Efficient communication and computing resource allocation is becoming a fundamental issue in wireless networks. Efficiency is most often defined in terms of throughput, utilization and spectral efficiency, while the required computational effort is often overlooked. In this paper, we focus on efficient and computationally lightweight downlink scheduling, and we propose a semi-persistent scheduler based on adaptive short term traffic prediction. We evaluate the performance of the proposed scheduler in terms of throughput, fairness, latency, and scheduling complexity. Our numerical results show that scheduling with prediction is a promising approach in improving network performance. The proposed semi-persistent scheduler performs equally well in terms of throughput, fairness, and latency as traditional proportional-fair scheduling, but at a significantly reduced computational cost.

Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The prediction of network traffic at the user level is particularly challenging, because the traffic characteristics emerge from a complex interaction of user level and application protocol behavior. In this work we address the problem of predicting the network traffic at the user level over a short horizon, motivated by its applications in cellular scheduling. Motivated by recent works on robust adversarial learning, we treat the prediction problem for non-stationary traffic in an adversarial context, and propose a meta-learning scheme that consists of a set of predictors, each optimized to predict a particular kind of traffic, and of a master policy that is trained for choosing the best fit predictor dynamically based on recent prediction performance, using deep reinforcement learning. We evaluate the proposed meta-learning scheme on a variety of traffic traces consisting of video and non-video traffic. Our results show that it consistently outperforms state-of-the-art predictors, and can adapt to before unseen traffic without the need for retraining the individual predictors.

Link scheduling, i.e., which links should transmit together and for how long, has been and remains a cornerstone optimization problem in wireless networking. In minimum-time scheduling, the task is to minimize the amount of time before emptying the data demand residing at the source nodes. We derive a complete structural characterization of the solution that unifies and significantly extends the known results. First, we approach link scheduling with a general system model without restrictions on the shape of the achievable rate region. Then, we give and prove a solution characterization of optimality that is conceptually simple yet powerful. We demonstrate several applications of this characterization for analysis of optimality and problem tractability. Next, we consider a significant extension by including deadline constraints, under which optimal scheduling becomes much more complex. Yet, we show how our formulation yields a solution description for that problem as well.

Age of information has been recently proposed to quantify the freshness of information, e.g., in cyber-physical systems, where it is of critical importance. Motivated by wireless camera networks where multi-view image processing is required, in this paper we propose to extend the concept of age of information to capture packets carrying correlated data. We consider a system consisting of wireless camera nodes with overlapping fields of view and a set of processing nodes, and address the problem of the joint optimization of processing node assignment and camera transmission scheduling, so as to minimize the maximum peak age of information from all sources. We formulate the multi-view age minimization (MVAM) problem, and prove its NP-hardness under the two widely used interference models as well as with given candidate transmitting groups. We provide fundamental results including tractable cases and optimality conditions of the MVAM problem for two baseline scenarios. To solve MVAM efficiently, we develop an optimization algorithm based on a decomposition approach. Numerical results show that by employing our approach the maximum peak age is significantly reduced in comparison to a traditional centralized solution with minimum-time scheduling.

Information age is a recently introduced metric to represent the freshness of information in communication systems. We investigate age minimization in a wireless network and propose a novel approach of optimizing the scheduling strategy to deliver all messages as fresh as possible. Specifically, we consider a set of links that share a common channel. The transmitter at each link contains a given number of packets with time stamps from an information source that generated them. We address the link transmission scheduling problem with the objective of minimizing the overall age. This minimum age scheduling problem (MASP) is different from minimizing the time or the delay for delivering the packets in question. We model the MASP mathematically and prove it is NP-hard in general. We also identify tractable cases as well as optimality conditions. An integer linear programming formulation is provided for performance benchmarking. Moreover, a steepest age descent algorithm with better scalability is developed. Numerical study shows that, by employing the optimal schedule, the overall age is significantly reduced in comparison to other scheduling strategies.

We consider link scheduling in wireless networks for emptying the queues at the transmitters in minimum time, with time constraints, or deadlines, for one or multiple individual links. We formulate the minimum-time scheduling problem with deadlines (MTSD) mathematically and derive the optimal activation order of the link sets in a schedule solution. Theoretical results are obtained, showing that the MTSD can be treated as the conventional minimum-time scheduling problem by “absorbing” the deadline constraints into the rate region where the scheduling problem is defined. By this approach, optimality characterization and geometric interpretation for the MTSD are provided. Furthermore, we extend the results to the MTSD in a general form that accommodates an arbitrary rate region.

We address the maximum link activation problem in wireless networks with new features, namely when the transmitters can perform cooperative transmission, and the receivers are able to perform successive interference cancellation. In this new problem setting, which transmitters should transmit and to whom, as well as the optimal cancellation patterns at the receivers, are strongly intertwined. We present contributions along three lines. First, we provide a thorough tractability analysis, proving the NP-hardness as well as identifying tractable cases. Second, for benchmarking purposes, we deploy integer linear programming for achieving global optimum using off-the-shelf optimization methods. Third, to overcome the scalability issue of integer programming, we design a suboptimal but efficient optimization algorithm for the problem in its general form, by embedding maximum-weighted bipartite matching into local search. Numerical results are presented for performance evaluation, to validate the benefit of cooperative transmission and interference cancellation for maximum link activation, and to demonstrate the effectiveness of the proposed algorithm.

