Performance analysis for low-complexity detection of MIMO V2V communication systems
Introduction
The rapid development of traffic network transport brings great pressure to traffic safety, transport efficiency and sustainable development of a city. At the same time of causing huge economic loss, it also induces traffic accidents, increased environmental pollution and other traffic problems. The relevant traffic management division have taken various measures, such as odd-and-even license plate rule, license auction, raise parking fees, but the cost is high. As a fairly complex system, it is difficult to solve the problem by considering vehicles or road separately [1], [2], [3].
Intelligent Transportation System (ITS) is a comprehensive technology that integrates a variety of computer, sensing technology and communication technology into the management and control of a traffic system. It can effectively utilize the existing transportation facilities to make the traffic system more secure, efficient and reliable [4], [5], [6]. In essence, the vehicle networking is a huge wireless sensor network, in which each car can be considered as a super sensor node [7]. Wireless sensor networks are composed of a large number of randomly-configured sensor nodes and network coverage is one of the most basic problems [8]. The wireless communication of ITS mainly relies on two technologies: short-range wireless communication and long-distance mobile communication, which is mainly mobile communication technologies such as GPRS(General Packet Radio Service), 3G(3rd Generation communication system), 4G(4th Generation communication system), LTE(Long Term Evolution) [9]. With the rapid development of big data, cloud computing and wireless communication technologies, network support is provided for the specific service applications of ITS [10]. A lot of storage resources of cloud computing are utilized to protect important data [11]. As one crucial component of ITS, vehicle communication is the focus of research [12], [13].
Generally speaking, the communication between vehicles in ITS can be divided into V2V and Vehicle-to-Infrastructure (V2I). As shown in Fig. 1, V2V communication system utilizes vehicles equipped with transmit unit to transmit signals through high-speed wireless network, including vehicle’s speed, direction, geographical location, routes and so on. Other vehicles will receive the transmitted wireless signal in real time and feedback similar information, forming an information exchange process. By sharing real-time information among vehicles, the system can respond more timely to avoid danger and further enhance the traffic safety. The V2V system can prejudge dangers in case the driver is unaware of in various road conditions by communicating with the surrounding vehicles. For example, a vehicle in emergency braking will broadcast a corresponding signal to the surrounding vehicles so that they can take actions automatically to avoid accidents, which is more accurate and quicker than the driver’s judgment [14], [15], [16]. In theory, the accident rate can be reduced by more than 70%.
When the vehicle moves quickly, it will cause serious Doppler shift phenomenon. Especially in the actual urban traffic environment, high buildings, bridges, tunnels and green belts make the channel attenuate more serious together with the high-speed movement of vehicles. When two vehicles are moving rapidly towards each other, the polarity of the Doppler frequency shift will be reversed in a short time, resulting in channel deterioration. While one vehicle moves with high buildings and other reflective objects around it, different paths of the same transmitted signal will superpose at the receiver and form multi-path fading, which may seriously affect the signal strength and communication quality. In most vehicle scenarios, especially in the urban scenes, the channel of V2V system has a highly dynamic characteristic due to the multi-path fading.
Traditional wireless communication technology cannot solve the above problems easily. MIMO technology with more than one antenna at both the transmitter and receiver, can satisfy higher rate requirements and shorten the transmission delay of emergency messages, realizing real-time communication among vehicles. Furthermore, it can also exploit diversity gain to provide more reliable communication for ITS applications by using Space-Time Block Code (STBC) and other signal processing techniques such as V-BLAST (Vertical-Bell Labs layered Space-Time) coding and space-time coding [17], [18], [19]. Beamforming technology can spatially converge the transmitted signals and expand the communication range significantly, which is more useful in scenarios where the vehicle density is low. Therefore, MIMO technology can be well applied in V2V communication to improve the anti-fading and anti-jamming capability, enhancing the channel capacity and transmission reliability. As shown in Fig. 2, assume that the transmitter and receiver are equipped with more than one antennas respectively. The input signal is transmitted through transmit antennas after modulation, and then the receiver receives multi-channel signal superposition and resolves the original signal through effective detection and demodulation. Although MIMO technology has many advantages, the adopted detection method directly determines the system performance and also the computational complexity. Therefore, many researchers have carried out research on signal detection and put forward relevant algorithms. IF detection is proposed in recent years as one linear detection for MIMO systems. Compared with the traditional linear and maximum likelihood detection, IF can obtain a tradeoff between complexity and performance. In this paper, we apply IF detection to a MIMO V2V communication system with Alamouti STBC. The specific detection steps are clarified and the system performance is analyzed theoretically and proves to be full in diversity gain.
Row vectors are presented by boldface letters and matrices are denoted by capital boldface letters. (.)*, (.)H, (.)T denotes conjugation, transpose and Hermitian of a vector or matrix respectively. In and 0n represents n × n identity and zero matrix respectively. denotes the set of complex numbers, integers numbers and real numbers respectively. The operator ⊗ denotes Kronecker product. ‖ · ‖ and ‖ · ‖F denotes Euclidean norm of a vector and Frobenious norm respectively. Let ⌊x⌉ represents the closest integer to x and denotes component-wise equivalent operation.
