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.

The next-generation wireless technologies are currently being researched to address the ever-increasing demands for higher data rates, massive connectivity, improved reliability, and extended coverage. Recently, massive multiple-input multiple-output (MIMO) has gained significant attention as a new physical-layer transmission technology that can achieve unprecedented spectral and energy efficiency gains via aggressive spatial multiplexing. Thus, massive MIMO has been one of the key enabling technologies for the fifth-generation and subsequent wireless standards. This dissertation thus focuses on developing a system, channel, and signal models by considering the practical wireless transmission impairments for massive MIMO systems, and ascertaining the viability of massive MIMO in fulfilling massive access, improved spectrum, enhanced security, and energy efficiency requirements. Specifically, new system and channel models, pilot sequence designs and channel estimation techniques, secure transmit/receive beamforming techniques, transmit power allocation schemes with enhanced security provisions, energy efficiency, and user fairness, and comprehensive performance analysis frameworks are developed for massive MIMO-aided non-orthogonal multiple access (NOMA), cognitive spectrum-sharing, and wireless relaying architectures.Our first work focuses on developing physical-layer transmission schemes for NOMA-aided massive MIMO systems. A spatial signature-based user-clustering and pilot allocation scheme is first formulated, and thereby, a hybrid orthogonal multiple access (OMA)/NOMA transmission scheme is proposed to boost the number of simultaneous connections. In our second work, the viability of invoking downlink pilots to boost the achievable rate of NOMA-aided massive MIMO is investigated. The third research contribution investigates the performance of underlay spectrum-sharing massive MIMO systems for reverse time division duplexing based transmission strategies, in which primary and secondary systems concurrently operate in opposite directions. Thereby, we show that the secondary system can be operated with its maximum average transmit power independent of the primary system in the limit of infinity many primary/secondary base-station antennas. In our fourth work, signal processing techniques, power allocation, and relay selection schemes are designed and analyzed for massive MIMO relay networks to optimize the trade-off among the achievable user rates, coverage, and wireless resource usage. Finally, the cooperative jamming and artificial noise-based secure transmission strategies are developed for massive MIMO relay networks with imperfect legitimate user channel information and with no channel knowledge of the eavesdropper. The key design criterion of the aforementioned transmission strategies is to efficiently combine the spatial multiplexing gains and favorable propagation conditions of massive MIMO with properties of NOMA, underlay spectrum-sharing, and wireless relay networks via efficient signal processing.

This paper is persuaded to understand the physical layer security in wireless commu-nications utilizing NOMA (Non Orthogonal Multiple Access) concepts in the presence of an eavesdropper. Physical layer security maintains the confidentiality and secrecyof the system against eavesdroppers. We use the power domain in this paper, where NOMA allows many users to share resources side by side. Power allocation concern-ing channel condition is taken into consideration where user whose channel condition is weak is allocated with eminent power to directly decode the signal, whereas theuser with better channel condition applies successive interference cancellation (SIC)to decode the signal. Here, the base station communicates with the users and sends data signals while the eavesdropper secretly eavesdrops on the confidential informa-tion simultaneously. In this thesis, to improve the physical layer security, jamming method was usedwhere users are assumed to be in full duplex, send jamming signals to degrade the performance of the eavesdropper. Analytic expressions of CDF, PDF, outage proba-bility and secrecy capacity are obtained from analyzing the NOMA jamming scheme. The numerical results are evaluated with the simulations results and analysed theeffect of jamming on improving the performance of the NOMA system in presenceof an eavesdropper.

In the area of computer and network security, due to the insufficiency, high costs, and user-unfriendliness of existing defending methods against a number of cyber attacks, focus for developing new security improvement methods has shifted from the digital to analog domain. In the analog domain, devices are distinguished based on the present variations and characteristics in their physical signals. In fact, each device has unique features in its signal that can be used for identification and monitoring purposes. In this regard, the term physical layer identification (PLI) or device fingerprinting refers to the process of classifying different electronic devices based on their analog identities that are created by employment of signal processing and data analysis methods. Due to the fact that a device behavior undergoes changes due to variations in external and internal conditions, the available PLI techniques might not be able to identify the device reliably. Therefore, a tracking system that is capable of extracting and explaining the present variations in the electrical signals is required to be developed. In order to achieve the best possible tracking system, a number of prediction models are designed using certain statistical techniques. In order to evaluate the performance of these models, models are run on the acquired data from five different fabrications of the same device in four distinct experiments. The results of performance evaluation show that the surrounding temperature of a device is the best option for predicting its signal. The last part of this research project belongs to the security evaluation of a PLI system. The leveraged security examination technique exposes the PLI system to different types of attacks and evaluates its defending strength accordingly. Based on the mechanism of the employed attack in this work, the forged version of a device’s signal is generated using an arbitrary waveform generator (AWG) and is sent to the PLI system. The outcomes of this experiment indicate that the leveraged PLI technique is strong enough in defeating this attack.

