A generalized analysis of the IEEE 802.15.4 medium access control (MAC) protocol in terms of reliability, delay and energy consumption is presented. The IEEE 802.15.4 exponential backoff process is modeled through a Markov chain taking into account retry limits, acknowledgements, and unsaturated traffic. Simple and effective approximations of the reliability, delay and energy consumption under low traffic regime are proposed. It is demonstrated that the delay distribution of IEEE 802.15.4 depends mainly on MAC parameters and collision probability. In addition, the impact of MAC parameters on the performance metrics is analyzed. The analysis is more general and gives more accurate results than existing methods in the literature. Monte Carlo simulations confirm that the proposed approximations offer a satisfactory accuracy.QC 2011012
In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and with the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean 5G RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture relies on a single general-purpose learning framework conceived for RRM directly using the data gathered in the network. The complexity of RRM is shifted to the design of the framework, whilst the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios and future directions on applications of machine learning to RRM are discussed.
Opportunistic routing is widely known to have substantially better performance than unicast routing in wireless networks with lossy links. However, wireless sensor networks are usually duty cycled, that is, they frequently enter sleep states to ensure long network lifetime. This renders existing opportunistic routing schemes impractical, as they assume that nodes are always awake and can overhear other transmissions. In this article we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. ORW uses a novel opportunistic routing metric, EDC, that reflects the expected number of duty-cycled wakeups that are required to successfully deliver a packet from source to destination. We devise distributed algorithms that find the EDC-optimal forwarding and demonstrate using analytical performance models and simulations that EDC-based opportunistic routing results in significantly reduced delay and improved energy efficiency compared to traditional unicast routing. In addition, we evaluate the performance of ORW in both simulations and testbed-based experiments. Our results show that ORW reduces radio duty cycles on average by 50% (up to 90% on individual nodes) and delays by 30% to 90% when compared to the state-of-the-art.
Abstract-Optimizing radio transmission power and user data rates in wireless systems via power control requires an accurate and instantaneous knowledge of the system model. While this problem has been extensively studied in the literature, an efficient solution approaching optimality with the limited information available in practical systems is still lacking. This paper presents a reinforcement learning framework for power control and rate adaptation in the downlink of a radio access network that closes this gap. We present a comprehensive design of the learning framework that includes the characterization of the system state, the design of a general reward function, and the method to learn the control policy. System level simulations show that our design can quickly learn a power control policy that brings significant energy savings and fairness across users in the system. Index Terms-Power and rate control, reinforcement learning.
Abstract-Opportunistic routing is widely known to have substantially better performance than traditional unicast routing in wireless networks with lossy links. However, wireless sensor networks are heavily duty-cycled, i.e. they frequently enter deep sleep states to ensure long network life-time. This renders existing opportunistic routing schemes impractical, as they assume that nodes are always awake and can overhear other transmissions.In this paper, we introduce a novel opportunistic routing metric that takes duty cycling into account. By analytical performance modeling and simulations, we show that our routing scheme results in significantly reduced delay and improved energy efficiency compared to traditional unicast routing. The method is based on a new metric, EDC, that reflects the expected number of duty cycled wakeups that are required to successfully deliver a packet from source to destination. We devise distributed algorithms that find the EDC-optimal forwarding, i.e. the optimal subset of neighbors that each node should permit to forward its packets. We compare the performance of the new routing with ETX-optimal single path routing in both simulations and testbed-based experiments.
We consider the joint design of packet forwarding policies and controllers for wireless control loops where sensor measurements are sent to the controller over an unreliable and energy-constrained multi-hop wireless network. For fixed sampling rate of the sensor, the co-design problem separates into two well-defined and independent subproblems: transmission scheduling for maximizing the deadlineconstrained reliability and optimal control under packet loss. We develop optimal and implementable solutions for these subproblems and show that the optimally co-designed system can be efficiently found.Numerical examples highlight the many trade-offs involved and demonstrate the power of our approach. Index Terms Optimal control; Wireless sensor networks; Markov decision process * ACCESS Linnaeus Centre, KTH Royal Institute of Technology, Osquldas vag 10, SE-100 44 Stockholm, Sweden. ‡ Huawei Technologies Sweden AB, Skalhogatan 9-11 box 54, SE-164 94, Kista, Sweden. † Corresponding author. E-mail: burak.demirel@ee.kth.se. A preliminary version of parts of this work was presented at several conferences; see [1]-[3]. November 2, 2018 DRAFT arXiv:1204.3100v3 [math.OC] 1 Jul 2014 DRAFT. 2 on the one hand, and control, on the other. It is then often possible to make the communication and computation appear reliable and predictable at the time scale of the control loop. When communication, computation and control do interact, the burden of ensuring reliable systemlevel performance is typically put on the control system. Using high-level abstractions of the deficiencies introduced by unreliable communication and resource-constrained hardware, controlalgorithms are synthesized to be robust to these uncertainties. However, robustification of control laws can come at a high performance price, and it is sometimes simply not possible to compensate for networking shortcomings in control software.We argue that efficient CPS systems must be based on the joint design of communication, computation, and control. At the same time, it is essential that such a joint design is modular, with well-defined interfaces between control algorithms and networking and computation primitives.Modularity allows for specialized development and innovation within each component without affecting the logical correctness of the overall system, and has been a key to massive proliferation in computing and communications. To this end, this paper explores modular co-design of networked control systems with certain optimality properties of the overall system.Networked control has been an active area of research for more than a decade, and the literature is by now rather extensive, see e.g., [6], [7] and the references therein. The research has mainly focused on control design methods that rely on high-level abstractions of the communication network in terms of its latency or loss. State-of-the-art control design techniques are extremely powerful when the control system is able to cope with the network deficiencies. However, when the resulting closed-loop performan...
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