We describe properties and constructions of constraint-based codes for DNA-based data storage which account for the maximum repetition length and AT/GC balance. We present algorithms for computing the number of sequences with maximum repetition length and AT/GC balance constraint. We describe routines for translating binary runlength limited and/or balanced strings into DNA strands, and compute the efficiency of such routines. We show that the implementation of AT/GC-balanced codes is straightforward accomplished with binary balanced codes. We present codes that account for both the maximum repetition length and AT/GC balance. We compute the redundancy difference between the binary and a fully fledged quaternary approach.

The multi-level-cell (MLC) NAND flash channel exhibits non-stationary behavior over increasing program and erase (PE) cycles and data retention time. In this paper, an optimization scheme for adjusting the read (quantized) and write (verify) voltage levels to adapt to the non-stationary flash channel is presented. Using a model-based approach to represent the flash channel, incorporating the programming noise, random telegraph noise (RTN), data retention noise and cell-to-cell interference as major signal degradation components, the writevoltage levels are optimized by minimizing the channel error probability. Moreover, for selecting the quantization levels for the read-voltage to facilitate soft LDPC decoding, an entropybased function is introduced by which the voltage erasure regions (error dominating regions) are controlled to produce the lowest bit/frame error probability. The proposed write and read voltage optimization schemes not only minimize the error probability throughout the operational lifetime of flash memory, but also improve the decoding convergence speed. Finally, to minimize the number of read-voltage quantization levels while ensuring LDPC decoder convergence, the extrinsic information transfer (EXIT) analysis is performed over the MLC flash channel.

A severe problem for mutual informationmaximizing lookup table (MIM-LUT) decoding of low-density parity-check (LDPC) code is the high memory cost for using large tables, while decomposing large tables to small tables deteriorates decoding error performance. In this paper, we propose a method, called mutual information-maximizing quantized belief propagation (MIM-QBP) decoding, to remove the lookup tables used for MIM-LUT decoding. Our method leads to a very practical decoder, namely the MIM-QBP decoder, which can be implemented based only on simple mappings and fixed-point additions. We further present how to practically and systematically design the MIM-QBP decoder for both regular and irregular LDPC codes. Simulation results show that the MIM-QBP decoder can always considerably outperform the state-of-the-art MIM-LUT decoder. Furthermore, the MIM-QBP decoder with only 3 bits per message can outperform the floating-point belief propagation (BP) decoder at high signal-tonoise ratio (SNR) regions when testing on high-rate codes with a maximum of 10-30 iterations.Index Terms-Finite alphabet iterative decoding (FAID), lookup table (LUT), low-density parity-check (LDPC) code, mutual information (MI), quantized belief propagation (QBP).

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Mobile edge computing (MEC) provides computational services at the edge of networks by offloading tasks from user equipments (UEs). This letter employs an unmanned aerial vehicle (UAV) as the edge computing server to execute offloaded tasks from the ground UEs. We jointly optimize user association, UAV trajectory, and uploading power of each UE to maximize sum bits offloaded from all UEs to the UAV, subject to energy constraint of the UAV and quality of service (QoS) of each UE. To address the non-convex optimization problem, we first decompose it into three subproblems that are solved with integer programming and successive convex optimization methods respectively. Then, we tackle the overall problem by the multi-variable iterative optimization algorithm. Simulations show that the proposed algorithm can achieve a better performance than other baseline schemes.