In this thesis, the superimposed training scheme is applied to the orthogonal frequency division multiplexing (OFDM) systems. To optimize power amplifier efficiency, the optimal value of pilot power allocation is derived to maximize data power. Peak-to-average power ratio (PAPR) is one of the major drawbacks for OFDM systems. A large PAPR will push the power amplifier into the saturation area, leading to nonlinear distortion, and reduce power efficiency. By using the superimposed training schemes, the system can not only improve the bandwidth efficiency, but also reduce the PAPR. In this thesis, the relation between PAPR and power efficiency is analyzed. In addition, the close form formula is derived for determining the pilot power allocation so that data power is maximized.

Growing request for wideband communications requires innovation in power efficiency and signal processing. Without the use of any peak to average power ratio (PAPR) reduction technique, the efficiency of power consumption at the transmitter end becomes very poor. PAPR reduction in this work is accomplished based on using a unique class of functions, prolate spheroidal wave functions (PSWFs). The difficulty arises from the fact that these pulses do not belong to the Nyquist family. A zero forcing equalizer (ZFE) is designed to compensate intersymbol interference (ISI), and its performance is studied under the presence of AWGN. Considering PAPR and ISI as the constraints of communication systems, based on the properties of PSWF, a set of pulses with minimum ISI with respect to a specific amount of PAPR is achieved by defining an optimization problem. The desired level of PAPR is considered to be moved to the constraint set to convert the multi-objective problem into a single objective problem. The results of the numerical optimization of both ISI and PAPR are presented along with a couple of examples of comparison between the resultant pulse and the conventional square root raised cosine. It is shown that by achieving the same level of PAPR of the SRRC, the obtained pulse is a close approximation of SRRC. An implementation based on state variable filters is introduced to realize PSWF for high speed applications. An example based on this approach is presented to compare the finite pole approximation result with the original pulse.

Small cells are low-powered radio access nodes that operate in licensed and unlicensed spectrum that have a range of 10 meters to 200 meters, compared to a mobile macrocell which might have a range of a few kilometres. This dissertation proposes algorithms for the enhancement of small cells installed in high speed rails. The thesis addresses two main points: the link between the small cell and the base station, and the link between the end-users and the small cell. The channel between the small cell and the base station is a fast fading channel due to the mobility of the high speed rail. The first part of the thesis proposes methods to enhance the link between the small cell and the base station using Low-Density Parity-Check codes (LDPC) for fast fading channels. The proposed uses nonuniform reconstruction methods based on the soft output log-likelihood ratio (LLR) provided by the LDPC decoder. The LLRs provide information about the location of the symbols with high probability of being correct. The grid formed under the assumption of a correlated Rayleigh channel affecting the transmitted data is highly nonuniform. Reconstruction of the channel under such assumptions is highly unstable. A signal-to-noise- ratio dependent regularization method is implemented to enhance the performance under imperfect Doppler spread estimation. The second part of the thesis proposes algorithms for the link between the end-user and the small cell. Since power efficiency is a major factor for end-users employing battery powered devices, we propose a Linear Programming (LP) algorithm for signal shaping to minimize the average transmitted power. The other problem the thesis addresses is the minimization of Peak-to-Average Power-Ratio (PAPR) of Orthogonal Frequency Division Multiplexing (OFDM) signals. The PAPR is minimized using a set of phase shifts for the constituting subcarriers of the OFDM signal. The set of phase shifts is determined using a LP approach that minimizes the complexity when the block length is high. A real-time implementation of some of the algorithms is carried out using the TMS320C6713 Texas Instruments board. The results for fixed-point versus floating-point implementation is shown for a different number of precision bits. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2013-04-27 16:54:32.464

The nature of perception and discernment through an examination of hypotheses and theories considered peculiar in their time but which had at least some popular, scientific or social support.

Asim M. Mazin, for the Master of Science degree in Electrical and Computer Engineering, presented on Mar 27, 2013, at Southern Illinois University Carbondale. TITLE: REDUCING THE PEAK TO AVERAGE POWER RATIO OF MIMO-OFDM USING PSO BASED PTS. MAJOR PROFESSOR: Dr. Garth V. Crosby, In this thesis we proposed PSO based PTS to accomplish the lowest Peak-to-Average Power Ratio of MIMO-OFDM system. We applied the PSO based PTS on each antenna of the system in order to find the optimal phase factors which is a straightforward method to get the minimum PAPR in such a system. The performance of PSO based PTS algorithm in MIMO-OFDM with a wide range of phase factor tends to give a high performance according to the simulation results. In addition, there is no need to increase the number of particles of the PSO algorithm to enhance the performance of the system, which keeps the complexity of finding the minimum PAPR reasonable.