Section snippets
Related work
The decoding complexity of MIMO is much higher than single antenna communication system due to that it utilizes multiple antennas for signal transmission and the received information includes multiple signals and interference. To recover the original transmitted information, the detection algorithm is very important since it directly affect the system performance. The common detection methods include ML and conventional linear detection with different performance and computational complexity.
ML
System model
Consider a system with K transmit nodes transmitting data to one receiving node at the same time, as shown in Fig. 3. The transmit node is equipped with Mk antennas, where k is the node index. The total number of transmit antennas is and the receiving node is equipped with N antennas. Assume that the channel is distributed as Rayleigh fading. The fading is independent and channel state information(CSI) is known at the receiver. Hk denotes the channel matrix between transmit
Known detection algorithms
In an MIMO system, the detection algorithms mainly falls into two types, ML detection and conventional linear detection.
Detection steps
Instead of detecting the transmit symbols directly, IF receiver detects the integer combinations of them at first and hence reduces the noise amplification to some extent, as shown in Fig. 4. For the IF receiver, one of the key steps is to find a preprocessing matrix B to force the channel matrix H to be a nonsingular integer matrix A as approximate as possible, i.e. BH ≈ A. In order to recover the Alamouti code using IF algorithm, it is necessary to satisfy 2NT ≥ 2Ms and the integer matrix A
Simulation results
In this section, simulation is carried out to verify the system performance. Independent and identically distributed quasi-static Rayleigh fading channel is assumed and the modulation order is 4. Constellation is adopted to ensure that the combination of codewords still belongs to the same constellation after mod operation.
Fig. 5 compares the bit error rate of ML detection and IF detection of a single-node V-BLAST MIMO system. Each transmit node is equipped with 2 antennas and
Conclusion
Because of the advantages, MIMO technology can be well applied in V2V communication. In an MIMO system, the detection algorithm is crucial since it can directly determine the quality of the entire communication system. In this paper, suitable MIMO system model for V2V communication and some detection algorithms are introduced, including the optimal ML algorithm, ZF and MMSE linear detection algorithm. However, these algorithms cannot provide good tradeoff between complexity and performance.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (grant nos. 61301124, 61471075 and 61671091), the University Innovation Team Construction Plan of Smart Medical System and Core Technology in Chongqing.
Guoquan Li received the Ph.D. degree in China in 2012. He is now an associate professor at the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China. His research interests include communication signal processing, multi-user MIMO systems, and body area network.
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Guoquan Li received the Ph.D. degree in China in 2012. He is now an associate professor at the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications (CQUPT), Chongqing, China. His research interests include communication signal processing, multi-user MIMO systems, and body area network.
Ying Zhou received her bachelor degree from The College of Posts and Telecommunication of Wuhan Institute of Technology in 2015 and she will receive M.S. degree in Communication and information technology institute from the Chongqing University of Posts and Telecommunications, China, in 2018. His research interest is wireless communication and the detection of wireless communication.
Tong Bai was born in Chongqing, China in 1987. He received a B.S. degree in Communication Engineering from the Chongqing University of Posts and Telecommunications, China, in 2010 and received M.S. degrees in Control Science and Engineering from the Chongqing University of Posts and Telecommunications, China, in 2015. He is currently pursuing a Ph.D. in Communications Engineering at Chongqing University of Posts and Telecommunications. From 2015 to 2017, he performed research with the Chongqing Ley Laboratory of Photoelectronic Information Sensing and Transmitting Technology, China. His research interests include wireless communication and body area networks.
Jinzhao Lin obtained doctor degree from Chongqing University in 2001. Now he is a professor of Chongqing University of Posts and Telecommunications. His main research interest includes wireless communications, digital signal processing and circuit design.
Yu Pang received a Ph.D. from the Department of Electrical and Computer Engineering at McGill University in 2010. He is currently a professor at Chongqing University of Posts and Telecommunications. His current research interests include wireless communications, circuit design and parallel computing.
Wei Wu was born in 1975. He received the B.S. degree from Tianjin University, Tianjin, China, in 1998, and the M.S. and Ph.D. degrees in communication and information systems from Sichuan University, Chengdu, China, in 2003 and 2008, respectively. He is currently an Associate Professor with the College of Electronics and Information Engineering, Sichuan University. His current research interests include image processing and video communications, and super resolution.
Sadia Din received his Bachelors in Computer Engineering from Comsats Institute of Information Technology Abbottabad, Pakistan. Currently, she is pursuing her Masters Leading Ph.D. degree at Kyungpook National University, Daegu, South Korea. Her research interests include IoT, Big Data analytics, Wireless Sensor Network, and 5G/4G.
Gwanggil Jeon received the B.S., M.S., and Ph.D. degrees in Hanyang University, in 2003, 2005, and 2008, respectively. From 2009 to 2011, he was a postdoctoral fellow at University of Ottawa, and from 2011 to 2012, he was an assistant professor at Niigata University. He is currently a professor at Incheon National University and Xidian University. His research interests fall under the umbrella of image processing, particularly image compression, motion estimation, demosaicking, and image enhancement as well as computational intelligence such as fuzzy and rough sets theories.