Satellite Anti-interception Communication System Based on WFRFT and MIMO
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This paper proposes a new kind of satellite anti-interception communication system based on weighted-type fractional Fourier transform (WFRFT) and Multiple Input Multiple Output (MIMO) technology to improve the safety and anti-interception of satellite communication system. This system adopts multiple-layer WFRFT to modulate the signal. The number of WFRFT layers and the transmit antennas is the same. The modulation parameter of each WFRFT layer is different. After WFRFT modulation, the original signal has the characteristic of time and frequency domain, which can effectively resist the parameter scanning. MIMO can effectively improve the spectrum utilization and system capacity. By theoretical analysis, the WFRFT-MIMO communication system can be used to restore the original signal efficiently, and the eavesdropper can't intercept the signal. Taking two transmit and one receive antennas for example, the results of the contrast of bit error rate (BER) performance between legal receiver and eavesdropper and relationship between receiving performance and WFRFT order deviation are simulated respectively, when the modulation order error of the eavesdropper is 0.1, and the signal-to-noise ratio is 20dB, the BER of eavesdropper is 10 -3 , which is more than the legal receiver 4dB. The performance between different receiving performance and transmitting antennas groups is simulated, when the signal to noise ratio (SNR) is 10dB, the performance of the three transmit four receive antennas is higher than that of the two transmit one receive antennas, which increases the 4. 5dB.Keywords:
SIGNAL (programming language)
Modulation (music)
Communications satellite
MIMO (multiple-input-multiple-output) is a technique in which the signal is transmitted through the actual channels. In order to further understand MIMO communication, this paper has conducted MIMO channel decomposition in theory. First, we introduce the actual MIMO channel model and detection model. Then, we decompose the actual MIMO channel into virtual parallel multiple single-input-single-output channels based on eigenvalues. After decomposition, the physical meaning of a virtual channel and mathematical distribution model of the transmit signal are finally given.
SIGNAL (programming language)
Spatial multiplexing
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Multiple-input multiple-output (MIMO) communications systems have been seriously considered in recent years to achieve substantial gains in spectral, power, and energy efficiency compared to conventional single-input single-output (SISO) systems. Various MIMO configurations based on Single-User (SU)-MIMO, Multi-User (MU)-MIMO, network-MIMO architectures have been proposed for LTE systems. To accommodate the ever-increasing demands of different multimedia services and applications, the evolving next-generation wireless access networks are envisioned to support 1,000-fold gains in capacity, connections for billions of diverse devices, and Gb/s individual user data rates. Massive MIMO has emerged as a promising technology to deal with such challenges. Through the use of an excessively large number of service antennas in a fully coherent and adaptive operation, transmission and reception of signal energy can be focused into small regions to offer large improvements in throughput and energy efficiency, especially when combined with multi-user scheduling and in mmWave bands. Furthermore, massive MIMO can enhance the robustness to interference and intentional jamming. The talk will provide an overview from MIMO, SU-MIMO, MU-MIMO, network-MIMO to massive MIMO developments applicable to next-generation wireless access communications, and highlight various technical issues and solutions.
Spatial multiplexing
Spectral Efficiency
Robustness
Cooperative MIMO
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Spatial multiplexing
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In this paper, we present the error vector magnitude (EVM) as a figure of merit for assessing the quality of digitally modulated telecommunication signals. We define EVM for a common industry standard and derive the relationships among EVM, signal to noise ratio (SNR) and bit error rate (BER). We also compare among the different performance metrics and show that EVM can be equivalently useful as signal to noise ratio and bit error rate. A few simulation results are presented to illustrate the performance of EVM based on these relationships.
