# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Bottleneck-Aware Congestion Control for Low-Latency Transmission  
Optimizing network performance requires bottleneck-aware congestion reduction, particularly in settings where traffic might adversely affect data delivery. A \"low-latency congestion control scheme (LLCCS)\", which concentrate on variables like bottleneck bandwidth and round-trip propagation time, are used to address this. An LLCCS dynamically shifts data rates adaptively in real-time according to network conditions such as bottleneck bandwidth and round-trip time. It works towards keeping low latency and high throughput with congestion avoidance before the occurrence of performance degradation. This approach plays a vital role in dealing with variable traffic in 5G mmWave networks. Through the evaluation of these characteristics, LLCCS can detect and handle network bottlenecks that result in latency and decreased throughput. By adjusting data transmission rates dynamically in response to actual network conditions, these schemes make sure that congestion is reduced before it causes a noticeable decline in performance.  
Numerous congestion control algorithms with low latency have been suggested in research to address the issue of buffer bloat. BBR aims to minimize queueing delays by functioning within the \"bandwidth-delay product (BDP)\" of a route in the system. BBR restricts the number of packets transferred to a fixed number of the projected BDP and updates its estimates for the bottleneck bandwidth and minimum RTT of the network path frequently. TCP BBR's initial implementation consists of four stages. During the initial stage, known as the startup phase, the goal is to promptly determine the bottleneck bandwidth by estimating it. Next, during the second phase known as the drain phase, the sending rate is decreased to empty the queue accumulated in the initial phase. BBR reaches a steady state where it alternates between conducting bandwidth probing and \"round-trip time (RTT)\" probing. BBR mainly focuses on bandwidth probing, typically sending data at the estimated bottleneck bandwidth rate, while occasionally exceeding this rate to find any extra capacity. In this stage, the CWND is adjusted to twice the calculated BDP. BBR continuously tracks the lowest RTT, and if 10 s pass without any changes to this value, it starts the RTT exploring phase. During the RTT exploring phase, BBR configures its CWND to four segments to clear the queue and ascertain the connection's base RTT. BBR returns to the bandwidth exploring step after updating the minimal RTT estimation.  
Version 2 of BBR brings about several alterations; however, it functions based on the same concept of calculating the BDP, focusing mainly on seeking additional bandwidth, and occasionally lowering its transmission speed to calculate the shortest RTT. L4S is a network service design that consists of the following two main parts, with a focus on low latency, low loss, and scalable throughput:  
* TCP Prague traffic management: TCP Prague, much like DCTCP, relies on precise ECN feedback to regulate the rate of sending. Nevertheless, it contains various additions and modifications compared to DCTCP, which enhance its compatibility for Internet usage, especially when sharing links with loss-based congestion control flows.
* Dual-Q paired AQM: The L4S architecture uses a Dual-Q paired AQM to address disparity in loss-based traffic flow control and to maintain minimal queuing latency for flows that comply with ECN. To provide fairness and low latency for ECN-responsive flows, this queue differentiates between classic loss-based streams and ECN-responsive streams.  
Through the use of comprehensive data on round-trip times and bottleneck bandwidth, LLCCS can make well-informed adjustments that minimize latency, avoid congestion, and maximize resource usage. This proactive strategy guarantees consistent and effective network performance even with fluctuating traffic volumes. We have enhanced the overall transmission control for 5G networks.