Due to its strong fitting ability, machine learning is seen to have great potential to be employed to solve telecommunication networks’ optimization problems that range from the design of hardware elements to network self-optimization. In fact, intelligent forecasting and decision-making strategies are several of the centerpieces of current artificial intelligence research in various domains. Mobile operators and network vendors enrolling in 5G face far more rapid demands than any technology before, and at the same time need to ensure efficiency and reliability in the network operations. With the growth in the amount of generated data, the number of wirelessly connected machines, traffic types, and associated requirements, ensuring high-quality mobile connectivity becomes incredibly difficult for technology suppliers.
= 20 dBĞxcellent Strong signal with maximum data speedsġ3 dB to 20 dB Good Strong signal with good data speedsĠ dB to 13 dBğair to poor Reliable data speeds may be attained, but marginal data with drop-outs is possible.The fifth generation (5G) of mobile networks connects people, things, data, applications, transport systems, and cities in smart networked communication environments.
When this value gets close to -20, performance will drop drastically 15 dB to -20 dBğair to poor Reliable data speeds may be attained, but marginal data with drop-outs is possible. 10 dB to -15 dB Good Strong signal with good data speeds = -10 dBĞxcellent Strong signal with maximum data speeds When this value gets close to -100, performance will drop drastically
90 dBm to -100 dBmğair to poor Reliable data speeds may be attained, but marginal data with drop-outs is possible. 80 dBm to -90 dBm Good Strong signal with good data speeds >= -80 dBmĞxcellent Strong signal with maximum data speeds
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