One of the crucial advancements in next-generation 5G wireless networks is the use of high-frequency signals specifically those are in the millimeter wave (mm-wave) bands. Using mmwave frequency will allow more bandwidth resulting higher user data rates in comparison to the currently available network. However, several challenges are emerging (such as fading, scattering, propagation loss etc.), whenever we utilize mm-wave frequency wave bands for signal propagation. Optimizing propagation parameters of the mm-wave channels system are much essential for implementing in the real-world scenario. To keep this in mind, this paper presents the potential abilities of high frequencies signals by characterizing the indoor small cell propagation channel for 28, 38, 60 and 73 GHz frequency band, which is considered as the ultimate frequency choice for many of the researchers. The most potential Close-In (CI) propagation model for mm-wave frequencies is used as a Large-scale path loss model. Results and outcomes directly affecting the user experience based on fairness index, average cell throughput, spectral efficiency, cell-edge user’s throughput and average user throughput. The statistical results proved that these mm-wave spectrum gives a sufficiently greater overall performance and are available for use in the next generation 5G mobile communication network.
Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix.