Controlling robots in real-time over a wireless interface present fundamental challenges for forthcoming fifth generation wireless networks. Mission critical real-time applications such as telesurgery over the tactile Internet require a communication link that is both ultra-reliable and low-latency, and that simultaneously serving multiple devices and applications. Wireless performance requirements for these applications surpass the capabilities of current wireless cellular standards. The prevailing ambitions for the fifth generation wireless specifications go beyond higher throughput and embrace the wireless performance demands of mission critical real-time applications in robotics and the Internet of Things. To accommodate these demands, changes have to be made across all layers of the wireless infrastructure. The fifth generation wireless standards are far from finalized but massive Multiple-Input Multiple-Output has surfaced as a strong radio access technology candidate and has great potential to cope with all these stringent requirements. In this paper, we investigate how Ultra-Reliable and Low-Latency Communication with massive MIMO can be achieved for bilateral teleoperation, an integral part of the tactile Internet. We conclude through simulation what the performance bounds are for massive MIMO and thus how to configure such a system for near deterministic latency and what the inherit trade-offs are.
In this paper, optimal resource allocation policies are characterized for wireless cognitive networks under the spectrum leasing model. We propose cooperative schemes in which secondary users share the time-slot with primary users in return for cooperation. Cooperation is feasible only if the primary system's performance is improved over the non-cooperative case. First, we investigate a scheduling problem where secondary users are interested in immediate rewards. Here, we consider both infinite and finite backlog cases. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long-term basis, in return for cooperation. Finally, we present a power allocation problem where the goal is to maximize the expected net benefit defined as utility minus cost of energy. Our proposed scheduling policies are shown to outperform non-cooperative scheduling policies, in terms of expected utility and net benefit, for a given set of feasible constraints. Based on Lyapunov optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.
Recently, IEEE 802.11ax Task Group has adapted OFDMA as a new technique for enabling multi-user transmission. It has been also decided that the scheduling duration should be same for all the users in a multi-user OFDMA so that the transmission of the users should end at the same time. In order to realize that condition, the users with insufficient data should transmit null data (i.e. padding) to fill the duration. While this scheme offers strong features such as resilience to Overlapping Basic Service Set (OBSS) interference and ease of synchronization, it also poses major side issues of degraded throughput performance and waste of devices' energy. In this work, for OFDMA based 802.11 WLANs we first propose practical algorithm in which the scheduling duration is fixed and does not change from time to time. In the second algorithm the scheduling duration is dynamically determined in a resource allocation framework by taking into account the padding overhead, airtime fairness and energy consumption of the users. We analytically investigate our resource allocation problems through Lyapunov optimization techniques and show that our algorithms are arbitrarily close to the optimal performance at the price of reduced convergence rate. We also calculate the overhead of our algorithms in a realistic set-up and propose solutions for the implementation issues.
It is well known that opportunistic scheduling algorithms are throughput optimal under dynamic channel and network conditions. However, these algorithms achieve a hypothetical rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. In this work, we design a joint scheduling and channel probing algorithm by considering the overhead of obtaining the channel state information. We adopt a correlated and non-stationary channel model, which is more realistic than those used in the literature. We use concepts from learning and information theory to accurately track channel variations to minimize the number of channels probed at every slot, while scheduling users to maximize the achievable rate region of the network. Simulation results show that with the proposed algorithm, the network can carry higher user traffic.
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