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The use of orthogonal frequency-division multiplexing (OFDM) by wireless standards is often preferred due to its high spectral efficiency and ease of implementation. However, data transmission via OFDM still suffers when passing through a noisy channel. In order to maximize the abilities of OFDM, channel effects must be corrected. Unfortunately, channel estimation is often difficult due to the nonlinearity and randomness present in a practical communication channel. Recently, machine learning based approaches have been used to improve existing channel estimation algorithms for a more efficient transmission. This thesis investigates the application of artificial neural networks (ANNs) as a means of improving existing channel estimation techniques. Multi-layer feed forward neural networks (FNNs) and convolutional neural networks (CNNs) are tested on a variety of random fading channels with different signal-to-noise ratios (SNRs) via computer simulations. Compared to the conventional least squares (LS) algorithm, the approach based on CNN can reduce the bit error rate (BER) of data transmission by an average of 47.59%.

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No / Abstract: The scarcity of frequency spectrum used for wireless communication systems has attracted a considerable amount of attention in recent years. The cognitive radio (CR) terminology has been widely accepted as a smart platform mainly aimed at the efficient interrogation and utilization of permitted spectrum. Non-parametric spectrum sensing, or estimation, represents one of the prominent tools that can be proposed when CR works under an undetermined environment. As such, the periodogram, filter bank, and multi-taper methods are well considered in many studies without relying on the transmission channel's characteristics. A unified approach to all these non-parametric spectrum sensing techniques is presented in this paper with analytical and performance comparison using simulation methods. Results show that the multi-taper method outperforms the others.

We implement reinforcement learning in the context of wireless communications in two very different settings. In the first setting, we study the use of reinforcement learning in an underwater acoustic communications network to adapt its transmission frequencies to avoid interference and potential malicious jammers. To that effect, we implement a reinforcement learning algorithm called contextual bandits. The harsh environment of an underwater channel provides a challenging problem. The channel may induce multipath and time delays which lead to time-varying, frequency-selective attenuation. These factors are also influenced by the distance between the transmitter and receiver, the subbands the interference is located within, and the power of the transmitter. We show that the agent is effectively able to avoid frequency bands that have degraded channel quality or that contain interference, both of which are dynamic or time-varying . In the second setting, we study the use of reinforcement learning to adapt the modulation and power scheme of a jammer seeking to disrupt a wireless communications system. To achieve this, we make use of a linear contextual bandit to learn to jam the victim system. Prior work has shown that with the use of linear bandits, improved convergence is achieved to jam a single-carrier system using time-domain jamming schemes. However, communications systems today typically employ orthogonal frequency division multiplexing (OFDM) to transmit data, particularly in 4G/5G networks. This work explores the use of linear Thompson Sampling (TS) to jam OFDM-modulated signals. The jammer may select from both time-domain and frequency-domain jamming schemes. We demonstrate that the linear TS algorithm is able to perform better than a traditional reinforcement learning algorithm, upper confidence bound-1 (UCB-1), in terms of maximizing the victim's symbol error rate. We also draw novel insights by observing the action states, to which the reinforcement learning algorithm converges. We then investigate the design and modification of the context vector in the hope of in- creasing overall performance of the bandit, such as decreased learning period and increased symbol error rate caused to the victim. This includes running experiments on particular features and examining how the bandit weights the importance of the features in the context vector. Lastly, we study how to jam an OFDM-modulated signal which employs forward error correction coding. We extend this to leverage reinforcement learning to jam a 5G-based system implementing some aspects of the 5G protocol. This model is then modified to introduce unreliable reward feedback in the form of ACK/NACK observations to the jammer to understand the effect of how imperfect observations of errors can affect the jammer's ability to learn. We gain insights into the convergence time of the jammer and its ability to jam the victim, as well as improvements to the algorithm, and insights into the vulnerabilities of wireless communications for reinforcement learning based jamming. / Master of Science / In this thesis we implement a class of reinforcement learning known as contextual bandits in two different applications of communications systems and jamming. In the first setting, we study the use of reinforcement learning in an underwater acoustic communications network to adapt its transmission frequencies to avoid interference and potential malicious jammers. We show that the agent is effectively able to avoid frequency bands that have degraded channel quality or that contain interference, both of which are dynamic or time-varying. In the second setting, we study the use of reinforcement learning to adapt the jamming type, such as using additive white Gaussian noise, and power scheme of a jammer seeking to disrupt a wireless communications system. To achieve this, we make use of a linear contextual bandit which implies that the contexts that the jammer is able to observe and the sampled probability of each arm has a linear relationship with the reward function. We demonstrate that the linear algorithm is able to outperform a traditional reinforcement learning algorithm in terms of maximizing the victim's symbol error rate. We extend this work by examining the impact of the context feature vector design, LTE/5G-based protocol specifics (such as error correction coding), and imperfect reward feedback information. We gain insights into the convergence time of the jammer and its ability to jam the victim, as well as improvements to the algorithm, and insights into the vulnerabilities of wireless communications for reinforcement learning based jamming.

