدم 1D CNN طبقة التفاف أحادية البعد لاستخراج الميزات، مما يؤدي إلى موثوقية عالية. تظهر نتائج المحاكاة أن طريقتنا المقترحة تتفوق على تقنيات التعلم العميق الحالية من حيث معدل الخطأ في العينة (SER). علاوة على ذلك ، يؤدي تقليل معلمة البادئة الدورية (CP) إلى زيادة التداخل بين العينات (ISI) ، ولكن طريقتنا لا تزال تحقق تحسينًا بمقدار 6 ديسيبل على النهوج في (11،13) وتقنيات تقدير القنوات التقليدية مثل الاحتمال الأقصى (ML) عند إشارة منخفضة إلى- نسب الضوضاء (SNR).Non-Orthogonal Multiple Access (NOMA) is a promising technology for the fifth and future generations of wireless communication networks, which increases spectral efficiency and reduces latency. However, NOMA performance can be affected by imperfect successive interference cancellation (SIC). Deep learning techniques have been proposed to aid in signal detection and channel estimation in NOMA systems. In this study, we propose a new approach using one-dimensional convolutional neural networks (1D CNN) to address the limitations of current deep learning methods. Unlike other deep learning methods that rely on time dependencies for data classification, 1D CNN uses a 1-dimensional convolution layer for feature extraction, resulting in high reliability. Simulation results demonstrate that our proposed method outperforms existing deep learning techniques in terms of sample error rate (SER) by 7dB. Moreover, reducing the cyclic prefix (CP) parameter increases inter-sample interference (ISI), but our method still achieves a 6 dB improvement over approaches in [11,13] and traditional channel estimation techniques like maximum likelihood (ML) at low signal-to-noise ratios (SNR)

Novel Efficient Precoding Techniques for Multiuser MIMO Systems

In Multiuser MIMO (MU-MIMO) systems, precoding is essential to eliminate or minimize the multiuser interference (MUI). However, the design of a suitable precoding algorithm with good overall performance and low computational complexity at the same time is quite challenging, especially with the increase of system dimensions. In this thesis, we explore the art of novel low-complexity high-performance precoding algorithms with both linear and non-linear processing strategies. Block diagonalization (BD)-type based precoding techniques are well-known linear precoding strategies for MU-MIMO systems. By employing BD-type precoding algorithms at the transmit side, the MU-MIMO broadcast channel is decomposed into multiple independent parallel SU-MIMO channels and achieves the maximum diversity order at high data rates. The main computational complexity of BD-type precoding algorithms comes from two singular value decomposition (SVD) operations, which depend on the number of users and the dimensions of each user's channel matrix. In this thesis, two categories of low-complexity precoding algorithms are proposed to reduce the computational complexity and improve the performance of BD-type precoding algorithms. One is based on multiple LQ decompositions and lattice reductions. The other one is based on a channel inversion technique, QR decompositions, and lattice reductions to decouple the MU-MIMO channel into equivalent SU-MIMO channels. Both of the two proposed precoding algorithms can achieve a comparable sum-rate performance as BD-type precoding algorithms, substantial bit error rate (BER) performance gains, and a simplified receiver structure, while requiring a much lower complexity. Tomlinson-Harashima precoding (THP) is a prominent nonlinear processing technique employed at the transmit side and is a dual to the successive interference cancelation (SIC) detection at the receive side. Like SIC detection, the performance of THP strongly depends on the ordering of the precoded symbols. The optimal ordering algorithm, however, is impractical for MU-MIMO systems with multiple receive antennas. We propose a multi-branch THP (MB-THP) scheme and algorithms that employ multiple transmit processing and ordering strategies along with a selection scheme to mitigate interference in MU-MIMO systems. Two types of multi-branch THP (MB-THP) structures are proposed. The first one employs a decentralized strategy with diagonal weighted filters at the receivers of the users and the second uses a diagonal weighted filter at the transmitter. The MB-MMSE-THP algorithms are also derived based on an extended system model with the aid of an LQ decomposition, which is much simpler compared to the conventional MMSE-THP algorithms. Simulation results show that a better BER performance can be achieved by the proposed MB-MMSE-THP precoder with a small computational complexity increase