Enabling next-generation wireless networks with custom and commodity PHY designs

Many IoT applications have stringent requirements on wireless transmission delay but must compete for channel access. Cross-technology coexistence is an approach to reducing the wireless transmission latency from the collision of different standards, especially the Internet of Things. Moreover, wireless interference mitigation is another key aspect to improve network quality for the next-generation wireless networks. This dissertation has three aspects to developing next-generation networks. First, we design EmBee, a new wireless PHY technique that enables cross-technology coexistence at no performance loss to these extremely weak wireless devices. EmBee adaptively reserves occupied spectrum from the strong devices for weak wireless devices’ concurrent data transmissions. Experiment results show that EmBee can effectively support ZigBee transmissions over a fully occupied WiFi channel without causing any extra delay, while only resulting in 10% WiFi throughput loss. Second, we develop TransFi, a new software technique that transforms commodity WiFi into custom wireless PHY via fine-grained emulation. To perform such emulation, TransFi considers the target signal as a mixture of QAM constellation points on the complex plane, and reversely computes the MAC payload of each MIMO stream from one selected QAM constellation point for mixing the transmitted signals from these streams in the air. We implemented TransFi on commodity WiFi devices to emulate three custom wireless PHYs with diverse characteristics. Experiment results show that TransFi’s accuracy of emulation is >90% when transmitting emulated data payloads at 11.4 Mbps, and data decoding errors at this transmission rate are <1%. Third, we present AiFi, a new interference cancellation technique on WiFi devices without extra RF hardware. The key idea of AiFi is to retrieve knowledge about interference from the locally available physical-layer (PHY) information at the WiFi receiver, including the pilot information (PI) and channel state information (CSI). AiFi leverages the power of AI to estimate interference from PHY information, and incorporate the domain knowledge about WiFi PHY to minimize the neural network complexity. Experiment results show that AiFi can correct 80% bit errors due to interference and improve the MAC frame reception rate by 18x, with