Memory optimization of MAP turbo decoder algorithms

Turbo codes are the most recent breakthrough in coding theory. However, the decoder's implementation cost limits their incorporation in commercial systems. Although the decoding algorithm is highly data dominated, no true memory optimization study has been performed yet. We have extensively and systematically investigated different memory optimizations for the maximum a posteriori (MAP) class of decoding algorithms. It turns out that it is not possible to present one decoder structure as being optimal. In fact, there are several tradeoffs, which depend on the specific turbo code, the implementation target (hardware or software), and the selected cost function. We therefore end up with a parametric family of new optimized algorithms out of which the designer can choose. The impact of our optimizations is illustrated by a representative example, which shows a significant decrease in both decoding energy (factor 2.5) and delay (factor 1.7)     

Adaptive link layer strategies for energy efficient wireless networking

Low power consumption is a key design metric for portable wireless network devices where battery energy is a limited resource. The resultant energy efficient design problem can be addressed at various levels of system design, and indeed much research has been done for hardware power optimization and power management within a wireless device. However, with the increasing trend towards thin client type wireless devices that rely more and more on network based services, a high fraction of power consumption is being accounted for by the transport of packet data over wireless links [28]. This offers an opportunity to optimize for low power in higher layer network protocols responsible for data communication among multiple wireless devices. Consider the data link protocols that transport bits across the wireless link. While traditionally designed around the conventional metrics of throughput and latency, a proper design offers many opportunities for optimizing the metric most relevant to battery operated devices: the amount of battery energy consumed per useful user level bit transmitted across the wireless link. This includes energy spent in the physical radio transmission process, as well as in computation such as signal processing and error coding. This paper describes how energy efficiency in the wireless data link can be enhanced via adaptive frame length control in concert with adaptive error control based on hybrid FEC (forward error correction) and ARQ (automatic repeat request). Key to this approach is a high degree of adaptivity. The length and error coding of the atomic data unit (frame) going over the air, and the retransmission protocol are (a) selected for each application stream (ATM virtual circuit or IP/RSVP flow) based on quality of service (QoS) requirements, and (b) continually adapted as a function of varying radio channel conditions due to fading and other impairments. We present analysis and simulation results on the battery energy efficiency achieved for user traffic of different QoS requirements, and describe hardware and software implementations.     

Model-Based Techniques for Data Reliability in Wireless Sensor Networks

Wireless Sensor Networks are a fast-growing class of systems. They offer many new design challenges, due to stringent requirements like tight energy budgets, low-cost components, limited processing resources, and small footprint devices. Such strict design goals call for technologies like nanometer-scale semiconductor design and low-power wireless communication to be used. But using them would also make the sensor data more vulnerable to errors, within both the sensor nodes' hardware and the wireless communication links. Assuring the reliability of the data is going to be one of the major design challenges of future sensor networks. Traditional methods for reliability cannot always be used, because they introduce overheads at different levels, from hardware complexity to amount of data transmitted. This paper presents a new method that makes use of the properties of sensor data to enable reliable data collection. The approach consists of creating predictive models based on the temporal correlation in the data and using them for real-time error correction. This method handles multiple sources of errors together without imposing additional complexity or resource overhead at the sensor nodes. We demonstrate the ability to correct transient errors arising in sensor node hardware and wireless communication channels through simulation results on real sensor data.