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Artificial fish swarm based power allocation algorithm for MIMO-OFDM relay underwater acoustic communication

Abstract:

This study investigates the application of artificial fish swarm algorithm (AFSA) in the power allocation for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) relay underwater acoustic (UWA) communication systems. First, by using the singular value decomposition technique, the two-hop transmission links are converted into the virtual direct links in an single-input and single-output OFDM (SISO-OFDM) system. Then, a power allocation optimisation problem, together with the assignment of subcarriers and relay nodes, are formulated for the virtual SISO-OFDM system. Finally, the problem-solving algorithms are proposed in two parts. Computer simulation results show that the proposed AFSA scheme improves in both power consumptions and diversity gains compared with two existing schemes for UWA communication systems.

Hybrid spread spectrum orthogonal waveforms for MIMO radar

Abstract:

In multiple input multiple output (MIMO) radar systems, choosing a proper orthogonal waveform is a critical task. A new hybrid spread spectrum (HSS) technique is proposed to maintain orthogonality at the transmit and receive ends. The HSS technique is a combination of direct-sequence spreading and frequency hopping schemes. In the context of MIMO radar, the transmitted signals are first spread using Hadamard-Walsh orthogonal codes and in every pulse repetition period, each signal hops to a different center frequency. The transmitted HSS signals are orthogonal in frequency and code domains. Simulation results show that the proposed HSS technique can achieve sharper auto ambiguity response and lower sidelobe cross ambiguity response with a gain of over 10 dB and better probability of detection in comparison with frequency orthogonality technique. The proposed HSS technique has the potential to resolve closely spaced targets and provide better immunity against narrowband interferences.

Hybrid beamforming for interference mitigation in MIMO radar

Abstract:

Hybrid beamforming for multiple input multiple output (MIMO) radar systems in a jamming environment is investigated. A new hybrid beamforming (HB) technique is proposed to reduce the dimensionality of the covariance matrix and to have a better jamming and interference mitigation capability. HB consists of two stages. The first stage decodes, phase shifts the received signals and adds signals decoded by the same code from different antenna elements. The second stage exploits digital beamforming techniques such as Minimum Variance Distortionless Response (MVDR) or convex optimization beamforming to determine the complex weights using N × N covariance matrix where N is the number of transmitting antennas. Simulation results show that the proposed HB technique can achieve better interference and jamming suppression results in comparison with other radar configurations. In addition, the HB technique has the potential to reduce the complexity of MIMO radar signal processing such as space-time adaptive processing.

Suppression Approach to Main-Beam Deceptive Jamming in FDA-MIMO Radar Using Nonhomogeneous Sample Detection

Abstract:

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit–receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit–receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach.

Computationally effective spectral MUSIC algorithm for monostatic MIMO radar using real polynomial rooting

Abstract:

A computationally effective real-valued variant of multiple signal classification (MUSIC) algorithm for monostatic multiple-input multiple-output (MIMO) radar is presented. Reduced-dimension transformation is utilized to reduce the dimension of the received data matrix at first, and then the unitary transformation is employed to transform the complex covariance matrix of the received data into a real-valued one. To further reduce the computational complexity, a real polynomial rooting technique is presented to determine the local maxima of the MUSIC spectrum that corresponding to the DOAs of the targets instead of the computationally-expensive spectrum search. Simulations results demonstrate that the presented algorithm can greatly reduce the computational complexity without sacrificing the estimation accuracy.

Efficient polar coded spatial multiplexing

Abstract:

This paper explores the design of efficient capacity achieving polar codes for multiple input multiple output (MIMO) channels and schemes for polar coded spatial multiplexing (PCSM). For polar code construction, the singular value decomposition (SVD) of a given MIMO channel into multiple independent single input single output (SISO) channels is considered as the first step of natural polarization. Firstly, we propose a basic PCSM scheme by constructing a polar code for each independent SISO channel. Then, we extend the scheme by using compound polar codes to construct a unified PCSM scheme for MIMO channels. Further, we present a novel approach for constructing an optimal compound polar codes which minimize block error rate (BLER) for a given NR× NTMIMO channel. Simulation results reveal that the extended schemes achieve at least 1.5 dB gain in terms of BLER with lesser computational complexity as compared to the basic scheme.

