MIMO Wireless Communication

An original algorithm is proposed for discrete Fourier transform of incomplete data, which differs from the usual fast Fourier transform in having high frequency resolution, side-lobe suppression, and scope for use for processing uniformly and non-uniformly sampled signals. An inverse fast Fourier transform provides for extrapolating and interpolating incomplete data as band-limited signals.

This study presents a multiuser (MU) multiple-input multiple-output (MIMO) downlink transmission scheme based on the quadrature spatial modulation (QSM) concept, which uses the indices of the non-zero entries in its transmission vector to modulate and transmit an independent sequence of bits for each user in the system. The MU interference is removed by using a matrix precoding technique based on channel state information (CSI) known as block diagonalisation (BD). The performance of the proposed scheme is compared with the conventional MU-MIMO-BD system for uncorrelated Rayleigh fading channels and correlated fading channels with imperfect CSI in the reception. Additionally, a low-complexity near maximum likelihood (ML) detection algorithm for the MU-MIMO-QSM signal's detection is proposed. For the considered cases, the proposed MU-MIMO-QSM scheme exhibits gains up to 1 dB in bit error rate performance and a reduction in detection complexity up to 93% as compared to the conventional MU-MIMO-BD scheme for the optimal ML detection. The proposed algorithm performs very near to the optimal ML detector whilst achieving a complexity reduction of up to 58%.

The main aim of massive multiuser multiple-input multiple-output (MU-MIMO) system is to improve the throughput and spectral efficiency in 5G wireless networks. The performance of MU-MIMO system is severely influenced by inter-antenna interference (IAI) and multiuser interference (MUI). The IAI occurs due to space limitations at each user terminal (UT) and the MUI is added when one UT is in the vicinity of another UT in the same cellular network. IAI can be mitigated through a precoding scheme such as singular value decomposition (SVD), and MUI is suppressed by an efficient multiuser detection (MUD) schemes. The maximum likelihood (ML) detector has optimal performance; however, it has a highly complex structure and involves the need of a large number of computations especially in massive structures. Thus, the neighborhood search-based algorithm such as likelihood ascent search (LAS) has been found to be a better alternative for mitigation of MUI as it results in near optimal performance with low complexity. Most of the recent papers are aimed at eliminating either MUI or IAI, whereas the proposed work presents joint SVD precoding and LAS MUD to mitigate both IAI and MUI. The proposed scheme can achieve a near-optimal performance with smaller number of matrix computations.

Spatial modulation (SM) presents a pragmatic approach for transmitting information, where the modulator uses well known amplitude/phase modulation (APM) techniques such as phase shift keying (PSK) and quadrature amplitude modulation (QAM), but also employs the antenna index to convey additional information. Nevertheless, the spectral efficiency of SM can be further improved. On this note, to further enhance the spectral efficiency of SM, quadrature SM (QSM) modulation was proposed. In coded channels, therefore, typically soft-output detection coupled with soft-input channel decoding yields significant signal-to-noise ratio (SNR) gain. Hence, in this paper, soft-output maximum-likelihood detector (SOMLD) for QSM modulated system is proposed. Monte Carlo simulation results demonstrate that there is a close agreement between the error performances of the proposed SOMLD scheme and those of its hard-decision maximum-likelihood detector counterpart in uncoded channels, while significant signal-to-noise ratio (SNR) gains are yielded in coded channels.

Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems. We show that new analog computing approaches, such as Coherent Ising Machines (CIMs), are promising candidates for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the naive approach and evolve it into a regularized, Ising-based tree search algorithm that achieves near-optimal performance. By means of numerical simulation using the Rayleigh fading channel model, we show that in principle, a MIMO detector based on a high-speed Ising machine (such as a CIM implementation optimized for latency) would allow a higher transmitter antennas (users)-to-receiver antennas ratio and thus increase the overall throughput of the cell by a factor of two or more for massive MIMO systems. Our methods create an opportunity to operate wireless systems using more aggressive modulation and coding schemes and hence achieve high spectral efficiency: for a 16 × 16 MIMO system, we estimate around 2.5× more throughput in the mid-SNR regime (≈ 12 dB) and 2× more throughput in the high-SNR regime (>20 dB) as compared to the industry standard, a Minimum-Mean Square Error (MMSE) linear decoder.

