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Abstract: An SDN controller is developed for both testbed management and experimentation for the optical x-haul network in the COSMOS testbed providing a service-on-demand and reconfigurable platform for 5G wireless experiments coupled with edge cloud services. 

Abstract: A hybrid machine learning (HML) model combining a-priori and a-posteriori knowledge is implemented and tested, which is shown to reduce the prediction error and training complexity, compared to an analytical or neural network learning model. 

Abstract: We investigate dynamic network resource allocation using software-defined networking optical controller with software-defined radios on the COSMOS testbed. 10 Gb/s capacity, deterministic low latency are maintained through user equipment wireless handover via optical switching. 

This paper focuses on COSMOS ś Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment. The COSMOS testbed is being deployed in West Harlem (New York City) as part of the NSF Platforms for Advanced Wireless Research (PAWR) program. It will enable researchers to explore the technology łsweet spotž of ultra-high bandwidth and ultra-low latency in the most demanding real-world environment. We describe the testbed’s architecture, the design and deployment challenges, and the experience gained during the design and pilot deployment. Specifically, we describe COSMOS’ computing and network architectures, the critical building blocks, and its programmability at different layers. The building blocks include software-defined radios, 28 GHz millimeter-wave phased array modules, optical transport network, core and edge cloud, and control and management software. We describe COSMOS’ deployment phases in a dense urban environment, the research areas that could be studied in the testbed, and specific example experiments. Finally, we discuss our experience with using COSMOS as an educational tool. 

Abstract—Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized Greedy Maximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid-GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughputoptimal. Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of HGMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16–30× better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm. 

Wireless systems which can simultaneously transmit and receive (STAR) are gaining significant academic and commercial interest due to their wide range of applications such as full-duplex (FD) wireless communication and FMCW radar. FD radios, where the transmitter (TX) and the receiver (RX) operate simultaneously at the same frequency, can potentially double the data rate at the physical layer and can provide many other advantages in the higher layers. The antenna interface of an FD radio is typically built using a multi-antenna system, or a single antenna through a bulky magnetic circulator or a lossy reciprocal hybrid. However, recent advances in CMOS-integrated circulators through spatio-temporal conductivity modulation have shown promise and potential to replace traditional bulky magnetic circulators. However, unlike magnetic circulators, CMOS-integrated non-magnetic circulators will introduce some nonlinear distortion and spurious tones arising from their clock circuitry. In this work, we present an FD radio using a highly linear CMOS integrable circulator, a frequency-flat RF canceler, and a USRP software-defined radio (SDR). At TX power level of +15 dBm, the implemented FD radio achieves a self-interference cancellation (SIC) of +55 dB from the circulator and RF canceler in the RF domain, and an overall SIC of +95 dB together with SIC in the digital domain. To analyze the non-linear phenomena of the CMOS circulator, we calculated the link level data-rate gain in an FD system with imperfect SIC and then extended this calculation to count the effect of TX-RX non-linearity of the circulator. In addition, we provide a qualitative discussion on the spurious tone responses of the circulator due to the clocking imperfections and non-linearity. Index Terms—Circulator, CMOS, conductivity modulation, full-duplex, non-reciprocity, self-interference cancellation. 

Abstract: The COSMOS testbed provides an open-access and programmable multi-layer beyond 5G wireless platform built on an advanced optical x-haul network supporting mobile edge cloud base band processing and applications. 

Full-duplex (FD) wireless is an emerging wireless communication paradigm where the transmitter and the receiver operate simultaneously at the same frequency. One major challenge in realizing FD wireless is the interference of the TX signal saturating the receiver, commonly referred to self-interference (SI). Traditionally, self-interference cancellation (SIC) is achieved in the antenna, RF/analog, and digital domains. In the antenna domain, SIC can be achieved using a pair of separate TX and RX antennas, or using a single antenna shared by the TX and RX through a magnetic circulator, which is usually bulky, expensive, and not integrable with CMOS. Recent advances, however, have shown the feasibility of realizing high-performance non-reciprocal circulators in CMOS based on spatio-temporal modulation. In this work, we demonstrate a high power handling FD radio using a USRP SDR which employs SIC (i) at the antenna interface using a watt-level power-handling CMOS integrated, magnetic-free circulator, (ii) in the RF domain using a compact RF canceler, and (iii) in the digital domain. Our prototyped FD radio achieves +95 dB overall SIC at +15dBm TX power level. We analyze the effects of the circulator TX-RX non-linearity on the total achievable SIC. 

Network slicing will allow 5G network operators to o�er a diverse set of services over a shared physical infrastructure. We focus on supporting the operation of the Radio Access Network (RAN) slice broker, which maps slice requirements into allocation of Physical Resource Blocks (PRBs). We �rst develop a new metric, REVA, based on the number of PRBs available to a single Very Active bearer. REVA is independent of channel conditions and allows easy derivation of an individual wireless link’s throughput. In order for the slice broker to e�ciently utilize the RAN, there is a need for reliable and short term prediction of resource usage by a slice. To support such prediction, we construct an LTE testbed and develop custom additions to the scheduler. Using data collected from the testbed, we compute REVA and develop a realistic time series prediction model for REVA. Speci�cally, we present the X-LSTM prediction model, based upon Long Short-Term Memory (LSTM) neural networks. Evaluated with data collected in the testbed, X-LSTM outperforms Autoregressive Integrated Moving Average Model (ARIMA) and LSTM neural networks by up to 31%. X-LSTM also achieves over 91% accuracy in predicting REVA. By using X-LSTM to predict future usage, a slice broker is more adept to provision a slice and reduce over-provisioning and SLA violation costs by more than 10% in comparison to LSTM and ARIMA.