A complex array for constructing perfect sequences is presented in this paper. The row sequences and their discrete Fourier transform form two sets of perfect sequences. The column sequences are orthogonal to each other for any cyclic shift. In addition, any combination of the column sequences with complex weighting coefficients of equal amplitude is also a perfect sequence. In addition, a superimposed training scheme is also proposed for channel estimation in OFDM systems. The perfect sequence is adopted since it has a constant magnitude in both the time domain and the frequency domain. Although the derived channel estimator has a slightly worse performance since the unknown data contributes extra noise, the effective data throughput is substantially increased. In addition, the proposed scheme is shown to have a much better peak-to-average power ratio (PAPR) because the added perfect sequence has a constant magnitude in the time domain.

Modern day wireless communication systems are constantly facing increasing bandwidth demands due to a growing consumer base. To cope up with it, they are required to have a better power vs performance from the RF devices. The amount of data being exchanged over wireless links has tremendously increased and simultaneously, there is a need to switch to portable RF devices and this has in turn forced the issue of low-power RF system design. Therefore, what we need is an RF transceiver that operates at high data rates and over adverse channels with a low power consumption. A major portion of the power is utilized by the RF front end of the wireless system. Many methods like controlled positive feedback, re-utilizing bias current, etc have been employed to reduce the power consumption of the RF front end. The most modern wireless systems adapt to the channel quality by adjusting the data transmission rates and by adjusting the output power of the RF Power Amplifier. However, each of these methods concentrates on working for the worst case channel and giving the highest data rate. What needs to be known is that the channel conditions are not always worst. Even for a normal channel, the system is going to utilize a lot of power and give the highest possible data rate which may or may not be necessary. And thus, for the most part, the system is going to use up more power than necessary. What we need instead, is a system which works nominally for a normal channel and exhaustively for a harsh channel condition. This requires the system to adapt to the channel conditions. Also another major factor causing fluctuations in the performance is the process variations. This calls for a channel-dependent dynamic transceiver with adequate power management and tuning. In our work, we try to devise a method to dynamically minimize the power considering the varying channel conditions and process variations. We first use companding to reduce the dynamic range of the signal so that it can be used on facilities with smaller dynamic range. This brings down the transmitted power. We also create multiple instances of the Power Amplifier to simulate process variations. After finding the optimum tuning knob settings for one instance of the PA, we try to use it to obtain the optimum settings for another instance. This requires the use of some heuristics and in our work, we have supplemented it with Simulated Annealing. Using SA, we can dynamically tune the power of a system for changing channel conditions and existing process variations. Towards the end, we have also proved that the slower the cooling rate of the experiment, the more elaborate the search space is and the more accurate the result is.

No description available.

Orthogonal Frequency-Division Multiplexing (OFDM) has been proved to be a promising technology that enables the transmission of higher data rate. Multicarrier Code-Division Multiple Access (MC-CDMA) is a transmission technique which combines the advantages of both OFDM and Code-Division Multiplexing Access (CDMA), so as to allow high transmission rates over severe time-dispersive multi-path channels without the need of a complex receiver implementation. Also MC-CDMA exploits frequency diversity via the different subcarriers, and therefore allows the high code rates systems to achieve good Bit Error Rate (BER) performances. Furthermore, the spreading in the frequency domain makes the time synchronization requirement much lower than traditional direct sequence CDMA schemes. There are still some problems when we use MC-CDMA. One is the high Peak-to-Average Power Ratio (PAPR) of the transmit signal. High PAPR leads to nonlinear distortion of the amplifier and results in inter-carrier self-interference plus out-of-band radiation. On the other hand, suppressing the Multiple Access Interference (MAI) is another crucial problem in the MC-CDMA system. Imperfect cross-correlation characteristics of the spreading codes and the multipath fading destroy the orthogonality among the users, and then cause MAI, which produces serious BER degradation in the system. Moreover, in uplink system the received signals at a base station are always asynchronous. This also destroys the orthogonality among the users, and hence, generates MAI which degrades the system performance. Besides those two problems, the interference should always be considered seriously for any communication system. In this dissertation, we design a novel MC-CDMA system, which has low PAPR and mitigated MAI. The new Semi-blind channel estimation and multi-user data detection based on Parallel Interference Cancellation (PIC) have been applied in the system. The Low Density Parity Codes (LDPC) has also been introduced into the system to improve the performance. Different interference models are analyzed in multi-carrier communication systems and then the effective interference suppression for MC-CDMA systems is employed in this dissertation. The experimental results indicate that our system not only significantly reduces the PAPR and MAI but also effectively suppresses the outside interference with low complexity. Finally, we present a practical cognitive application of the proposed system over the software defined radio platform.