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In order to establish a secure connections in the wireless environment, cryptographic methods may require an exchange of a key or secret. Fortunately, the environment provides randomness due to multi-path fading that can be exploited by physical-layer security algorithms to help establish this shared secret. However, in some cases, multi-path fading might be absent or negligible; therefore, we look for artificial ways to increase randomness. In this thesis, we explore antenna radiation variation by altering the phase between two antennas as a means of creating artificial fading. We construct a model of the antenna gain variation by analyzing the radiation pattern and run Monte-Carlo simulations to compare our approach to a base case with only multi-path fading. We then empirically collect data in order to confirm our analysis. Finally, we incorporate this model in a prominent security algorithm to demonstrate the improvements in security possible through such an approach.

This thesis consists of two research projects on wireless communication systems. In the first project, we propose a fast inphase and quadrature (I/Q) imbalance compensation technique for the analog quadrature modulators in direct conversion transmitters. The method needs no training sequence, no extra background data gathering process and no prior perfect knowledge of the envelope detector characteristics. In contrast to previous approaches, it uses points from both the linear and predictable nonlinear regions of the envelope detector to hasten convergence. We provide a least mean square (LMS) version and demonstrate that the quadrature modulator compensator converges. In the second project, we propose a technique to deceive the automatic gain control (AGC) block in an eavesdropper's receiver to increase wireless physical layer data transmission secrecy. By sharing a key with the legitimate receiver and fluctuating the transmitted signal power level in the transmitter side, a positive average secrecy capacity can be achieved even when an eavesdropper has the same or even better additive white gaussian noise (AWGN) channel condition. Then, the possible options that an eavesdropper may choose to fight against our technique are discussed and analyzed, and approaches to eliminate these options are proposed. We demonstrate that a positive average secrecy capacity can still be achieved when an eavesdropper uses these options.

The Internet of Things (IoT) impacts nearly all aspects surrounding our daily life, including housing, transportation, healthcare, and manufacturing. IoT devices communicate through a variety of communication protocols, such as Bluetooth Low Energy (BLE), Zigbee, Z-Wave, and LoRa. These protocols serve essential purposes in both commercial industrial and personal domains, encompassing wearables and intelligent buildings. The organic and decentralized development of IoT protocols under the auspices of different organizations has resulted in a fragmented and heterogeneous IoT ecosystem. In many cases, IoT devices do not have an IP address. Furthermore, some protocols, such as LoRa and Z-Wave, are proprietary in nature and incompatible with standard protocols. This heterogeneity and fragmentation of the IoT introduce challenges in assessing the security posture of IoT devices. To address this problem, this thesis proposes a novel methodology that transcends specific protocols and supports network and security monitoring of IoT devices at scale. This methodology leverages the capabilities of software-defined radio (SDR) technology to implement IoT protocols in software. We first investigate the problem of IoT network reconnaissance, that is the discovery and characterization of all the IoT devices in one’s organization. We focus on four popular protocols, namely Zigbee, BLE, Z-Wave, and LoRa. We introduce and analyze new algorithms to improve the performance and speed-up the discovery of IoT devices. These algorithms leverage the ability of SDRs to transmit and receive signals across multiple channels in parallel. We implement these algorithms in the form of an SDR tool, called IoT-Scan, the first universal IoT scanner middleware. We thoroughly evaluate the delay and energy performance of IoT-Scan. Notably, using multi-channel scanning, we demonstrate a reduction of 70% in the discovery times of Bluetooth and Zigbee devices in the 2.4GHz band and of LoRa and Z-Wave devices in the 900MHz band, versus single-channel scanning. Second, we investigate a new type of denial-of-service attacks on IoT cards, called Truncate-after-Preamble (TaP) attacks. We employ SDRs to assess the security posture of off-the-shelf Zigbee and Wi-Fi cards to TaP attacks. We show that all the Zigbee devices are vulnerable to TaP attacks, while the Wi-Fi devices are vulnerable to the attack to a varying degree. Remarkably, TaP attacks demand energy consumption five orders of magnitude lower than what is required by a continuous jamming mechanism. We propose several countermeasures to mitigate the attacks. Third, we devise an innovative approach for the purpose of identifying and creating unique profiles for IoT devices. This approach leverages SDRs to create malformed packets at the physical layer (e.g., truncated or overlapping packets). Experiments demonstrate the ability of this approach to perform fine-grained timing experiments (at the microsecond level), craft multi-packet transmissions/collisions, and derive device-specific reception curves. In summary, the results of this thesis validate the feasibility of our proposed SDR-based methodology in addressing fundamental security challenges caused by the heterogeneity of the IoT. This methodology is future-proof and can accommodate new protocols and protocol upgrades.

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