Receive antenna shift keying (RASK) is a multiple-input multiple-output transmission scheme that applies the spatial modulation concept at the receiver. Zero-forcing (ZF) precoding can be used at the transmitter side to steer the signal toward the targeted antenna. Different detection schemes can be implemented in order to detect the RASK information. Moreover, for the sake of low complexity and low power consumption, decreasing the number of RF chains by the use of switched antennas can also be implemented. In this paper, we lead a theoretical analysis of the transmission of spatial symbols in RASK, using the ZF precoding. Two detection schemes relying on maximum likelihood (ML) detection are presented and proved to be implemented as a simple single-tap detector. One, called coherent ML (CML), needs one RF chain per receive antenna and the other one, named incoherent ML (IML), is based on an envelope detector and needs only one RF chain for the entire antenna array. Theoretical expressions of the bit error rate (BER) are derived for each scheme and validated through simulation. The CML detector is demonstrated to obviously outperform the IML detector in terms of the BER but at the cost of higher complexity and power consumption. We then proposed to use switches to reduce the number of RF chains, thus trading off some performance against complexity. At some point, IML is shown to outperform CML, depending on the number of switches, RF chains, and system configuration. This turning point is analytically calculated and validated through simulations, and so can be used for further system comparison studies. INDEX TERMS Spatial modulation (SM), receive antenna shift keying (RASK), beamforming, multiple input multiple output (MIMO).

This work presents a low-complexity pilot contamination attack (PCA) detector for a multi-cell multiuser massive multiple-input, multiple-output (MU-MaMIMO) system. An active eavesdropper (Eve) in the reference cell transmits synchronized training sequence of the legitimate user (LU) under attack in order to alter precoder to steel information in the downlink data phase. The detection of an active Eve is vital for the information security and enhances secrecy capacity of the LU. We formulate binary hypotheses for active Eve detection from the contaminated channel estimation. We provide performance analysis of the proposed detector. The comparison of analysis and Monte Carlo runs verifies the accuracy of the analysis. The simulation results demonstrate that the performance of the proposed PCA detector is better than the well-known minimum descriptive length (MDL) method in low SNR regime.

This paper presents low-complexity detectors for space-time block coded spatial modulation with cyclic structured (STBC-CSM) codeword over a quasi-static frequency-flat Rayleigh fading channel. Two low-complexity detectors are proposed, which reduce the computational complexity of the STBC-CSM maximum-likelihood (ML) detector and demonstrate near-ML error performance of STBC-CSM. Furthermore, Monte-Carlo simulation results for the proposed schemes are presented to validate the theoretical Average Bit Error Probability (ABEP) of STBC-CSM. The proposed low-complexity detectors can achieve a 99% reduction in computational complexity, when high order M-ary (M > 64) amplitude and/or phase modulation symbols are employed for STBC-CSM.

In this paper, we propose an algorithm based on the Alternating Minimization technique to solve the uplink massive MIMO detection problem. The proposed algorithm provides a lower complexity compared to the conventional MMSE detection technique, especially when the number of user equipment (UE) antennas is close to the number of base station (BS) antennas. This improvement is obtained without any matrix inversion. Moreover, the algorithm re-formulates the maximum likelihood (ML) detection problem as a sum of convex functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Alternating Minimization is used to solve the new formulated problem in an iterative manner with a closed form solution update in every iteration. Simulation results demonstrate the efficacy of the proposed algorithm in the uplink massive MIMO setting for both coded and uncoded cases

In this paper, we propose an algorithm based on the Alternating Minimization technique to solve the uplink massive MIMO detection problem. The proposed algorithm is specifically designed to avoid any matrix inversion and any computations of the Gram matrix at the receiver. The algorithm provides a lower complexity compared to the conventional MMSE detection technique, especially when the total number of user equipment (UE) antennas (across all users) is close to the number of base station (BS) antennas. The idea is that the algorithm reformulates the maximum likelihood (ML) detection problem as a sum of convex functions based on decomposing the received vector into multiple vectors. Each vector represents the contribution of one of the transmitted symbols in the received vector. Alternating Minimization is used to solve the new formulated problem in an iterative manner with a closed form solution update in every iteration. Simulation results demonstrate the efficacy of the proposed algorithm in the uplink massive MIMO setting for both coded and uncoded cases.

This paper presents an extended quadrature spatial modulation (EQSM) scheme that combines K quadrature spatial modulation (QSM) constellations to achieve a K-fold spectral efficiency (SE) enhancement. The idea underlying EQSM consists in using the average powers of the K QSM constellations as an additional dimension for the transmission of information bits. The modulation process starts with the design of a conventional QSM constellation. The remaining (K −1) QSM constellations are then configured with scaled amplitudes considering the same group of transmit antennas. The EQSM constellation is obtained by the superposition of the K scaled QSM constellations. A novel near maximum likelihood (ML) low-complexity algorithm for EQSM signal detection is also proposed in this paper. Analytical and simulation results obtained for independent and identically distributed (i.i.d.) Rayleigh fading channels show that the proposed EQSM scheme provides gains of up to 2.5, 5, 7, and 8 dB in bit error rate (BER) performance as compared to spatial multiplexing (SMux), double spatial modulation (DSM), conventional QSM, and spatial modulation (SM), respectively. In terms of detection complexity, EQSM allows a 67% complexity reduction when compared to SMux, and a 20% increase as compared to SM/QSM schemes. The obtained results also show that the proposed near ML detection algorithm reduces the complexity of the ML criterion by 82%, while achieving near optimal ML performance.

