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Various packet-based simulation tools (e.g., NS-3) have been employed for design, validation, and evaluation of new protocols for WiFi networks since they offer cost efficiency, scalability, and reproducibility. These benefits come, however, at the expense of lack of realism compared to live testbed experiments. This is attributed in a major part to the difficulty of capturing detailed characteristics of channel dynamics, bit-level protocol specification (PHY layer), and application/user behaviors in a high-fidelity manner. The performance gap predicted by simulation and live testbed becomes even more pronounced when one considers a wide diversity of device characteristics and the way each device is used by end users. For example, smartphones generally show worse WiFi performance than other WiFi devices (e.g., laptops and tablets) because smartphones suffer from additional signal loss due to hand-grips and the low antenna gains of their embedded antennas. The goal of this study is to significantly close the gap by incorporating survey-and measurement-based smartphone WiFi characteristics and realistic hand-grip models into traditional WiFi network simulators (NS-3 in this study). The enhanced WiFi simulation tool's performance prediction capability is validated through an comparative study between testbed experiments and simulations.

Random linear network coding (RLNC) is widely employed to enhance the reliability of wireless multicast. In RLNC encoding/decoding, Galois Filed (GF) arithmetic is typically used since all the operations can be performed with symbols of finite bits. Considering the architecture of commercial computers, the complexity of arithmetic operations is constant regardless of the dimension of GF , if  is smaller than 32 and pre-calculated tables are used for multiplication/division. Based on this, we show that the complexity of RLNC inversely proportional to . Considering additional overheads, i.e., the increase of header length and memory usage, we determine the practical value of . We implement RLNC in a commercial computer and evaluate the codec throughput with respect to the type of the tables for multiplication/division and the number of original packets to encode with each other.