Word error rate
Figure of Merit
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전송 데이터 용량의 요구치가 급속히 증가하면서 공간 스트림마다 독립된 정보를 전송할 수 있는 spatial multiplexing (SM) 기반 multi-input multi-output (MIMO) 기술에 대한 관심이 증대되고 있다. 3GPP LTE-advanced, IEEE 802.11ac 등의 최근 표준들에서는 최대 8개까지의 공간 스트림을 지원하고 있으며, beyond 4G 시스템의 핵심 기술로 고려되고 있는 massive MIMO나 mm-wave 시스템에서는 수십~수백개 이상의 안테나 까지도 지원을 고려하고 있다. SM MIMO 시스템의 최적 복조 기법인 maximum likelihood (ML) 방식의 연산복잡도는 안테나수에 지수적으로 증가하므로, 안테나 수의 급속한 증가는 연산량의 급격한 증가를 유발하게 되어 낮은 복잡도로 구현 가능한 수신 기법들에 대한 필요성을 증대시키게 되었다. 본 논문에서는 이러한 SM MIMO 복조 기법들에 대한 연구 결과들을 설명한다. 또한, 기존의 복조 기법들과 달리, 지수적으로 복잡도의 증가가 필요하지 않는 간단한 선형 기법에 기반한 massive MIMO 시스템용 수신 기법에 대해서도 설명하고 향후의 시스템 디자인 시 고려할 사항들에 대해 간략히 정리한다. With the increasing demands on high data rate, there has been growing interests in multi-input multi-output (MIMO) technology based on spatial multiplexing (SM) since it can transmit independent information in each spatial stream. Recent standards such as 3GPP LTE-advanced and IEEE 802.11ac support up to eight spatial streams, and massive MIMO and mm-wave systems that are expected to be included in beyond 4G systems are considering employment of tens to hundreds of antennas. Since the complexity of the optimum maximum likelihood based detection method increases exponentially with the number of antennas, low-complexity SM MIMO detection becomes more critical as the number of antenna increases. In this paper, we first review the results on the detection schemes for SM MIMO systems. In addition, massive MIMO reception schemes based on simple linear filtering which does not require exponential increment of complexity will be explained, followed by brief description on receiver design for future high dimensional SM MIMO systems.
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Full dimension MIMO, or FD-MIMO, has attracted significant attention in the wireless industry and academia in the past few years as a candidate technology for the next generation evolution toward beyond fourth generation and 5G cellular systems. FD-MIMO utilizes a large number of antennas placed in a 2D antenna array panel for realizing spatially separated transmission links to a large number of mobile stations. The arrangement of these antennas on a 2D panel allows the extension of spatial separation to the elevation domain as well as the traditional azimuth domain. This article discusses features and performance benefits of FD-MIMO along with the ongoing standardization efforts in 3GPP to incorporate FD-MIMO features into the next evolution of LTE. Furthermore, a design of a 2D antenna array, which plays a key role in the implementation of FD-MIMO, is also discussed. Finally, in order to demonstrate performance benefits of FD-MIMO, system-level evaluation results are provided.
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Reconfigurable intelligent surface (RIS) is a promising technology that can reshape the electromagnetic environment in wireless networks, offering various possibilities for enhancing wireless channels. Motivated by this, we investigate the channel optimization for multiple-input multiple-output (MIMO) systems assisted by RIS. In this letter, an efficient RIS optimization method is proposed to enhance the effective rank of the MIMO channel for achievable rate improvement. Numerical results are presented to verify the effectiveness of RIS in improving MIMO channels. Additionally, we construct a $2\times 2$ RIS-assisted MIMO prototype to perform experimental measurements and validate the performance of our proposed algorithm. The results reveal a significant increase in effective rank and achievable rate for the RIS-assisted MIMO channel compared to the MIMO channel without RIS.
