GPS Network: For position-based contexts, 1-D convolution neural networks (CNNs) capture spatial dependencies
and patterns in sequential coordinates. The network consists
of convolutional layers that apply learnable filters to local
subsequences of the input data, enabling the model to detect
positionwise spatial features and relationships. The input sets
of normalized GPS data gˆ are consecutively forwarded into the
layers of the CNN (as shown in Fig. 2) to produce the output
embedding ζ 1(gˆ). Concurrently these learned features are
forwarded to the transformer layer, particularly a self-attention
mechanism followed by multilayer perceptron (MLP).
2) LiDAR Network: A similar mechanism is adopted for
standardized point cloud P with a different number of filters.
The network architecture incorporates three layers of 1-D
CNN configured similarly to the feature extractor in the
PointNet architecture [40]. Each layer applies a set of filters to
the input point cloud data, enabling the extraction of geometric
spatial features from the point cloud. The layerwise features
from LiDAR point clouds are extracted as ζ (P) in the last
dimension to obtain the appropriate embedding before fusion.

 Vision Transformer: ViT redefine how images are
analyzed and understood by leveraging the principles of
transformers. The primary strength of ViT lies in its ability to
process images at a granular pixelwise level while simultaneously maintaining awareness of the entire image spatial-global
context. Hence, this model excels in extracting a deeper
understanding of image features, highlighting its superiority in
handling complex image analysis tasks [33], [43].To process
2-D images from RGB camera and YOLOv8 segmentation,
an image represented as Q is transformed into ν sequences
of vectorized 2-D p patches in Rν×(p2c) with the convolution
layer of c channels. This restructuring yields a sequence well
suited for the transformer architecture. A learnable linear
transformation is applied to each patch vector to accommodate
the transformer requirement for uniform dimensionality across
its layers, converting it to a model-specific dimension dm.
These transformed vectors are recognized as patch embedding.
Beforehand, for the wrapped camera data, we have to change
the view to images in R(Z×B) ×c×w×h to feed to the network
for compatibility, where B denotes the batch size. Similarly,
the embeddings are produced through MHSA of the ViT and
fed into the blocks of MLP to produce the output ζ (Q) for
the input images Q while ζ (Qs) for the image segments
Qs of Q, respectively, as shown in Fig. 3. In Table III, we
outline the specifications of various ViT models, ranging from
the lightweight ViT-Tiny to the ViT-Huge. ViT-Tiny and
ViT-Small are distilled variants optimized for performance,
featuring reduced resource consumption and faster inference
speeds. The table compares ViT variants based on layers,
embedding dimension (dm), MLP size, attention heads, and
parameters, highlighting the scalability and efficiency of each
model. For our model, we employ the ViT-Small with a patch embedding size of 32 for the edge-device practicality while
preserving the performance of the learnable features

We concatenate all the embeddings from the aforementioned
blocks. For the position-based network (as shown in Fig. 2),
we interpolate the output from the quantum layer to produce ζ 3(gˆ) while we utilize fully connected (FC) layers for
the multimodal network to produce ζ (gˆ) for matching their
dimensions with the other embeddings from the transformer
and CNN blocks. Wrapping up all the embeddings from the
transformer, convolution, and quantum layers, the combined
feature output ζ  is given by
ζ  =

ζ

gˆ


⊕ ζ

P

⊕ ζ (Q) ⊕ ζ (Qs), multimodal
ζ 1

gˆ


⊕ ζ 2

gˆ


⊕ ζ 3

gˆ


, GPS (22)
where ⊕ denotes the concatenation operation in the target
fused dimension. The fused features ζ , which combine
outputs from central processing unit (CPU) (CNN 1-D),
graphical processing unit (GPU) (ViT), and QPU (Quantum
Embedding), are then downsampled using FC layers on
the CPU into an M-array vector (in our case M = 64)
to represent the logits for M beam indices. The predicted
beam is selected by applying the Softmax function to these
logits and choosing the beam vector corresponding to the
index with the highest probability. This approach leverages
the CPU for efficiently handling dense computations in FC
layers, the GPU for parallel processing in ViT operations,
and the QPU for managing quantum embeddings, integrating
classical and quantum resources for accurate predictions. This
fusion process ensures that the model effectively captures and
consolidates information from different modalities, facilitating
comprehensive predictions.

now i have all the data needed for above in a csv file 
index,unit1_rgb,unit1_pwr_60ghz,unit1_lidar,unit1_radar,unit1_loc,unit2_loc,unit1_beam,unit1_max_pwr,time_stamp,seq_index,unit2_spd_over_grnd_kmph,unit2_num_sats,unit2_altitude,unit2_geo_sep,unit2_mode_fix_type,unit2_pdop,unit2_hdop,unit2_vdop,unit2_interpolated_position
1,./unit1/camera_data/image_1.jpg,./unit1/mmWave_data/mmWave_power_1.txt,./unit1/lidar_data/lidar_data_1.ply,./unit1/radar_data/radar_data_1.npy,./unit1/GPS_data/gps_location.txt,./unit2/GPS_data/GPS_location_1.txt,3,0.174674109,01:22:30-405771,1,5.82,12.0,356.81,-27.749,3.0,1.09,0.58,0.92,0.0
2,./unit1/camera_data/image_2.jpg,./unit1/mmWave_data/mmWave_power_2.txt,./unit1/lidar_data/lidar_data_2.ply,./unit1/radar_data/radar_data_2.npy,./unit1/GPS_data/gps_location.txt,./unit2/GPS_data/GPS_location_2.txt,3,0.254460901,01:22:30-497819,1,,,,,,,,,1.0

this is the format of the csv file now write a google colab code accordingly which generate top 5 beams and calculate the accruracy , i have also given you lidar and gps file type so you can see the format and write the code accordingly