A Joint Rate and Buffer Control Scheme for Video Transmission over LTE Wireless Networks

Abstract:

In wireless communication systems, the quality of time-varying wireless channels and limited resources, make video transmission very challenging. In order to play video frequently, this paper proposes a novel method of QoE (Quality of Experience)-aware video transmission optimization algorithm over LTE networks by jointly controlling the transmission rate and playback buffer management to reduce the probability of video playback interruptions and adapt to constantly changing network status effectively. In order to calculate QoE more accurately and meet user’s requirements, this paper also proposes an improved QoE calculation model based on ITU-TP.1201, which considers video bitrate, playback interruption duration, number of playback interruptions, buffer overflow duration, and number of buffer overflows. The experimental results demonstrate that the proposed method can reduce the probability of video playback interruptions and video frame skipping under the finite resource constraints and varying network status more effectively compared with an existing algorithm, thus improving the QoE of video streaming.

Performance analysis of PDSCH downlink & inter-cell interferece parameters in LTE network

Abstract:

The expanding interest for mobile information activity brings new difficulties on cell networks as far as expanded information throughput. With this advances in the cell systems, which presents Long Term Evolution (LTE), the rearranged design utilizes a less number of nodes in the client plane. LTE Downlink transmission is dissected by LTE transceiver. Simulation is introduced in Physical Downlink Shared Channel (PDSCH). Estimations of Simulation result throughput for various numbers of edges and SNR values are calculated. Compelling use of dense spectrum reuse may increment Inter cell interference, which thus extremely restricts limit of clients in framework. It can confine general framework execution as far as throughput, particularly for the clients situated at the cell edge territory. Consequently, cautious administration of inter cell interference noticeably pivotal to enhance LTE performance.

Estimation of transmit-antenna number with different space–frequency transmission schemes for MIMO-OFDM systems

Abstract:

A hypothesis testing based algorithm is proposed to estimate the transmit-antenna number with different space-frequency transmission schemes for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The ranks of received sample covariance matrices composed by every two adjacent subcarriers can be estimated by utilising the statistical property of the largest noise eigenvalue. With a series of the estimated values of the ranks, the number of transmit antennas is determined by a decision mechanism, which is designed based on the frequencies of the rank values. Simulations demonstrate that the proposed algorithm performs well without the prior knowledge of the space-frequency transmission scheme.

Large random matrix-based channel estimation for massive MIMO-OFDM uplink

Abstract:

This study investigates the benefits offered by random matrix theory (RMT) towards the design of reliable channel estimation algorithms for a multi-user massive multiple-input multiple-out (MIMO)-orthogonal frequency-division multiplexing uplink. Assuming no a priori knowledge of channel statistics (KCS) at the massive base station, the authors propose RMT-aided minimum mean square estimation (MMSE) and RMT-aided sparse Bayesian learning (SBL) approaches for massive channel estimation. These approaches render efficient channel estimates, as illustrated through mean square error (MSE) performance, extracted via Monte-Carlo simulations. The results also show that with increasing antennas at the base station, MSE from the RMT-aided MMSE approach decreases, suggesting its aptness to massive MIMO systems. To further enhance the MSE performance, the MMSE and SBL estimated channel impulse responses are pruned using threshold computed from RMT analysis. The authors characterise MSE degradation due to the randomness in the threshold, with the help of the Marcenko-Pastur law-based non-asymptotic framework and concentration inequalities. Analysis results show that, for channels with approximate sparse common support, this MSE degradation is quite insignificant. Altogether, the study demonstrates that RMT analysis is competent in improving channel estimation at a massive MIMO system, when a priori KCS is completely unavailable.