yes / This paper describes the characterization and design of energy efficient user terminal transceiver power amplifier. To reduce the design of bulky external circuitry, the load modulation technique is employed. The design core is based on the combination of Class B and Class C that includes quarter wavelength transformer at the output to perform the load modulation. The handset transceiver for this power amplifier is designed to operate over the frequency range of 3.4GHz to 3.6GHz mobile WiMAX band. The performances of the load modulation amplifier are compared with conventional Class B amplifier. The results of 30dBm output power and 53% power added efficiency are achieved.

Yes / The biggest challenge for future 4G systems is the need to limit the energy consumptions of battery-powered and base station devices, with the aim to prolong their operational time and avoid active cooling in the base station. The green wireless communications requires research in areas such as energy efficient RF front end, MAC protocol, networking, deployment, operation, and also the integration of base station with renewable power supply. In this paper, the design concept of energy efficient RF front end is considered in terms of RF power amplifiers at which it represents the workhorse of modern wireless communication systems and inherently nonlinear. The approach of output power back off is to amplify the signal at the linear region to avoid distortion, but this approach suffers from significant reduction in efficiency and power output. To boost the efficiency at wide range of output power and keep the same margin for signal with high crest factor, the load modulation technique with new offset line are employed to operate over the frequency range of 3.4GHz to 3.6GHz band. The performances of load modulation power amplifier are compared with balanced amplifier. The results of 42dBm output power and 62% power added efficiency are achieved.

Unmanned Aerial Vehicles (UAV) are expected to be deployed both by government and industry. Rules for integrating commercial UAVs into a nation's airspace still need to be defined, safety being the main concern. As part of this thesis, the communication needs of UAVs as important requirement for UAV integration into the national airspace is considered. Motivated by recent prediction of UAV quantities, revealing the importance of Micro UAVs (MAV) and Small UAVs (SUAV), the thesis determines spectral requirements for control and non-payload communication (CNPC). We show that spectral efficiency, particularly in the downlink, is critical to the large-scale deployment of UAVs. Due to the limited range of small SUAV and MAV systems, communication between air and ground elements of these UAVs is established through radio Line-of-Sight (LoS) links. Ultimately, efficient LoS UAV systems are based on a better understanding of channels in the downlink, i.e. air-to-ground (A2G) channels, and also on efficient waveform as well as spectrum management implementation. Because of limited research in wideband aeronautical channel modeling, we have derived an A2G channel prototype applicable to SUAV and MAV. As part of the research at Wire- less@VT in designing and prototyping Orthogonal Frequency Division Multiplexing (OFDM) waveforms, this thesis derives the optimal parameters for SUAV and MAV A2G channels. Finally, the thesis discusses concepts that relate flight route with spectrum management as well as opportunities for a more dynamic spectrum allocation for UAV communication systems. / Master of Science