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In this letter, we propose an airtime-based Resource Allocation (RA) model for network slicing in IEEE 802.11 Radio Access Networks (RANs). We formulate this problem as a Quadratically Constrained Quadratic Program (QCQP), where the overall queueing delay of the system is minimized while strict Ultra-Reliable Low Latency Communication (URLLC) constraints are respected. We evaluated our model using three different solvers where the optimal and feasible sets of airtime configurations were computed. We also validated our model with experimentation in real hardware. Our results show that the solution time for computing optimal and feasible configurations vary according to the slice's demand distribution and the number of slices to be allocated. Our findings support the need for precise RA over IEEE 802.11 RANs and present the limitations of performing such optimizations at runtime.

Given the urgency of standardizing the 5 th generation mobile systems (5G) to meet the ever more stringent demands of new applications, the importance of field trials and experimentation cannot be overstated. Practical experimentation with cellular networks has been historically reserved exclusively to operators, primarily due to equipment costs and licensing constraints. The state of play is changing with the advent of open-source cellular stacks based on increasingly more affordable software defined radio (SDR) systems. Comprehensive understanding of the performance, limitations, and interoperability of these tools however lacks. In this article we fill this gap, by assessing by means of controlled experiments the performance of today's most popular open software Evolved Node B (eNB) solutions in combination with different commodity User Equipment (UE) and an SDR alternative, over a range of practical settings. Although these cannot underpin complete 5G systems yet, their development is progressing rapidly and researchers have employed them for 5G specific applications including LTE unlicensed and network slicing. We further shed light onto the perils of open tools and give configuration guidelines that can be used to deploy these solutions effectively. Our results quantify the throughput attainable with each stack, their resource consumption footprint, and their reliability and bootstrap times in view of automating experimentation. Lastly, we evaluate qualitatively the extensibility of the solutions considered.

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Traditionally, the radio spectrum has been allocated statically. However, this process has become obsolescence as most of the allocated spectrum is underutilized, and the part of the spectrum that is mainly used by the technologies that we use for daily communication is over-utilized. As a result, there is a shortage of available spectrum to deploy emerging technologies like 5G that require high demands on data. Several global efforts are addressing this problem, i.e., the Citizens Broadband Radio Service (CBRS) and Licensed Shared Access (LSA) bands, to increase the spectrum reuse by providing multi-tiers spectrum sharing frameworks in the re-allocated radio spectrum. However, these approaches suffer from two main problems. First, this is a slow process that may take years before authorities can reassign the spectrum to new uses. Second, they do not scale fast since it requires a centralized infrastructure to protect the legacy technology and coordinate and grant access to the shared spectrum. As a solution, the Spectrum Collaboration Challenge (SC2) challenge has shown that Collaborative Intelligent Radio Network (CIRN), i.e., Artificial Intelligence (AI)based autonomous wireless radio technologies that collaborate, can share and reuse spectrum efficiently without any coordination and with the guarantee of incumbent protection. In this paper, we present the architectural design and the experimental validation of an incumbent protection system for the next generation of spectrum sharing frameworks. The proposed system is a twostep AI-based algorithm that recognizes, learns, and proactively predicts the transmission pattern of the incumbent in near real-time, less than 300 ms to perform a prediction, with an accuracy above 95% to correctly predict where the incumbent is transmitting in the future. The proposed algorithm was validated in Colosseum, the RF channel emulator built for the SC2 competition, using up to two incumbents simultaneously, which have different transmission patterns, and sharing spectrum with up to 5 additional networks.

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Self-organizing networks able to adapt to changes in the environment have already been a longstanding research topic. Given the limited number of license-free Industrial, Scientific, and Medical (ISM) radio bands, wireless technologies end up competing with one another for the wireless spectrum. As such, the proper employment of Medium Access Control (MAC) protocols is essential to guarantee efficient and reliable wireless communication. At the data link level, there has been extensive research towards programmable and more future-proof MAC protocols (e.g., Software-Defined Radios (SDRs), which enable to reconfigure the entire protocol and hence access/control finegrained radio functionalities). However, actual deployments are so far limited because of performance issues and cost. With the increasing popularity of Software-Defined Networking (SDN), also in the wireless domain, and the increasing performance of SDRs, we are evolving into a fully programmable data link layer. In this survey, we deliver: a landscape of the state-of-theart on programmable MAC protocols; a coherent terminology that represents scope and level of programmability supported; an in-depth study of their advantages and disadvantages; and a discussion about future research challenges on MAC programmability. Many surveys have investigated the use of specific MAC protocols for a wide range of optimization criteria and application demands. This survey is the first that investigates the scope and the level of programmability that MAC protocols support. TABLE I RELATED SURVEYS ARTICLES ON WIRELESS MAC PROTOCOLS AND THEIR MAIN TARGETS. Authors Year Main Target Focus Technology H. Peyravi [18] 1999 Classify MAC protocols based on five classes: fixed assignments, Mode-of-operation Satellite Networks demand assignment, random access, hybrid of random access Performance and reservation, and adaptive protocols. Reconfigurability A. C. V. Gummalla 2000 Classify MAC protocols based on architecture design, Mode-of-operation Wireless Networks and J. O. Limb [19] mode-of-operation, performance, and application domain. Architecture S. Kumar, V. S. 2006 Classify MAC protocols based on their brief description, Mode-of-operation Ad Hoc Networks Raghavan, and J. Deng [20] mode-of-operation, and underlying features. Underlying Features I. Demirkol, C. Ersoy, 2006 Describe MAC protocols emphasizing energy consumption, Mode-of-operation WSNs and F. Alagoz [21] strengths and weaknesses. Energy efficiency T. V. Krishna 2009 Compare MAC protocols for centralized and decentralized Architecture CRNs and A. Das [22] Opportunistic Spectrum Access (OSA) networks. Reconfigurability C. Cormio and 2009 Compare MAC protocols according to its features and the Mode-of-operation CRNs K. R. Chowdhury [23] different modes-of-operation. Underlying features M. J. Booysen, S. Zeadally, 2011 Survey the different MAC protocols focusing on the benefits and Mode-of-operation Vehicular Ad Hoc and G. J. van Rooyen [24] limitations of their mode-of-operation on future deployments. Performance...