Abstract
In this paper, we investigate the channel estimation and decoding methods exploiting the channel sparsity in pilot-assisted Multiple-Input Multiple-Output (MIMO) Vector Orthogonal Frequency Division Multiplexing (V-OFDM) systems. Based on the sparse multipath channels, we utilize orthogonal and non-orthogonal pilot schemes to design the compressed sensing (CS) measurement process. For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with the simplified evaluation criterion to improve the pilot design procedure. Meanwhile, the effect of pilot insertion on the Peak-to-Average Power Ratio (PAPR) is reduced by a particular precoding matrix method without affecting the decoding complexity. Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method has higher channel estimation accuracy with unknown sparsity. On the other hand, to overcome the complexity of CS-based decoding, we design the fully connected Deep Neural Network (FC-DNN) decoders, which combine the results of channel estimation results with the prevalent neural network technology. We observe that when the sparse channels are estimated accurately by CS methods, the proposed FC-DNN can achieve the same performance as the high-precision linear decoder by using the time-domain pilots and channel estimation results.
Highlights
Vector Orthogonal Frequency Division Multiplexing (V-OFDM) is proposed by [1] as a precoded OFDM system, which can be treated as a tradeoff between the spectrum resource management and Peak-to-Average Power Ratio (PAPR) reduction [2]
For sparse Multiple-Input Multiple-Output (MIMO) channel estimation, orthogonal pilot distribution in the frequency-domain is extensively applied as shown in Fig.2(a), which means pilots distribute in different positions on different antennas
In this paper, a practical OFDM framework is constructed by the pilot-assisted method
Summary
Vector Orthogonal Frequency Division Multiplexing (V-OFDM) is proposed by [1] as a precoded OFDM system, which can be treated as a tradeoff between the spectrum resource management and Peak-to-Average Power Ratio (PAPR) reduction [2]. Some packages which can create firmware implementations of neural network (NN) algorithms using High-level synthesis (HLS) language are in the process of development [14], and it means that we can further simplify the design of the OFDM receiver These feasible frameworks are not mature enough to completely replace the conventional methods, the current work has shown that researchers’ attention has changed from the sophisticated communication algorithm to the research of low cost and more straightforward implementation solutions. This paper designs a pilot-assisted sparse channel estimation scheme in MIMO-V-OFDM systems and the receiver is proposed by using the fully connected Deep Neural Network (FC-DNN) to compensate for the shortcomings of the CS methods. The SAMP algorithm for time-varying sparse channels achieves excellent results in SISO-OFDM systems, which is based on block-type pilots [23].
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