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본 논문에서는 ESPAR(Electronically Steerable Parasitic Array Radiator) 안테나를 이용한 $M{\times}M$ BS-MIMO(beam space multiple input multiple output) 시스템을 제안한다. 기존 MIMO 시스템은 전송 데이터 신호를 다수의 안테나에 맵핑시켜 전송하기 때문에 다수의 RF 체인이 필요한 문제점이 있다. 다수의 RF 체인은 하드웨어의 비용을 및 RF 회로의 전력 소모를 증가 시킨다. 또한, 휴대폰과 같이 공간적인 제약이 큰 모바일 장비에서 MIMO 시스템을 사용하기 어렵다. 이러한 문제를 해결하기 위해서 단일 RF 체인을 가지는 ESPAR 안테나를 사용하여 빔 공간에서 신호를 맵핑시키는 BS-MIMO 시스템이 제안되었다. 본 논문에서는 BS-MIMO 시스템 기법에 대해서 설명하고 이를 확장한 $M{\times}M$ BS-MIMO 전송 기법의 설계 및 성능을 분석한다. 컴퓨터 모의실험을 통한 성능 확인 결과 제안된 BS-MIMO 전송기법은 기존 MIMO 기법과 비교하여 거의 동일한 수신 BER 성능을 얻는다. 따라서 다수의 RF 체인을 가지는 기존 MIMO 시스템과 비교하여 BS-MIMO 시스템은 단일 RF 체인을 가지고 MIMO 전송이 가능하며, 이로 인해서 하드웨어 비용 및 RF 회로의 전력 소모를 획기적으로 줄일 것으로 예상된다. In this paper, we propose a $M{\times}M$ beam-space multiple input multiple output (BS-MIMO) system using electronically steerable parasitic array radiator (ESPAR) antenna. Conventional MIMO method required multiple RF chains because it map the transmission signals onto multiple antennas. So, conventional MIMO system has high cost for design and high energy consumption at RF circuit. Also, It is difficult to use MIMO system in battery based mobile terminals with limited physical area. In order to solve these problems, BS-MIMO technique which map the data signal onto bases in beam space domain was proposed using ESPAR antenna with single RF chain. This paper, we design and analyze the performance of extended $M{\times}M$ BS-MIMO technique. Simulation results show that the proposed BS-MIMO system has similar BER performance compare to conventional MIMO scheme. Therefore, BS-MIMO system with single RF chain will has a low RF power consumption and low cost for RF hardware design as compared with conventional MIMO technique with multiple RF chains.
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To achieve high capacity and high data rates is the main requirement for today’s generation. This paper studies about the performance and capacity comparison of MIMO and cooperative MIMO systems. The comparison of capacity between multiple- input- multiple- output (MIMO) and cooperative MIMO systems helps us to know that which system have better performance and better capacity. The simulation results shows that among SISO, SIMO, MISO and MIMO system the capacity of MIMO will be better but in between MIMO and cooperative MIMO, Cooperative MIMO system have high capacity than MIMO systems.
Cooperative MIMO
Spatial multiplexing
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Предложен подход глубокого обучения для совместной задачи обнаружения MIMO и декодирования канала. Обычные приемники MIMO применяют подход на основе существующей модели для обнаружения MIMO и декодирования канала линейным или итеративным образом. Однако из-за сложной модели сигнала MIMO оптимальное решение проблемы совместного обнаружения MIMO и декодирования канала (то есть декодирование с максимальной вероятностью переданных кодовых слов из принятых сигналов MIMO) невозможно с вычислительной точки зрения. В качестве практической меры все современные приемники MIMO на основе стандартных моделей используют неоптимальные методы декодирования MIMO с доступной вычислительной сложностью. В этой работе применяются последние достижения в области глубокого обучения для проектирования приемников MIMO. В частности, используем глубокие нейронные сети (DNN) с контролируемым обучением для решения проблемы совместного обнаружения MIMO и декодирования каналов. DNN можно обучить для обеспечения гораздо лучшей производительности декодирования, чем это делают обычные приемники MIMO. Моделирование показывает, что реализация DNN, состоящая из семи скрытых слоев, может превзойти традиционные линейные или итерационные приемники на основе используемых моделей. Это улучшение производительности указывает на новое направление для будущей конструкции приемников MIMO The article proposes a deep learning approach for the joint problem of MIMO detection and channel decoding. Conventional MIMO receivers use an existing model approach to detect MIMO and decode the channel in a linear or iterative manner. However, due to the complex model of the MIMO signal, an optimal solution to the problem of joint MIMO detection and channel decoding (i.e., maximum probability decoding of the transmitted codewords from the received MIMO signals) is computationally impossible. As a practical measure, all current standard model based MIMO receivers use sub-optimal MIMO decoding techniques with affordable computational complexity. This work applies the latest advances in deep learning to the design of MIMO receivers. In particular, we use deep neural networks (DNN) with supervised learning to solve the problem of joint MIMO detection and channel decoding. DNNs can be trained to provide much better decoding performance than conventional MIMO receivers. Simulations show that a DNN implementation consisting of seven hidden layers can outperform traditional linear or iterative receivers based on the models used. This performance improvement points to a new direction for future MIMO receiver design
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