This research addresses green communication issues, including energy efficiency, peak-to-average power ratio (PAPR) reduction and power amplifier (PA) linearization. Green communication is expected to be a primary goal in next generation cellular systems because it promises to reduce operating costs. The first key issue is energy efficiency of distributed antenna systems (DASs). The power consumption of high power amplifiers (HPAs) used in wireless communication systems is determined by the transmit power and drain efficiency. For unequal power allocation of orthogonal frequency division multiplexing (OFDM), the drain efficiency of the PA is determined by the PAPR and hence by the power distribution. This research proposes a PAPR-aware energy-efficient resource allocation scheme for joint orthogonal frequency division multiple access (OFDMA)/space division multiple access (SDMA) downlink transmission from DASs. Grouping-based SDMA is applied to exploit the spatial diversity while avoiding performance degradation from correlated channels. The developed scheme considers the impact of both system data rate and effective power consumption on the PAPR during resource allocation. We also present a suboptimal joint subcarrier and power allocation algorithm to facilitate implementation of power-efficient multi-channel wireless communications. By solving Karush-Kuhn-Tucker conditions, a closed-form solution for the power allocation of each remote radio head is obtained. The second key issue is related with PAPR reduction in the massive multiple-input multiple-output (MIMO) systems. The large number of PAs in next generation massive MIMO cellular communication system requires using inexpensive PAs at the base station to keep array cost reasonable. Large-scale multiuser (MU) MIMO systems can provide extra spatial degrees-of-freedom (DoFs) for PAPR reduction. This work applies both recurrent neural network (RNN)- and semidefinite relaxation (SDR)-based schemes for different purposes to reduce PAPR. The highly parallel structure of RNN is proposed in this work to address the issues of scalability and stringent requirements on computational times in PAPR-aware precoding problem. An SDR-based framework is proposed to reduce PAPR that accommodates channel uncertainties and intercell coordination. Both of the proposed structures reduce linearity requirements and enable the use of lower cost RF components for large-scale MU-MIMO-OFDM downlink. The third key issue is digital predistortion (DPD) in the massive MIMO systems. The primary source of nonlinear distortion in wireless transmitters is the PA, which is commonly modeled using polynomials. Conventional DPD schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is impractical for scaling to tens or hundreds of PAs in massive MIMO systems. This work therefore proposes a scalable DPD method, achieved by exploiting massive DoFs of next generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero-forcing precoding and DPD. Experimental results show that over 70% of computational complexity is saved for the proposed solution, it is shown that a 3rd order polynomial with the new solution achieves the same performance as the conventional DPD using 11th order polynomial for a 100x10 massive MIMO configuration. / Ph. D. / The global climate change has emerged as a critical issue over the last decades. The increasing popularity of wireless communication networks, has resulted in information and communication technology becoming a non-negligible contributor to the overall carbon footprint. The increasing number of base stations and remote radio heads leads to higher operating expenditure mainly because of the higher energy consumption. This growth can be attributed not only to the increase in the number of smart devices in emerging economies, but also to the growth of shared multimedia data and online games. The wireless industry needs significant improvements in the energy efficiency of base stations and other network infrastructure to compensate for the increased energy demands from the network growth. Therefore, designing energy-efficient communication systems has become a critical issue for 5G, which promises massive deployment of smart devices served new infrastructure elements. In this dissertation, we primarily investigate the theoretical foundations and practical algorithms for the next generation wireless technologies, and discuss the impact of ongoing trends in cellular communications, such as shrinking cell sizes and multi-antenna system deployments, on energy-efficient 5G networks. The theoretical development and wireless algorithms are valuable for the deployment of next generation wireless network systems