Wireless communication technology is now popular worldwide and has made mobile communication very attractive and in high demand. For higher data rates to meet the ever increasing demands in mobile communications, multiple-input multiple-output (MIMO) technology has been standardized as the 4G communication systems. MIMO, a wireless communication system which utilises multiple antennas at both sides of the transmission link, has received high attention in the last few decades due to its ability to achieve high data rate transmission. Several papers have presented different techniques in the implementation of MIMO wireless systems which include diversity methods. Physical diversity is one of these methods that is well known and can be used to combat multipath fading, a common problem in wireless communications. This paper summarizes physical diversity as well as virtual diversity techniques. The paper shows that unlike the former, virtual diversity methods such as co-operative diversi...

One of the main limitations in Spatial Modulation (SM) systems is the lack of transmit diversity, which directly impacts its error rate performance. The lack of the transmit diversity refers to activating only a single transmit antenna in SM systems. In this paper, we propose a novel scheme that aims at improving the performance of SM systems by achieving half-full transmit diversity. The proposed scheme, called Half-Full Transmit-Diversity SM (HFTD-SM), divides the transmit antennas into two-antenna groups. From each group, only a single antenna is activated, and all active transmit antennas emits one modulated symbol. The proposed HFTD-SM scheme is shown to outperform the conventional SM performance in terms of spectral efficiency, error rate, and design flexibility, while maintaining the main property of SM representing by the need of only a single RF chain. Simulation results corroborate the superior performance of the proposed scheme as compared to other SM variants in the literature.

Spatial multiplexing (SM) multi-input multi-output (MIMO) systems can transmit independent data streams using multiple antennas to achieve high data rates. SM techniques can be employed in single-user multi-input multi-output (SU-MIMO) and multi-user multi-input multioutput (MU-MIMO) systems. There are two types of interference in SM MIMO systems, interantenna interference and multi-user interference (MUI). In order to improve SM MIMO system performance, channel precoding is used at the base station (BS). In this report, block diagonalization (BD) channel precoding is employed to reduce the MUI and decompose the MU-MIMO system into parallel SU-MIMO systems. Then, several detection techniques are used to detect the received signal and reduce the inter-antenna interference at each user. Simulation results are presented to evaluate and compare the bit error rate (BER) performance of SU-MIMO and MU-MIMO systems with zero-forcing (ZF), minimum mean square error (MMSE), ordered successive...

This paper presents low-complexity detectors for space-time block coded spatial modulation with cyclic structured (STBC-CSM) codeword over a quasi-static frequency-flat Rayleigh fading channel. Two low-complexity detectors are proposed, which reduce the computational complexity of the STBC-CSM maximum-likelihood (ML) detector and demonstrate near-ML error performance of STBC-CSM. Furthermore, Monte-Carlo simulation results for the proposed schemes are presented to validate the theoretical Average Bit Error Probability (ABEP) of STBC-CSM. The proposed low-complexity detectors can achieve a 99% reduction in computational complexity, when high order M-ary (M > 64) amplitude and/or phase modulation symbols are employed for STBC-CSM.

Spatial Modulation (SM) is a technique that can enhance the capacity of MIMO schemes by exploiting the index of transmit antenna to convey information bits. This paper describes a MIMO transmission scheme that combines SM and spatial multiplexing. In the basic form of SM, only one out of N T available antennas is selected for transmission in any given symbol interval. This paper proposes to use more than one active antenna to transmit several symbols simultaneously. This would increase the spectral efficiency and decreases BER (bit error rate) at the receiver, an MMSE detector is employed to jointly estimate the Transmitted symbols as well as the index of the active transmit antennas. The results show that the detection based on zero forcing is good for noise free channel and is successful in removing ISI, but MMSE is a better choice than ZF in terms of BER characteristics and under Noise performance. In MIMO transmission scheme the uncorrelated fading channel also called statistical model is used for the effect of a propagation environment on a radio signal. Sometimes use of multiple antenna techniques is problem depended but it used because it gives better performance like heavily built urban environment where radio signals are fade and scattered.

