Steven Hong

Applications of self-interference cancellation in 5G and beyond

Self-interference cancellation invalidates a long-held fundamental assumption in wireless network design that radios can only operate in half duplex mode on the same channel. Beyond enabling true in-band full duplex, which effectively doubles spectral efficiency, self-interference cancellation tremendously simplifies spectrum management. Not only does it render entire ecosystems like TD-LTE obsolete, it enables future networks to leverage fragmented spectrum, a pressing global issue that will continue to worsen in 5G networks. Self-interference cancellation offers the potential to complement and sustain the evolution of 5G technologies toward denser heterogeneous networks and can be utilized in wireless communication systems in multiple ways, including increased link capacity, spectrum virtualization, any-division duplexing (ADD), novel relay solutions, and enhanced interference coordination. By virtue of its fundamental nature, self-interference cancellation will have a tremendous impact on 5G networks and beyond.

Picasso: flexible RF and spectrum slicing

This paper presents the design, implementation and evaluation of Picasso, a novel radio design that allows simultaneous transmission and reception on separate and arbitrary spectrum fragments using a single RF frontend and antenna. Picasso leverages this capability to flexibly partition fragmented spectrum into multiple slices that share the RF frontend and antenna, yet operate concurrent and independent PHY/MAC protocols. We show how this capability provides a general and clean abstraction to exploit fragmented spectrum in WiFi networks, handle coexistence in dense deployments as well as many other applications. We prototype Picasso, and demonstrate experimentally that a Picasso radio partitioned into four slices, each concurrently operating four standard WiFi OFDM PHY and CSMA MAC stacks, can achieve the same sum throughput as four physically separate radios individually configured to operate on the spectrum fragments. We also demonstrate experimentally how Picassos slicing abstraction provides a clean mechanism to enable multiple diverse networks to coexist and achieve higher throughput, better video quality and latency than the best known state of the art approaches.

DOF: a local wireless information plane

The ability to detect what unlicensed radios are operating in a neigh borhood, their spectrum occupancies and the spatial directions their signals are traversing is a fundamental primitive needed by many applications, ranging from smart radios to coexistence to network management to security. In this paper we present DOF, a detector that in a single framework accurately estimates all three parameters. DOF builds on the insight that in most wireless protocols, there are hidden repeating patterns in the signals that can be used to construct unique signatures, and accurately estimate signal types and their spectral and spatial parameters. We show via experimental evaluation in an indoor testbed that DOF is robust and accurate, it achieves greater than 85% accuracy even when the SNRs of the detected signals are as low as 0 dB, and even when there are multiple interfering signals present. To demonstrate the benefits of DOF, we design and implement a preliminary prototype of a smart radio that operates on top of DOF, and show experimentally that it provides a 80% increase in throughput over Jello, the best known prior implementation, while causing less than 10% performance drop for co-existing WiFi and Zigbee radios.The ability to detect what unlicensed radios are operating in a neigh borhood, their spectrum occupancies and the spatial directions their signals are traversing is a fundamental primitive needed by many applications, ranging from smart radios to coexistence to network management to security. In this paper we present DOF, a detector that in a single framework accurately estimates all three parameters. DOF builds on the insight that in most wireless protocols, there are hidden repeating patterns in the signals that can be used to construct unique signatures, and accurately estimate signal types and their spectral and spatial parameters. We show via experimental evaluation in an indoor testbed that DOF is robust and accurate, it achieves greater than 85% accuracy even when the SNRs of the detected signals are as low as 0 dB, and even when there are multiple interfering signals present. To demonstrate the benefits of DOF, we design and implement a preliminary prototype of a smart radio that operates on top of DOF, and show experimentally that it provides a 80% increase in throughput over Jello, the best known prior implementation, while causing less than 10% performance drop for co-existing WiFi and Zigbee radios.

