Hybrid Beamforming Projects

Hybrid Beamforming Projects encounter the most important aspects to do the hybrid beamforming. In short, Hybrid beamforming is the combination of analog and digital beamforming that reduces complexity and also power consumption. In a while, it refers to HBF. All the user equipment (UE) is connected to the base station over uplink for data transmission. As per the increase in interference, the number of user’s data transmission would be lost. In the end, it induces a huge latency. 

It is very hard to find the digital and analog BF matrices to optimize HBF. In addition, it must perform well in two cases as LOS and NLOS. It means a line of sight and non-line of sight, respectively. There are three solutions for HBF as follows. 

• Channel State Information (CSI) Prediction 
• Architecture Complexity based (Full & Reduced Complexity, and also Switched)
• Carrier Frequency (cmWave and mmWave)

   Besides, CSI is a key element here. It impacts in the HBF. Unlike other factors, environmental changes also affect this performance. When we pool these two into one, we can obtain a better result. 

Research Issues: In the making of a good Hybrid Beamforming Projects 

• Higher overhead for CSI acquisition
• Under blockages such as static, dynamic, and also self-blockage
• Optimal Number of RF Chains 
• Joint Digital and also Analog Precoders 
• Beam Training and also Feedback

   Due to the above problems, it needs to design the best HBF as we need to overcome the snags one by one. We ready to solve it even though HBF is a hard topic. Finally, we have the novel ideas as we want.  

WHERE HBF TAKES PLACE?

• D2D
• Car2X (V2X)
• IoT
• Internet of Vehicles
• M2M
• HetNet

HYBRID BEAMFORMING AT MMWAVE 

   The ever-rising request for a high data rate and also more UEs capacity raises the need to use the spectrum more robustly. So, the next generation i.e., 5G Network Simulation Projects will use a millimeter wave (mmWave) band to take benefit of its wide bandwidth. Without a doubt, mmWave communication provides seamless connectivity for UEs. It is also used for both indoor and outdoor. Here, a number of methods are in use as follows.

• Use of Codebooks 
• Spatially Sparse Precoding 
• Antenna Selection 
• Optimum Beam Selection 
• Interference Prediction (Inter & Intra Cell)
• Dual-Band Access under Small Cells (Licensed and Unlicensed)

   Deep learning (DL) is a very close solution to optimize hybrid beamforming projects. A DL based HBF supports for complex scenarios as follows. 

• UAV-assisted communication
• Cell-free massive MIMO scenarios
• NOMA-based backscattering communication 

   The first thing to remember while we work on the project is to select the novel idea. For the above novel cases, we use the best DL method. Some of the few methods include the following. 

• CNN
• AlexNet
• GooLeNet
• ResNet
• RNN
• DAE, and also SAE

   There are many parameters for HBF projects. The parameters that are taking into account for evaluation follows.

• Blockage probability
• Latency
• Served number of users
• Spectral energy efficiency
• Achievable rate 
• Channel capacity
• Bit Error Rate 
• End-to-End SINR

   This page presents the concept of HBF. This topic can closely match the electronics students. Thus, you can choose your HBF projects if you not yet clear how our projects work means; call us to know more.

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