## 1 Introduction  
Massive multiple input multiple output (MIMO) makes a substantial contribution to augmenting the throughput of wireless communication systems and providing high-speed data services, which is essential in future mobile communication systems [1; 2]. Massive MIMO systems can be categorized into two principal domains: time division duplexing (TDD) systems and frequency division duplexing (FDD) systems where orthogonal time and frequency are recognized to distinguish the uplink and downlink [3; 4]. A critical prerequisite for achieving the desired transmission quality in massive MIMO is the availability of perfect channel state information (CSI) at both the base station (BS) and the user equipment (UE). Leveraging the accurate CSI, the BS can design the appropriate precoding vector, thus improving the transmission quality. This is especially significant in FDD systems because, unlike in TDD systems where channel reciprocity is taken into account, the CSI of the downlink cannot be directly obtained from the channel estimation at the BS [5; 6; 7]. The CSI at the BS in FDD systems relies heavily on accurate feedback from the UE in real time, which poses a formidable challenge since the feedback overhead increases in a linear manner as the number of antennas grows [8]. To reduce the feedback cost, scholars naturally explore techniques for compressing the downlink CSI at the UE and subsequently recovering the original CSI at the BS. A variety of schemes have been devised to narrow the gap between the recovered CSI and the actual CSI. Conventional methods for CSI feedback, such as those utilizing codebooks and compressive sensing (CS), struggle to balance low complexity with high accuracy. Therefore, CSI compression and feedback mechanisms in massive MIMO systems have begun leveraging deep learning (DL) technologies. This approach is inspired by the autoencoder architecture commonly used in image compression. The deep learning based methods can be divided into two categories, one focusing on improving the performance of CSI feedback with novel neural network (NN) architecture design and multi-domaincorrelation utilization, the other favoring joint design with other modules and practical consideration to promote the practical deployment of DL-based CSI feedback. In recent years, the Transformer architecture designed for natural language processing (NLP), which is marked by the attention mechanism, has been applied to numerous research areas [9]. A convolutional Transformer structure named CsiFormer is proposed in [10] to compensate for NN methods' deficiency in long-range dependency. TransNet [11] presents a two-layer Transformer architecture, enabling the encoder to learn connections between various parts of the CSI during feedback. Its outstanding results underscore the immense potential of the Transformer architecture in the realm of CSI feedback. According to TransNet, higher dimensional feature representation is beneficial to obtain higher-resolution CSI features. In this paper, we first use feature representation with higher dimensions to improve the feedback quality of the model at high compression rates. Secondly, the warm-up-aided cosine training scheme proposed in [12] is exploited. It can not only accelerate the training speed, but also enhance the model's capacity to learn high-resolution features. We present a comprehensive analysis that includes FLOPs and recovery accuracy for different models based on the attention mechanism in CSI feedback. Furthermore, we conduct a comparative experiment to investigate the impact of varying numbers of attention heads within the multi-head attention layer.