Unified Framework Towards Flexible Multiple Access Schemes for 5G

Non-orthogonal multiple access （NOMA） schemes have achieved great attention recently and been considered as a crucial compo-nent for 5G wireless networks since they can efficiently enhance the spectrum efficiency, support massive connections and poten-tially reduce access latency via grant free access. In this paper, we introduce the candidate NOMA solutions in 5G networks, com-paring the principles, key features, application scenarios, transmitters and receivers, etc. In addition, a unified framework of these multiple access schemes are proposed to improve resource utilization, reduce the cost and support the flexible adaptation of multi-ple access schemes. Further, flexible multiple access schemes in 5G systems are discussed. They can support diverse deployment scenarios and traffic requirements in 5G. Challenges and future research directions are also highlighted to shed some lights for the standardization in 5G.

AI/ML Enabled Automation System for Software Defined Disaggregated Open Radio Access Networks:Transforming Telecommunication Business

Open Air Interface(OAI)alliance recently introduced a new disaggregated Open Radio Access Networks(O-RAN)framework for next generation telecommunications and networks.This disaggregated architecture is open,automated,software defined,virtual,and supports the latest advanced technologies like Artificial Intelligence(AI)Machine Learning(AI/ML).This novel intelligent architecture enables programmers to design and customize automated applications according to the business needs and to improve quality of service in fifth generation(5G)and Beyond 5G(B5G).Its disaggregated and multivendor nature gives the opportunity to new startups and small vendors to participate and provide cheap hardware software solutions to keep the market competitive.This paper presents the disaggregated and programmable O-RAN architecture focused on automation,AI/ML services,and applications with Flexible Radio access network Intelligent Controller(FRIC).We schematically demonstrate the reinforcement learning,external applications(xApps),and automation steps to implement this disaggregated O-RAN architecture.The idea of this research paper is to implement an AI/ML enabled automation system for software defined disaggregated O-RAN,which monitors,manages,and performs AI/ML-related services,including the model deployment,optimization,inference,and training.

Capacity and Flexibility Improvement of Traffic Aggregation for Fixed 5G:Key Enabling Technologies,Challenges and Trends

As the emergence of various highbandwidth services and the requirements to support 5G/Wi-Fi 6 wireless networks,the next generation fixed networks,i.e.F5G,are expected to be realized in the 5G era.F5G is endowed with new characteristics,including ultra-high bandwidth,all-optical connections and optimal service experience.With the prospect of optical-to-everywhere,optical technologies are used for mobile front-haul,mid-haul,and back-haul.Optical access networks would play an important role in F5G to support radio access network and fixed access network.Low-latency PON is a key for cost effective-haul traffic aggregation.In terms of signal transmission,intensity modulation directdetection(IM-DD)is a promising scheme due to its simple architecture.The fundamental challenge associated with direct-detection is the disappearance of the transmitted signal’s phase.In access network,the flexibility and low latency are the two key factors affecting service experience.In this article,we review the evolution of PONs and the challenges of current PONs in detail.We analyze key enabling digital signal processing(DSP)techniques,including detection linearization for direct-detection and simplified coherent detection,adaptive equalizers,digital filer enabled flexible access network and low-latency inter-ONU communications.Finally,we discuss the developing trends of future optical access networks.

A Novel Memory Structure for Embedded Systems： Flexible Sequential and Random Access Memory

The on-chip memory performance of embedded systems directly affects the system designers＇ decision about how to allocate expensive silicon area. A novel memory architecture, flexible sequential and random access memory （FSRAM）, is investigated for embedded systems. To realize sequential accesses, small “links”are added to each row in the RAM array to point to the next row to be prefetched. The potential cache pollution is ameliorated by a small sequential access buyer （SAB）. To evaluate the architecture-level performance of FSRAM, we ran the Mediabench benchmark programs on a modified version of the SimpleScalar simulator. Our results show that the FSRAM improves the performance of a baseline processor with a 16KB data cache up to 55%, with an average of 9%; furthermore, the FSRAM reduces 53.1% of the data cache miss count on average due to its prefetching effect. We also designed RTL and SPICE models of the FSRAM, which show that the FSRAM significantly improves memory access time, while reducing power consumption, with negligible area overhead.

Rainbow:reliable personally identifiable information retrieval across multi-cloud

Personally identifiable information(PII)refers to any information that links to an individual.Sharing PII is extremely useful in public affairs yet hard to implement due to the worries about privacy violations.Building a PII retrieval service over multi-cloud,which is a modern strategy to make services stable where multiple servers are deployed,seems to be a promising solution.However,three major technical challenges remain to be solved.The first is the privacy and access control of PII.In fact,each entry in PII can be shared to different users with different access rights.Hence,flexible and fine-grained access control is needed.Second,a reliable user revocation mechanism is required to ensure that users can be revoked efficiently,even if few cloud servers are compromised or collapse,to avoid data leakage.Third,verifying the correctness of received PII and locating a misbehaved server when wrong data are returned is crucial to guarantee user’s privacy,but challenging to realize.In this paper,we propose Rainbow,a secure and practical PII retrieval scheme to solve the above issues.In particular,we design an important cryptographic tool,called Reliable Outsourced Attribute Based Encryption(ROABE)which provides data privacy,flexible and fine-grained access control,reliable immediate user revocation and verification for multiple servers simultaneously,to support Rainbow.Moreover,we present how to build Rainbow with ROABE and several necessary cloud techniques in real world.To evaluate the performance,we deploy Rainbow on multiple mainstream clouds,namely,AWS,GCP and Microsoft Azure,and experiment in browsers on mobile phones and computers.Both theoretical analysis and experimental results indicate that Rainbow is secure and practical.