A Research Of Signal Detection Algorithms In Multiple Antenna Systems

A critical fundamental technology conceived for the forthcoming fifth generation communication systems(5G)is closely related to multiple-input multiple-output(MIMO),which employs a number of antennas at the transmitter and receiver to achieve high spectral-efficiency and energy-efficiency.In order to realize the substantial benefits of MIMO techniques,it is important to design a high-efficiency detection algorithm in receiver.Specifically,the complexity of signal detection will greatly increase as the drastic increase of antenna number in 5G MIMO systems.Thus,it is still extraordinarily necessary to study the signal detection problem to achieve satisfied detection algorithms with low complexity and excellent performance,especially for large MIMO systems.In this paper,the joint signal detection for MIMO systems is further studied,and a series of low-complexity and high-performance detection algorithms are proposed.The main contributions of the dissertation are summarized as follows.1.The typical detection algorithms in MIMO systems are studied,and then we propose a low-complexity list SML-SIC detection algorithm.The sub-detectors involved in the proposed algorithm consist of simplified maximum-likelihood(SML)detectors and successive interference cancellation(SIC)detectors.Adjacent symbols are utilized in the proposed algorithm to realize joint-sliding detection to achieve higher diversity gain,while the traditional SIC algorithm is only able to detect a single symbol each time.Analysis and simulation results present that the list SML-SIC detector performs significantly enhanced performance at the cost of slightly increased complexity,comparing with the traditional list SIC detection algorithm.2.A low-complexity space alternating iterative filtering(SAIF)algorithm is proposed,which quickly converges and efficiently improves the performance of traditional MIMO detection algorithms.The results of the traditional detectors are used as initial points in SAIF algorithm to converge to better output value with smaller Euclidean distance between the filter output and the received signal.In each iteration process,the proposed algorithm jointly detects partial symbols in the hidden sub-space which cancels the interference of other symbols.Analysis and simulation results show that the proposed SAIF aided MIMO detectors have slightly higher complexity while achieving a considerable enhancement of performance compared to original MIMO detectors.3.A low-complexity group alternate iterative list(GAIL)detection algorithm for MIMO systems is proposed.By utilizing the recursive interference suppression and successive interference cancellation techniques,the symbol vector can be partitioned into many subgroups.Subsequently,symbols in each subgroup are detected in terms of the K-best detector.The inter-group interference is effectively mitigated in the GAIL algorithm by creating a candidate list and iteratively correcting the unreliable symbols in the detection result.We provide a feasible performance-complexity tradeoff based on different parameter settings.The numerical results show that the GAIL algorithm can achieve close-to-optimal performance while maintaining low computational complexity.In addition,the running speed of the GAIL algorithm increases dramatically by using parallel processing in real-time communication systems.4.From the view of maximizing the minimum received signal-to-noise ratio,a fast dual-lattice reduction(FDLR)algorithm is proposed to minimize the orthogonality deficiency of dual-basis.Meanwhile,a tree search implementation method of FDLR is provided,which enables flexible tradeoff between performance and complexity.Compared to the existing dual LLL(DLLL)algorithm,FDLR algorithm requires less iteration time and yields more orthogonal basis vectors.Moreover,FDLR aided detectors achieve better performance and lower complexity than DLLL aided detectors,especially for large MIMO systems.