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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.

Orthogonal Frequency Division Multiplexing (OFDM) is fast gaining ground as a preferred modulation technique for short range wireless data application such as 802.11a/g, 802.15.3a and 802.16. Recently, use of multiple transmit and receive antenna for improving spectral efficiency in a wireless system has received much interest. IEEE 802.11 has set up the Work Group 802.11n to develop a standard for enhanced rate 802.11 based on OFDM using Multi Input/Multiple Output (MIMO) techniques. The most dominant proposal is the use of singular value decomposition based MIMO methods to achieve the high data rate. The selection of modulation and coding rates plays a significant role in the overall throughput of the system, more so in cases where the traffic between the transmitter and the receiver consists of short bursts and the user location is not fixed. The performance of a given modulation and coding technique depends on the channel condition. Closed form or bounding solutions exists for various modulation and coding techniques. But these techniques are not suitable for real time application where the channel is dynamic. The approach taken in this thesis is to decouple frequency selective MIMO OFDM channel into orthogonal spatial and frequency domains channels using Fast Fourier Transforms and Singular Value Decomposition. The channels can be viewed as parallel flat fading channels for which the expected BER rate can be computed. A SNR-BER table is used to efficiently compute the performance efficiently. An effective SNR is computed using the table and compared with rate threshold to select a suitable rate. Improvements of 15 dB and above are shown the link budget while using a four transmit four receive MIMO system. Proposed 802.11n TGn Sync physical layer standard is used to evaluate the performance. The performance in case of one of the systems being a legacy 802.11a/g nodes is also looked into. Gains up to 7 dB are shown in the link budget. / Master of Science

Orthogonal Frequency Division Multiplexing (OFDM) has recently gained a lot of attention and is a potential candidate for Fourth Generation (4G) wireless systems because it promises data rates up to 10Mbps. A variation of OFDM is Multi-Carrier CDMA (MC-CDMA) which is an OFDM technique where the individual data symbols are spread using a spreading code in the frequency domain. The spreading code associated with MC-CDMA provides multiple access technique as well as interference suppression. Often times in cellular and military environments the desired signal can be buried below interference. In such conditions, the processing gain associated with the spreading cannot provide the needed interference suppression. This research work investigates multi-antenna receivers for OFDM and MC-CDMA systems; specifically this works investigates adaptive antenna algorithms for MC-CDMA for very different channel conditions. Frequency domain beamforming is studied in this research predominantly through simulation. As an alternative a time domain beamforming is also studied. Time variations in the channel can disrupt the orthogonality between subcarriers. Minimum Mean Square Error (MMSE) detection coupled with MMSE beamforming is proposed for time varying channels. Semi-analytic results are derived to study the Bit Error Rate (BER) performance. These results show significant performance improvement in the presence of interference. Joint MMSE weights in space and frequency is also investigated and semi-analytic results are derived to study their BER performance. / Master of Science

En esta tesis doctoral se plantea la mejora de la capacidad de transmisión en las redes HDR-WPAN ( redes WPAN con elevada tasa binaria) empleando el estándar MB-OFDM (OFDM sobre múltiples bandas) en canales UWB con modulación adaptativa realizando una adaptación discreta de la tasa de bits transmitidos por subportadora. La tesis comienza con un análisis en el dominio del tiempo y de la frecuencia de los dos modelos de canal UWB propuestos en IEEE: IEEE 802.15.3a y 802.15.4a. El objetivo consiste en determinar la distribución estadística que mejor se aproxima a la amplitud de cada una de las subportadoras, para posteriormente definir la métrica a emplear en la evaluación del estado y dinámica del canal UWB. En la modulación adaptativa se requiere que el transmisor se adapte a la variabilidad del canal. El análisis se puede realizar de dos formas: - Asumiendo adaptación perfecta (estimación ideal), lo que implica que el transmisor siempre conoce la dinámica y estructura del canal. - Considerando un error (estimación imperfecta del canal) en la adaptación debido a la incertidumbre en el conocimiento del canal. El método de estimación del canal UWB empleado en la tesis se fundamenta en el conocimiento del coeficiente de correlación en potencia entre las subportadoras de datos y la subportadora piloto. A partir de la información sobre el estado del canal, u una vez definida la métrica que permite su evaluación, se calculan las prestaciones de la modulación adaptativa. Esta evaluación se realiza a través de expresiones cerradas para la capacidad media, la probabilidad de error de bit media y la probabilidad de bloqueo, así como la obtención de la distribución y estadísticos del error de estimación en el caso de estimación imperfecta del canal. Por otro lado, a partir de la distribución estadística de la amplitud de cada una de las subportadoras en frecuencia del canal UWB, se obtienen resultados respecto a la variación de potencia del canal en función del ancho de banda . / Llano Ramírez, G. (2010). Modelado en frecuencia del canal UWB y su aplicación en el análisis de técnicas de modulación adaptativa en sistemas MB-OFDM UWB para redes WPAN [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8429

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.