Mehnaz Rahman

An iterative LR-aided MMSE extended soft MIMO decoding algorithm

This paper presents an iterative soft decision based lattice reduction (LR) aided Schnorr-Euchner (SE) multiple-input-multiple-output (MIMO) decoding algorithm, which reduces the gap in performance between suboptimal K-best and maximum likelihood (ML) detectors. Following IEEE 802.16e standard, we develop an iterative soft decoding algorithm for 4×4 MIMO with different modulation schemes. Using this method, we obtain 1.1 to 2.7 dB improvement over iterative soft decision based least sphere decoding (LSD) for different iterations. Then, using extensive simulation, we determine the optimum values for list size and saturation limit, which are the two governing parameters of our algorithm. Finally, we demonstrate that limiting the log likelihood ratio (LLR) values in LR-aided and LSD algorithm results in more than 8x reduction in list size as well as in the complexity of detectors and LLR calculation units.

Asynchronous baseband processor design for cooperative MIMO satellite communication

The challenges in satellite communication (SatCom) include but not limited to the customary complications of telecommunication such as channel condition, signal to noise ratio (SNR), etc. SatCom system is also prone to transient and permanent radiations hazards. Hence, in spite of the harsh environmental factors (weather phenomena, solar events, etc), a SatCom system must maintain reliable and predictable communication functions with limited source of power. This paper presents a SatCom system design for achieving both low-power and high fidelity communication. The design uses cooperative multiple input multiple output (MIMO) for spectral efficiency and diversity, low-density parity-check (LDPC) decoding for near Shannon-limit gain, and dynamic voltage and frequency scaling (DVFS)-assisted asynchronous circuit designs to achieve low-power and fault tolerance. The MIMO system permits uninterrupted service in the event of temporary/permanent link or unit failures. The results show that the resilience against injected radiation levels of upto about 25 fempto-Coulombs on critical path is achieved. This is more than 600 times the minimum charge required to logically flip a gate output in ordinary static CMOS gate.

An iterative soft decision based adaptive K-best decoder without SNR estimation

This paper presents an iterative soft decision based adaptive K-best multiple-input-multiple-output (MIMO) decoding algorithm. It has the flexibility of changing the list size, K with respect to the channel condition, although the accurate measurement of signal to noise ratio (SNR) is not required. Moreover, the concept of iterative soft decision based lattice reduction (LR)-aided minimum mean square error (MMSE) extended K-best decoder is applied instead of conventional hard decision based K-best algorithm to reduce computational complexity to a great extent It is found that the ratio of the minimum path metric to the second minimum can provide reliable estimation of channel condition. Hence, in the proposed algorithm, K is changed adaptively with respect to the ratio. Using this method with less number of K, we can obtain similar performance compared to the conventional LR-aided K-best algorithm operating with maximum list size of 64. Comparing to the fourth iteration of iterative soft decision based least sphere decoding (LSD), the proposed method with less K achieves 1.6 dB improvement at the bit error rate (BER) of 10-6. Therefore, similar performance can be obtained by the proposed adaptive K-best algorithm with less computational complexity of the tree search decoder.

Signal reconstruction processor design for compressive sensing

This paper presents a very-large-scale integration (VLSI) design to reconstruct compressively sensed data. The proposed digital design recovers signal compressed by specific analog-to-digital converter (ADC). Our design is based on a modified iterative hard threshold (IHT) reconstruction algorithm to adapt unknown and varying degree of sparsity of the signal. The algorithm is composed empirically and implemented in a hardware-friendly fashion. The reconstruction fidelity using fixed-point hardware model is analyzed. The design is synthesized using Synopsys Design Compiler with TSMC 45nm standard cell library. The post-synthesis implementation consumes 165 mW and is able to reconstruct data with information sparsity of 4%, at equivalent sampling rate of 1 gigasample-per-second (GSPS).

Complex Domain Iterative K-Best Decoder

This chapter presents an iterative soft decision-based complex K-Best decoder, which enables the utility of lattice reduction and complex SE enumeration in MIMO decoder [15]. For complex domain detection, the tree search does not need to be expanded twice the height for the mapping to real domain. This inherently saves complexity and required calculation. However, node calculation with complex value became challenging in terms of algorithmic and hardware implementation.

Fixed Point Realization of Iterative K-Best Decoder

This chapter includes a novel study on fixed point realization of iterative LR-aided K-Best decoder based on simulation [18]. It is a required step to decide on the hardware implementation. The process involves two steps: first is to select optimized architecture for each sub-module of K-Best decoder, and the second is to perform the fixed point conversion. The choice of proper architecture makes the hardware implementation easier, while the fixed point conversion minimizes the bit length of each variable. These objectives gradually lead to the minimization of hardware cost, power, and area as well.

Adaptive Real Domain Iterative K-Best Decoder

The chapter begins with a description of soft decision-based iterative LR-aided adaptive K-Best MIMO decoder [19]. All the detectors mentioned above have fixed use of K. Hence, an adaptive K-Best MIMO detector is proposed to include more adaptability and re-configurability. The proposed method has several advantages over adaptive conventional K-Best scheme for MIMO system.

Real Domain Iterative K-Best Detector

The chapter begins with a description of K-Best detector for real domain. As shown in ( 2.3), the real domain tree search is twice as deep resulting in larger latency in terms of hardware implementation. Hence, for the real domain, the number of the possible children of a node is twice that of the complex domain. In this chapter, we present the proposed soft decision-based iterative LR-aided K-Best MIMO decoder [13, 14].