Low Complexity Wireless Communication Digital Baseband Design

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This thesis addresses two problems in digital baseband design of wireless communication systems, namely, those in Internet of Things (IoT) terminals that support long range communications and those in full-duplex

This thesis addresses two problems in digital baseband design of wireless communication systems, namely, those in Internet of Things (IoT) terminals that support long range communications and those in full-duplex systems that are designed for high spectral efficiency.

IoT terminals for long range communications are typically based on Orthogonal Frequency-Division Multiple Access (OFDMA) and spread spectrum technologies. In order to design an efficient baseband architecture for such terminals, the workload profiles of both systems are analyzed. Since frame detection unit has by far the highest computational load, a simple architecture that uses only a scalar datapath is proposed. To optimize for low energy consumption, application-specific instructions that minimize register accesses and address generation units for streamlined memory access are introduced. Two parameters, namely, correlation window size and threshold value, affect the detection probability, the false alarm probability and hence energy consumption. Next, energy-optimal operation settings for correlation window size and threshold value are derived for different channel conditions. For both good and bad channel conditions, if target signal detection probability is greater than 0.9, the baseband processor has the lowest energy when the frame detection algorithm uses the longest correlation window and the highest threshold value.

A full-duplex system has high spectral efficiency but suffers from self-interference. Part of the interference can be cancelled digitally using equalization techniques. The cancellation performance and computation complexity of the competing equalization algorithms, namely, Least Mean Square (LMS), Normalized LMS (NLMS), Recursive Least Square (RLS) and feedback equalizers based on LMS, NLMS and RLS are analyzed, and a trade-off between performance and complexity established. NLMS linear equalizer is found to be suitable for resource-constrained mobile devices and NLMS decision feedback equalizer is more appropriate for base stations that are not energy constrained.