Multi-Resolution Bayesian Compressive Sensing for Cognitive Radio Primary User Detection

Current Cognitive Radios are limited in their operational bandwidth by existing hardware devices, much of the extensive theoretical work on spectrum sensing is impossible to realize in practice over a wide frequency band. To alleviate the sampling bottleneck, some researchers have begun to use a technique called Compressive Sensing (CS), which allows for the acquisition of sparse signals at sub-Nyquist rates, in conjunction with CRs. These researchers have sequentially combined the two techniques: first acquiring compressed samples, then reconstructing the Nyquist rate signal, and lastly performing CR spectrum sensing on the reconstructed signal. While CS alleviates the bandwidth constraints imposed by front-end ADCs, the resulting increase in computation/complexity is non-trivial, especially in a power-constrained mobile CR. In addition, the computation time of the signal reconstruction introduces significant delay into the spectrum sensing operation. This motivates us to look at different ways to reduce computational complexity while achieving the same goals. In this paper, we will demonstrate how utilizing a Bayesian Compressive Sensing (BCS) framework can achieve the sampling reduction advantage of Compressive Sensing with significantly less computational complexity. Our key observation is that the CR does not have to reconstruct the entire signal because it is only interested in detecting the presence of Primary Users. Our BCS PU detection algorithm takes advantage of this observation by estimating signal parameters directly from the compressed signal, thereby eliminating the reconstruction stage and reducing the computational complexity. In addition, the BCS framework provides a measure of the quality of estimation allowing the system to optimize its data acquisition process to always acquire the minimum number of compressed measurements, even in a dynamic spectral environment.

Beyond full duplex wireless

Recent work has shown the possibility of implementing full-duplex wireless radios using commodity hardware. We discuss the possibility of extending full-duplex designs to support multiple input, multiple output (MIMO) systems. We explore how such a design could lead to a rethinking of wireless networks. We discuss various applications of full-duplex radios and the gains possible with those applications. We also discuss some of the challenges present in getting such radios and their applications to be a part of production networks.

Direct spectrum sensing from compressed measurements

Because current Cognitive Radios are limited in their operational bandwidth by existing hardware devices, much of the extensive theoretical work on spectrum sensing is impossible to realize in practice over a wide frequency band. To solve this problem, many have used Compressive Sensing (CS) in sequence with CRs: first acquiring compressed samples, then reconstructing the Nyquist Rate signal, and lastly performing spectrum sensing on the reconstructed signal. While CS alleviates the bandwidth constraints imposed by front-end ADCs, the resulting increase in computation/complexity is non-trivial, especially in a power-constrained mobile CR. This motivates us to look at different ways to reduce computational complexity while achieving the same goals. In this paper, we will demonstrate how directly performing spectrum sensing from the compressed measurements can achieve the sampling reduction advantage of Compressive Sensing with significantly less computational complexity. Our key observation is that the CR does not have to reconstruct the entire signal because it is only interested in detecting the presence of Primary Users. Our algorithm takes advantage of this observation by estimating signal parameters directly from the compressed signal, thereby eliminating the reconstruction stage and reducing the computational complexity. In addition, our framework provides a measure of the quality of estimation allowing the system to optimize its data acquisition process to always acquire the minimum number of compressed measurements, even in a dynamic spectral environment.

Intercarrier Interference Immune Single Carrier OFDM via Magnitude-Keyed Modulation for High Speed Aerial Vehicle Communication

Orthogonal Frequency Division Multiplexing (OFDM) has been considered as a strong candidate for next generation wireless communication systems. Compared to traditional OFDM, Single Carrier OFDM (SC-OFDM) has demonstrated excellent bit error rate (BER) performance, as well as low peak to average power ratio (PAPR). Similar to other multi-carrier transmission technologies, SC-OFDM suffers significant performance degradation resulting from intercarrier interference (ICI) in high mobility environments. Existing techniques for OFDM can be directly adopted in SC-OFDM to improve performance, however, this improved performance comes at costs such as decreased throughput. In this paper, we analyze the effect of ICI on an SC-OFDM system and propose a novel modulation scheme. The proposed Magnitude-Keyed Modulation (MKM) modulation provides SC-OFDM system immunity to ICI and with an easy implementation it significantly outperforms OFDM, SC-OFDM and MC-CDMA systems with Phase Shift Keying (PSK) modulation and Quadrature Amplitude Modulation (QAM) in severe ICI environment. Analysis also illustrates the proposed SC-OFDM system with MKM modulation maintains low PAPR compared to traditional OFDM and SC-OFDM systems with PSK and QAM modulations. Simulation results for different modulation schemes in various ICI environments confirm the effectiveness of the proposed system.

