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Upcoming 5G networks are expected to enable data transmissions of ultra-high-speeds, nearly x1000 times faster than the speeds of current Long Term Evolution (LTE) networks, support a significantly larger number of user devices (x10 up to x100 times more devices), provide ultra-low latencies (≤ 1ms) that are 5 times lower than existing LTE latencies and prolong device battery lifetimes (x10 times). Such networks should also be capable of satisfying the variant requirements of network services, such as enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable and low latency communication (URLLC). The fact that each and every one of the aforementioned services are in need of different requirements (e.g., eMBB services require very high bandwidth and mMTC services require ultra-dense connectivity), it goes without saying that homogeneous networks would never be able to efficiently satisfy such services. As a result, 5G networks come into play with sufficient resources by using network function virtualization (NFV) and Software Defined Networking (SDN) technologies, where using network softwarization techniques, network operators may set up, configure and control network slices (International Telecommunication Union, 2015; International Telecommunication Union, 2018).
Having considered all the above, the authors should be able to comprehend why such big amounts of complex data need to be processed so that operators may efficiently design, construct, deploy and manage network slices to satisfy the users’ Quality of Service (QoS) needs. Meanwhile, according to Ghaleb et al. (2018), transmission power limitations in are currently being confronted through very low code rates and modulation schemes of high order, an approach which results in high levels of spectral efficiency (SE). Thus, 5G HetNet architecture should eventually turn from the existing models that are considered network-centric (NC) into models that are user-centric (UC). Such models can provide improved connection between network users and Base Stations (BSs) inside HetNets. What is different is that fact that a HetNet chooses to decouple the homogeneous network into two independent networks, which are the downlink (DL) and the uplink (UL) networks. A User Equipment (UE) takes advantage of this decoupling and now may connect to different BSs in the UL and DL, providing increased freedom to the decoupled networks. Furthermore, HetNets are generally expected to extend the existing macro cell infrastructures though small cell deployments placed close to the macro cell borders, in order to offer extended coverage and data rates for UEs near the macrocell’s borders. The optimal Modulation and Coding Scheme (MCS) is necessary to be selected, due to the fact that will define the practical throughput for a user that is linked to a BS.
In this work, the authors will present a resource-aware mechanism that targets at improving network coverage through the decoupling of a dense HetNet into the UL and DL networks. Increased demands and requirements that 5G networks expect are satisfied covered by incorporating and applying the 5G NR radio interface protocol. The proposed mechanism fully preserves users’ QoS and provides higher data rates than those initially requested, but in favor of coverage for all network users. The algorithm requires knowledge of the Resource Block (RB) demands for each device and begins iterating, starting from users that have the lowest RB demands, so that the maximum number of users is satisfied. Data throughputs derive from the appropriate selection of the optimal MCS inside each distinct macrocell area. The authors perform the simulations in both applicable 5G physical layer scenarios, which are the settings of Frequency Range 1 (FR1) and Frequency Range 2 (FR2). Simulation results revealed that the proposed mechanism does indeed succeed at respecting users’ QoS demands and ends up providing greatly augmented data rates than originally requested, as promised. Furthermore, the fact that Frequency Range 2 offers an increased amount of resources, it is revealed that this equivalent simulation provides both increased data rates and higher amounts of devices that are covered by the overall network.