Time domain cyclic selective mapping for PAPR reduction in MIMO-OFDM systems

Abstract:

Peak-to-average power ratio (PAPR) is one of the main impairments in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Large PAPR causes inefficiency in the power amplifier (PA) so that the energy consumption of the devices increases. Selective mapping (SLM) has been commonly used as the favorable PAPR reduction technique. Conventional SLM technique has relatively high complexity due to the use of some inverse discrete Fourier transform (IDFT) operations. In addition, it requires to transmit side information (SI) to the receiver. In this paper, we examine the performance of the low complexity time domain cyclic SLM (TD-C-SLM) in MIMO-OFDM systems. TD-C-SLM generates the signal candidates by summing the original OFDM signal and its cyclically shifted version. The signal candidate with the lowest PAPR will be transmitted. This technique requires no SI transmission. Simulation results show that up to 2 dB PAPR reduction can be achieved without increasing the out-of-band (OOB) spectrum by using the TD-C-SLM.

Performance evaluations of software-defined acoustic MIMO-OFDM transmission

Abstract:

In recent years, the system using acoustic communication is increasing. However, because acoustic communication uses low frequency, transmission rate is lower than radio wave communication. In wireless communication, MIMO-OFDM is proposed for improvement quality and transmission rate. In this paper, we introduce a software-defined acoustic communication platform by using MATLAB and implement acoustic MIMO-OFDM transmission into the platform. Also, we evaluate BER characteristics in various experimental parameters in MATLAB simulation and real environment. Moreover, we evaluate image quality in actual acoustic image transmission by using the acoustic communication platform and we can successfully transmit the image via acoustic MIMO-OFDM.

Channel Estimation in MIMO – OFDM Systems based on a new adaptive greedy algorithm

Abstract:

Channel estimation methods based on Compressed Sensing (CS) can be used to obtain channel state information of MIMO-OFDM system effectively. This paper proposes a new adaptive matching pursuit (NAMP) algorithm and the evaluation prototype based on LTE-Advanced wireless channel model. First, NAMP does not need the priori-knowledge of the sparsity level. Second, the fixed step size is determined in order to improve the efficiency of signal reconstruction. Third, a Singular Entropy order determination mechanism is employed to prevent the less relevant atoms from being introduced. Finally, Simulation results are discussed in detail, which demonstrate that the proposed method expenses smaller computational complexity, especially achieves more stable performance.

Structured compressed sensing-based time-frequency joint channel estimation for MIMO-OFDM systems

Abstract:

This paper proposes a time-frequency joint channel estimation method based on structured compression sensing (SCS) for multi-input and multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, which is different from traditional channel estimation scheme. In the proposed method, the received time-domain training sequences (TSs) without interference cancellation are exploited to obtain the coarse MIMO channel estimation of the path delays. By utilizing structured compression sensing method, furthermore a priori information-assisted adaptive structured subspace pursuit (PA-ASSP) algorithm which adopts a small amount of frequency domain orthogonal pilots is proposed to reconstruct the channel impulse response (CIR) of the MIMO channel so that the accurate channel gains is obtained. The simulation results show that the proposed scheme can more accurately estimate the channel with fewer pilots, and its performance is closer to the least squares (LS) algorithm.

Compressive sensing based channel estimation for MIMO-OFDM systems

Abstract:

In this paper, an adaptive structured-generalized orthogonal matching pursuit (AS-gOMP) algorithm is proposed for time domain channel estimation by utilizing the characteristics of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. This algorithm uses the properties of the PN sequence to obtain partial channel prior information firstly, then the remaining support sets are obtained by the improved generalized orthogonal matching pursuit (gOMP) algorithm in MIMO system. A good channel estimation result is achieved by exploiting the characteristics of PN sequence and the common sparsity in spatial and time domain. The simulation results show that the proposed method can reduce the bit error rate (BER) of channel estimation and improve the performance of the MIMO-OFDM system.

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