Total Intercarrier Interference Cancellation for OFDM Mobile Communication Systems

For orthogonal frequency division multiplexing (OFDM) communication systems, the orthogonality among subcarriers is lost in mobile radio channels due to the frequency offsets caused by mobility. As a direct result, intercarrier interference (ICI) is observed on each and every subcarrier, leading to significant performance degradation. Many ICI cancellation methods such as windowing and frequency domain coding have been proposed in the literature to cancel ICI and improve the BER performance of OFDM in mobile channel. However, the performance improvement achieved by all the existing ICI cancellation methods is far from enough: the BER performance after ICI cancellation is still much worse than the BER performance of original OFDM without ICI. Moreover, popular ICI cancellation methods like ICI self-cancellation reduce ICI at the price of lowering the transmission rate and reducing the bandwidth efficiency. Other frequency-domain coding methods do not reduce the data rate, but produce less reduction in ICI as well. In this paper, we propose a novel ICI cancellation scheme which can eliminate the ICI entirely and offer a OFDM mobile system with the same BER performance of a OFDM system without ICI. More importantly, the proposed ICI cancellation scheme, namely Total ICI Cancellation, does not lower the transmission rate or reduce the bandwidth efficiency. Specifically, the Total ICI Cancellation scheme takes advantage of the orthogonality of the ICI matrix and offers perfect ICI cancellation and significant BER improvement at linearly growing cost. Simulation results in AWGN channel and multi-path fading channel confirm the superb performance of the proposed Total ICI Cancellation scheme in the presence of frequency offset or time variations in the channel, outperforming all the existing ICI cancellation methods.

Enhancing Cognitive Radio dynamic spectrum sensing through adaptive learning

Cognitive Radio (CR) networks present a difficult set of challenges due to the fluctuating nature of the available spectrum and wide ranging number of applications, each having different Quality of Service (QoS) requirements. This paper studies the key enabling technologies of Cognitive Radio and makes contributions in two key areas: sensing and learning. We shall first present the software testbed which is developed to implement the Cognitive Radio spectrum sensing system. Next, we derive the mathematical relationship between varying parameters and the QoS and test it on our system to verify the overall performance. Novel learning techniques which determine the statistics of primary user (PU) channel usage over time are proposed to enhance the cognitive radios dynamic spectrum sensing ability. Using our testbed, we shall demonstrate the feasibility of the innovative adaptive learning algorithms and their ability to increase spectrum sensing efficiency and improve performance over time without feedback from the receiver. We will then proceed to the domain where there are multiple non-cooperative cognitive users (secondary users) selfishly applying the learning algorithms to increase their data rate in channels with varying primary user activity. Finally we conclude with discussions about our results and future work.

Picasso: full duplex signal shaping to exploit fragmented spectrum

Wireless spectrum is increasingly fragmented due to the growing proliferation of unlicensed wireless devices and piecemeal licensed spectrum allocations. Current radios are ill-equipped to exploit such fragmented spectrum since they expect large contiguous chunks of spectrum to operate on. In this paper we argue that future radios should provide full duplex signal shaping to the higher layers to systematically exploit fragmented spectrum. Such an architectural design would allow the radio to decouple the use of different spcetrum fragments. We present the design and implementation of Picasso, a system that provides such a general signal shaping abstraction. Picasso has two novel components: a self-interference cancellation technique and a programmable filter engine that enables it to simultaneously send and receive over different spectrum fragments. We provide an initial design and empirically evaluate the feasibility of both components.