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Register a new userDynamic Power Control for Time-Critical Networking with Heterogeneous Traffic
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Added by
Emmanouil Fountoulakis
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Future wireless networks will be characterized by heterogeneous traffic requirements. Such requirements can be low-latency or minimum-throughput. Therefore, the network has to adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before a deadline, and they co-exist with throughput oriented users. In addition, the users are mobile and they share the same wireless channel. Therefore, they have to adjust their power transmission to achieve reliable communication. However, due to the limited power budget of wireless mobile devices, a power-efficient scheduling scheme is required by the network. In this work, we cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimum throughput and taking into account the limited-power capabilities of the users. We apply tools from Lyapunov optimization theory in order to provide an algorithm, named Dynamic Power Control (DPC) algorithm, that solves the formulated problem in realtime. It is proved that the DPC algorithm gives a solution arbitrarily close to the optimal one. Simulation results show that our algorithm outperforms the baseline Largest-Debt-First (LDF) algorithm for short deadlines and multiple users.
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In 5G and beyond systems, the notion of latency gets a great momentum in wireless connectivity as a metric for serving real-time communications requirements. However, in many applications, research has pointed out that latency could be inefficient to handle applications with data freshness requirements. Recently, the notion of Age of Information (AoI) that can capture the freshness of the data has attracted a lot of attention. In this work, we consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, we cast a novel optimization problem formulation for minimizing the average AoI while satisfying the timely throughput constraints. The formulated problem is cast as a Constrained Markov Decision Process (CMDP). We relax the constrained problem to an unconstrained Markov Decision Process (MDP) problem by utilizing Lyapunov optimization theory and it can be proved that it is solved per frame by applying backward dynamic programming algorithms with optimality guarantees. Simulation results show that the timely throughput constraints are satisfied while minimizing the average AoI. Also, simulation results show the convergence of the algorithm for different values of the weighted factor and the trade-off between the AoI and the timely throughput.
Traffic load balancing and radio resource management is key to harness the dense and increasingly heterogeneous deployment of next generation $5$G wireless infrastructure. Strategies for aggregating user traffic from across multiple radio access technologies (RATs) and/or access points (APs) would be crucial in such heterogeneous networks (HetNets), but are not well investigated. In this paper, we develop a low complexity solution for maximizing an $alpha$-optimal network utility leveraging the multi-link aggregation (simultaneous connectivity to multiple RATs/APs) capability of users in the network. The network utility maximization formulation has maximization of sum rate ($alpha=0$), maximization of minimum rate ($alpha to infty$), and proportional fair ($alpha=1$) as its special cases. A closed form is also developed for the special case where a user aggregates traffic from at most two APs/RATs, and hence can be applied to practical scenarios like LTE-WLAN aggregation (LWA) and LTE dual-connectivity solutions. It is shown that the required objective may also be realized through a decentralized implementation requiring a series of message exchanges between the users and network. Using comprehensive system level simulations, it is shown that optimal leveraging of multi-link aggregation leads to substantial throughput gains over single RAT/AP selection techniques.
Traffic load balancing and resource allocation is set to play a crucial role in leveraging the dense and increasingly heterogeneous deployment of multi-radio wireless networks. Traffic aggregation across different access points (APs)/radio access technologies (RATs) has become an important feature of recently introduced cellular standards on LTE dual connectivity and LTE-WLAN aggregation (LWA). Low complexity traffic splitting solutions for scenarios where the APs are not necessarily collocated are of great interest for operators. In this paper, we consider a scenario, where traffic for each user may be split across macrocell and an LTE or WiFi small cells connected by non-ideal backhaul links, and develop a closed form solution for optimal aggregation accounting for the backhaul delay. The optimal solution lends itself to a water-filling based interpretation, where the fraction of users traffic sent over macrocell is proportional to ratio of users peak capacity on that macrocell and its throughput on the small cell. Using comprehensive system level simulations, the developed optimal solution is shown to provide substantial edge and median throughput gain over algorithms representative of current 3GPP-WLAN interworking solutions. The achievable performance benefits hold promise for operators expecting to introduce aggregation solutions with their existing WLAN deployments.
The growing demand for high-speed data, quality of service (QoS) assurance and energy efficiency has triggered the evolution of 4G LTE-A networks to 5G and beyond. Interference is still a major performance bottleneck. This paper studies the application of physical-layer network coding (PNC), a technique that exploits interference, in heterogeneous cellular networks. In particular, we propose a rate-maximising relay selection algorithm for a single cell with multiple relays based on the decode-and-forward strategy. With nodes transmitting at different powers, the proposed algorithm adapts the resource allocation according to the differing link rates and we prove theoretically that the optimisation problem is log-concave. The proposed technique is shown to perform significantly better than the widely studied selection-cooperation technique. We then undertake an experimental study on a software radio platform of the decoding performance of PNC with unbalanced SNRs in the multiple-access transmissions. This problem is inherent in cellular networks and it is shown that with channel coding and decoders based on multiuser detection and successive interference cancellation, the performance is better with power imbalance. This paper paves the way for further research in multi-cell PNC, resource allocation, and the implementation of PNC with higher-order modulations and advanced coding techniques.
We consider a dense, ad hoc wireless network, confined to a small region. The wireless network is operated as a single cell, i.e., only one successful transmission is supported at a time. Data packets are sent between sourcedestination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc wireless network (described above) as a single cell, we study the hop length and power control that maximizes the transport capacity for a given network power constraint.
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