Doctor of Philosophy (Ph.D.)
Degree Granting Department
Richard D. Gitlin, Sc.D.
Nasir Ghani, Ph.D.
Ismail Uysal, Ph.D.
Srinivas Katkoori, Ph.D.
Gabriel Arrobo, Ph.D.
MAC, mmWave, NOMA, Physical Layer Security, Recurrent Neural Network
This dissertation presents several novel approaches to enhance security, and increase the throughput, and decrease the delay synchronization in 5G networks.
First, a new physical layer paradigm was proposed for secure key exchange between the legitimate communication parties in the presence of a passive eavesdropper was presented. The proposed method ensures secrecy via pre-equalization and guarantees reliable communications using Low-Density Parity Check (LDPC) codes. One of the main findings of this research is to demonstrate through simulations that the diversity order of the eavesdropper will be zero unless the main and eavesdropping channels are almost correlated, while the probability of a key mismatch between the legitimate transmitter and receiver will be low. Simulation results demonstrate that the proposed approach achieves very low secret key mismatch between the legitimate users while ensuring very high error probability at the eavesdropper.
Next, a novel medium access control (MAC) protocol Slotted Aloha-NOMA (SAN), directed to Machine to Machine (M2M) communication applications in the 5G Internet of Things (IoT) networks was proposed. SAN is matched to the low-complexity implementation and sporadic traffic requirements of M2M applications. Substantial throughput gains are achieved by enhancing Slotted Aloha with non-orthogonal multiple access (NOMA) and a Successive Interference Cancellation (SIC) receiver that can simultaneously detect multiple transmitted signals using power domain multiplexing. The gateway SAN receiver adaptively learns the number of active devices using a form of multi-hypothesis testing and a novel procedure enables the transmitters to independently select distinct power levels. Simulation results show that the throughput of SAN exceeds that of conventional Slotted Aloha by 80% and that of CSMA/CA by 20% with a probability of transmission of 0.03, with a slightly increased average delay owing to the novel power level selection mechanism.
Finally, beam sweeping pattern prediction, based on the dynamic distribution of user traffic, using a form of recurrent neural networks (RNNs) called Gated Recurrent Unit (GRU) is proposed. The spatial distribution of users is inferred from data in call detail records (CDRs) of the cellular network. Results show that the user's spatial distribution and their approximate location (direction) can be accurately predicted based on CDRs data using GRU, which is then used to calculate the sweeping pattern in the angular domain during cell search. Furthermore, the data-driven proposed beam sweeping pattern prediction was compared to random starting point sweeping (RSP) to measure the synchronization delay distribution. Results demonstrate the data- drive beam sweeping pattern prediction enables the UE to initially assess the gNB in approximately 0.41 of a complete scanning cycle that is required by the RSP scheme with probability 0.9 in a sparsely distributed UE scenario.
Scholar Commons Citation
Mazin, Asim, "Methods and Algorithms to Enhance the Security, Increase the Throughput, and Decrease the Synchronization Delay in 5G Networks" (2019). USF Tampa Graduate Theses and Dissertations.