Linear Precoding with Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink

We consider the downlink of a massive multiuser (MU) multiple-input multiple-output (MIMO) system in which the base station (BS) is equipped with low-resolution digital-to-analog converters (DACs). In contrast to most existing results, we assume that the system operates over a frequency-selective wideband channel and uses orthogonal frequency division multiplexing (OFDM) to simplify equalization at the user equipments (UEs). Furthermore, we consider the practically relevant case of oversampling DACs. We theoretically analyze the uncoded bit error rate (BER) performance with linear precoders (e.g., zero forcing) and quadrature phase-shift keying using Bussgang's theorem. We also develop a lower bound on the information-theoretic sum-rate throughput achievable with Gaussian inputs, which can be evaluated in closed form for the case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet accurate, expressions for the distortion caused by low-precision DACs, which can be used to establish lower bounds on the corresponding sum-rate throughput. Our results demonstrate that, for a massive MU-MIMO-OFDM system with a 128-antenna BS serving 16 UEs, only 3--4 DAC bits are required to achieve an uncoded BER of 10^-4 with a negligible performance loss compared to the infinite-resolution case at the cost of additional out-of-band emissions. Furthermore, our results highlight the importance of taking into account the inherent spatial and temporal correlations caused by low-precision DACs

Channel Related Optimization of Wireless Communication Systems

This thesis deals with different optimization problems in the design of wireless communication systems. It is mainly directed to the design of systems based on multicarrier techniques and orthogonal frequency division multiplex, OFDM, but some of the problems apply to single carrier systems as well. The influence of different pilot patterns is analyzed when pilot symbol assisted modulation, PSAM, is used in OFDM systems. It is desirable to decrease the number of required pilot symbols and it is shown that the pilot pattern used plays a major role to enable reliable channel estimates from a small amount of pilot symbols. Rearrangement of the pilot pattern enables a reduction in the number of needed pilot symbols up to a factor 10, still retaining the same bit error performance. The effect of the number of sub-channels used in an OFDM system is analyzed with respect to resulting bit error rate. An analytical expression for the bit error rate on Rayleigh fading channels when interchannel interference, ICI, caused by channel changes during a symbol and energy loss due to the cyclic prefix are regarded. This expression is used to optimize the number of sub-channels, and thereby the sub-channel bandwidth (sub-channel spacing) in the system. It is argued that the system can be optimized neglecting the effect of imperfect channel estimation and on a worst case assumption for the Doppler frequency and signal to noise ratio. The benefits of using pre-compensation (precoding) in wireless time division duplex, TDD, systems are also investigated. The uplink channel estimate is used to compensate the channel impact on the downlink symbols. This enables less complex receiver structures in the mobile terminal since channel equalization is performed in the base station. Three different methods where amplitude and/or phase are adjusted are analyzed in terms of performance limits. Closed-form expressions for the QPSK bit error rate are given assuming a fully known channel. It is shown that pre-compensation is an attractive alternative to differential decoding. Phase-only compensation is preferred at low signal to noise ratios, while at high signal to noise ratios an order of magnitude improvement in the bit error rate can be obtained by including amplitude pre-compensation. All the analyses and optimizations are general and can be applied to any OFDM system

Sensing Integrated DFT-Spread OFDM Waveform and Deep Learning-powered Receiver Design for Terahertz Integrated Sensing and Communication Systems

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the $10^{-3}$ BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise, and multi-target estimation

Design and implementation of an ETSI-SDR OFDM transmitter with power amplifier linearizer

Satellite radio has attained great popularity because of its wide range of geographical coverage and high signal quality as compared to the terrestrial broadcasts. Most Satellite Digital Radio (SDR) based systems favor multi-carrier transmission schemes, especially, orthogonal frequency division multiplexing (OFDM) transmission because of high data transfer rate and spectral efficiency. It is a challenging task to find a suitable platform that supports fast data rates and superior processing capabilities required for the development and deployment of the new SDR standards. Field programmable gate array (FPGA) devices have the potential to become suitable development platform for such standards. Another challenging factor in SDR systems is the distortion of variable envelope signals used in OFDM transmission by the nonlinear RF power amplifiers (PA) used in the base station transmitters. An attractive option is to use a linearizer that would compensate for the nonlinear effects of the PA. In this research, an OFDM transmitter, according to European Telecommunications Standard Institute (ETSI) SDR Technical Specifications 2007-2008, was designed and implemented on a low-cost Xilinx FPGA platform. A weakly nonlinear PA, operating in the L-band SDR frequency (1.450-1.490GHz), was used for signal transmission. An FPGA-based, low-cost, adaptive linearizer was designed and implemented based on the digital predistortion (DPD) reference design from Xilinx, to correct the distortion effects of the PA on the transmitted signal