3,569 research outputs found
Survey of Spectrum Sharing for Inter-Technology Coexistence
Increasing capacity demands in emerging wireless technologies are expected to
be met by network densification and spectrum bands open to multiple
technologies. These will, in turn, increase the level of interference and also
result in more complex inter-technology interactions, which will need to be
managed through spectrum sharing mechanisms. Consequently, novel spectrum
sharing mechanisms should be designed to allow spectrum access for multiple
technologies, while efficiently utilizing the spectrum resources overall.
Importantly, it is not trivial to design such efficient mechanisms, not only
due to technical aspects, but also due to regulatory and business model
constraints. In this survey we address spectrum sharing mechanisms for wireless
inter-technology coexistence by means of a technology circle that incorporates
in a unified, system-level view the technical and non-technical aspects. We
thus systematically explore the spectrum sharing design space consisting of
parameters at different layers. Using this framework, we present a literature
review on inter-technology coexistence with a focus on wireless technologies
with equal spectrum access rights, i.e. (i) primary/primary, (ii)
secondary/secondary, and (iii) technologies operating in a spectrum commons.
Moreover, we reflect on our literature review to identify possible spectrum
sharing design solutions and performance evaluation approaches useful for
future coexistence cases. Finally, we discuss spectrum sharing design
challenges and suggest future research directions
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks
This paper investigates the use of deep reinforcement learning (DRL) in a MAC
protocol for heterogeneous wireless networking referred to as
Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is
partially inspired by the vision of DARPA SC2, a 3-year competition whereby
competitors are to come up with a clean-slate design that "best share spectrum
with any network(s), in any environment, without prior knowledge, leveraging on
machine-learning technique". Specifically, this paper considers the problem of
sharing time slots among a multiple of time-slotted networks that adopt
different MAC protocols. One of the MAC protocols is DLMA. The other two are
TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC
protocols are TDMA and ALOHA. Yet, by a series of observations of the
environment, its own actions, and the resulting rewards, a DLMA node can learn
an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes
according to a specified objective (e.g., the objective could be the sum
throughput of all networks, or a general alpha-fairness objective)
Coordinated Dynamic Spectrum Management of LTE-U and Wi-Fi Networks
This paper investigates the co-existence of Wi-Fi and LTE in emerging
unlicensed frequency bands which are intended to accommodate multiple radio
access technologies. Wi-Fi and LTE are the two most prominent access
technologies being deployed today, motivating further study of the inter-system
interference arising in such shared spectrum scenarios as well as possible
techniques for enabling improved co-existence. An analytical model for
evaluating the baseline performance of co-existing Wi-Fi and LTE is developed
and used to obtain baseline performance measures. The results show that both
Wi-Fi and LTE networks cause significant interference to each other and that
the degradation is dependent on a number of factors such as power levels and
physical topology. The model-based results are partially validated via
experimental evaluations using USRP based SDR platforms on the ORBIT testbed.
Further, inter-network coordination with logically centralized radio resource
management across Wi-Fi and LTE systems is proposed as a possible solution for
improved co-existence. Numerical results are presented showing significant
gains in both Wi-Fi and LTE performance with the proposed inter-network
coordination approach.Comment: Accepted paper at IEEE DySPAN 201
Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi
We study the fair coexistence of scheduled and random access transmitters
sharing the same frequency channel. Interest in coexistence is topical due to
the need for emerging unlicensed LTE technologies to coexist fairly with WiFi.
However, this interest is not confined to LTE/WiFi as coexistence is likely to
become increasingly commonplace in IoT networks and beyond 5G. In this article
we show that mixing scheduled and random access incurs and inherent
throughput/delay cost, the cost of heterogeneity. We derive the joint
proportional fair rate allocation, which casts useful light on current LTE/WiFi
discussions. We present experimental results on inter-technology detection and
consider the impact of imperfect carrier sensing.Comment: 14 pages, 8 figures, journa
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A decentralized approach for self-coexistence among heterogeneous networks in TVWS
IEEE This paper focuses on coexistence and self- coexistence challenges between secondary heterogeneous wireless networks/users sharing TV Whitespace spectrum. The coexistence problems arise from having several primary and secondary networks of different technologies cohabiting the same licensed spectrum simultaneously. The self- coexistence problems arise from many secondary systems /users coexisting at the same place while using identical or different technologies. In particular, fair distribution of available spectrum becomes a serious issue. In this work we use a game theoretic approach to model the self-coexistence problem as a competitive game between secondary networks. We show that our game belongs to the class of congestion-averse games which are known to posses pure Nash Equilibria. This leads us to a decentralized approach for spectrum sharing among systems with different PHY/MAC characteristics. We show that our proposal outperforms other centralized algorithms in terms of user fairness and per-user theoretical data rates
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