Natural signals, such as seismic, biological, and electromagnetic signals, can be characterized by their frequency content. Usually, the frequency content of the signal provides us with the important information that we are interested in extracting from the signal. The frequency content of a signal is indicated by its spectrum. For example, in the case of biological signals, frequency analysis is often used for diagnostic purposes (e.g., to diagnose an abnormality in a physical system, such as illness in a person). In seismic signals, frequency analysis may serve as an aid in exploring the structure of the earth and in searching for oil. In the case of electromagnetic signals, such as radar, frequency analysis provides important information on the speed of an airplane. There are other applications in which the spectrum is more informative than the time waveform. If one has data with certain periodicities contained in the data, it is often easier to find these periodic components using spectral analysis rather than using time domain methods. If we want to use a digital computer to compute signal spectral information which is frequently the case, we can use only a finite number of signal samples. Also, we can compute only a finite number of samples of the continuous-frequency spectrum that correspond to either a continuous-or discrete-time signal. One of the principal reasons for investigating the Fourier transforms of a discretetime signal is to use the result to approximate the Fourier transform of a continuous-time signal. First, we see the Fourier Representations and then we will see the method to find the Fourier transform of a discrete-time signal. There are four distinct Fourier Representations, each applicable to a different class of signals. These four classes are defined by the periodicity properties of a signal and whether it is continuous-time or discrete-time.

In the conventional space-time coding technique [1], n T radio frequency (RF) chains are employed to transmit signals simultaneously from n T transmit antennas. In this Letter, a lowcomplexity transmit diversity scheme with n T = 2 transmit antennas is proposed. The proposed system employs only one RF chain as well as a low-complexity switch for transmission.

This paper presents a novel scheme to transmit signals in a Multiuser Multiple-Input Multiple-Output (MU-MIMO) downlink scenario. The proposed scheme combines Block Diagonalization (BD) and Generalized Spatial Modulation (SM) Techniques. The SM technique is used to add a broadcast transmission to the conventional MU-MIMO BD scheme, which results in a novel BDSM proposal. We compare the proposed BDSM scheme with the conventional BD scheme considering the Bit Error Rate (BER) performance , detection complexity, and spectral efficiency using Maximum Likelihood (ML) joint-detection and ML segmented-detection algorithms. A correlated fading channel with Rayleigh distribution is considered. Simulations results show that the broadcast transmission can be added to the BD scheme without performance impairment. The proposed BDSM system has performance gains of 4 dB for error rates of 10 À3 approximately when some bits are considered as broadcast transmission in the BD scheme. In general , the proposed detection algorithms have a tradeoff between the BER performance and the detector's complexity.

In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into an equivalent-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle.

The DFT is the most important discrete transform, used to perform Fourier analysis in many practical applications. In digital signal processing, the function is any quantity or signal that varies over time, such as the pressure of a sound wave, a radio signal, or daily temperature readings, sampled over a finite time interval (often defined by a window function). In image processing, the samples can be the values of pixels along a row or column of a raster image. The DFT is also used to efficiently solve partial differential equations, and to perform other operations such as convolutions or multiplying large integers.
A fast Fourier transform (FFT) algorithm computes the discrete Fourier transform (DFT) of a sequence, or its inverse. Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. An FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. As a result, it manages to reduce the complexity of computing the DFT from {\displaystyle O(n^{2})} O(n^{2}), which arises if one simply applies the definition of DFT, to {\displaystyle O(n\log n)} O(n\log n), where {\displaystyle n} n is the data size.

Fast Fourier transforms are widely used for many applications in engineering, science, and mathematics. The basic ideas were popularized in 1965, but some algorithms had been derived as early as 1805. In 1994, Gilbert Strang described the FFT as "the most important numerical algorithm of our lifetime and it was included in Top 10 Algorithms of 20th Century by the IEEE journal Computing in Science & Engineering.

A fast Fourier transform (FFT) algorithm computes the discrete Fourier transform (DFT) of a sequence, or its inverse. Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. An FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. IFFT is a fast algorithm to perform inverse (or backward) Fourier transform (IDFT), which undoes the process of DFT. IDFT of a sequence { } that can be defined as: ALGORITHM/PROCEDURE:

The operational challenges of the contemporary data center network (DCN) has given rise to the need to change its IT service delivery to a modern data center network model that is more efficient, service-oriented, and responsive to business needs. A new dynamic infrastructure for such a modern data center network was modeled, which allows organizations to be better positioned to adopt integrated new technologies, such as consolidation, virtualization and cloud computing. This will help them to deliver dynamic access to IT services and resources thereby enabling IT departments to spend less time fixing IT problems and more time solving real business challenges. Open Shortest Path First (OSPF) as a link state protocol in addressing network convergence and cost in the proposed data center network was evaluated in this work. OPNET simulator was used for modeling and parameter characterization.