Freshness of information is of critical importance for a host of applications of wireless communications. In order to deliver information from multiple sources in a timely and fair fashion through a wireless channel, we propose optimizing the link scheduling strategy in respect of age of information, which is a newly introduced metric that measures how fresh information is. Specifically, we consider a set of co-channel links, each having a number of packets to be delivered, and address the problem that aims to find the optimal scheduling solution, such that the maximum peak age of information is minimized. We mathematically formulate this so-called min-max peak age scheduling problem (MPASP), and prove it is NP-hard. Theoretical insights including tractable cases and optimality properties are derived. For problem solution, an integer linear programming (ILP) formulation is proposed. We also develop a sub-optimal, but fast, algorithm to solve the problem with better scalability. Numerical study shows that, by employing the optimal schedule, the maximum peak age is significantly reduced in comparison to other classic scheduling strategies such as minimum-time scheduling.

We consider link scheduling in wireless networks for emptying the queues of the source nodes, and provide a unified mathematical formulation that accommodates all meaningful settings of link transmission rates and network configurations. We prove that, any scheduling problem is equivalent to solving a convex problem defined over the convex hull of the rate region. Based on the fundamental insight, a general optimality condition is derived, that yields a unified treatment of optimal scheduling. Furthermore, we demonstrate the implications and usefulness of the result. Specifically, by applying the theoretical insight to optimality characterization and complexity analysis of scheduling problems, we can both unify and extend previously obtained results.

In wireless powered communication networks (WPCNs), each user node, e.g., wireless powered sensor, is capable of either harvesting energy from a power station or transmitting data to a sink node. In the previous works, time division multiple access (TDMA) is typically used for transmission scheduling in WPCNs, that is, only one node can transmit data in one time slot. The spectrum efficiency is therefore limited by this orthogonality in time-domain scheduling. In this paper, to maximize the throughput in WPCNs, we present a new scheduling approach for energy harvesting and data transmission. Unlike TDMA, we consider that multiple nodes can simultaneously transmit their data in the same time slot, and the signals are separated at the sink node by performing successive interference cancellation (SIC). We formulate the throughput maximization problem as a linear programming problem. For solving the large scale instances, we design an algorithmic framework based on column generation. Numerical results demonstrate that compared to the TDMA based scheduling approach, substantial throughput improvement is achieved by the proposed algorithm.

There is a growing interest in age of information, which is a newly introduced metric that measures the freshness of information in communication systems. We investigate the age of information in wireless networks and propose the novel approach of optimizing the scheduling strategy to deliver the information as timely as possible. We consider a set of links that share a common channel, each containing a number of packets with time stamps, and address the scheduling problem with the objective of minimizing the overall information age. We model this problem mathematically and prove it is NP-hard in general. Fundamental insights including tractable cases and optimality conditions are presented. An integer linear programming formulation is provided for performance benchmarking. Moreover, a steepest age decent algorithm with better scalability is developed. Numerical study shows that, by employing the optimal schedule, the overall information age is significantly reduced in comparison to other scheduling strategies.

We consider a wireless network comprising a number of cochannel (hence mutually interfering) links. We study the power control problem of maximizing the rate that all links can simultaneously support under a novel setup, where receivers have interference cancelation (IC) capabilities. The problem of allocating the transmitting power is intertwined with determining the links on which receivers can perform IC and the order of cancelations. We provide and prove the theoretical results of the problem complexity and structural properties. For the problem solution, we propose a mixed-integer linear programming framework that enables jointly determining the optimal power and the IC patterns using off-the-shelf algorithms. This allows for the accurate assessment of the potential of IC for power control. Extensive numerical results are presented for performance evaluation, demonstrating the benefit of deploying IC in power control.

We consider a wireless network with transmitters and receivers sharing a common channel. We study the maximum link activation (LA) problem under a novel setup, where the transmitters are able to cooperatively transmit data and the receivers have interference cancellation (IC) capabilities. In this version of LA problem, determining which transmitters should jointly transmit and to whom, as well as the proper IC patterns at receivers, to achieve the maximum number of compatible links, are intertwined. We provide and prove theoretical results related to problem complexity. From the theoretical insights, an algorithm composed by local search and bipartite matching is proposed. Numerical results are presented for performance evaluation. The results show the satisfactory performance of the algorithm and demonstrate the benefit of jointly deploying cooperative transmission and successive IC in LA.

We consider a set of transmitter-receiver pairs, or links, that share a wireless medium and address the problem of emptying backlogged queues with given initial size at the transmitters in minimum time. The problem amounts to determining activation subsets of links, and their time durations, to form a minimum-time schedule. Scheduling in wireless networks has been studied under various formulations before. In this paper, we present fundamental insights and solution characterizations that include: 1) showing that the complexity of the problem remains high for any continuous and increasing rate function; 2) formulating and proving sufficient and necessary optimality conditions of two baseline scheduling strategies that correspond to emptying the queues using one-at-a-time or all-at-once strategies; and 3) presenting and proving the tractability of the special case in which the transmission rates are functions only of the cardinality of the link activation sets. These results are independent of physical-layer system specifications and are valid for any form of rate function. We then develop an algorithmic framework for the solution to this problem. The framework encompasses exact as well as sub-optimal, but fast, scheduling algorithms, all under a unified principle design. Through computational experiments, we finally investigate the performance of several specific algorithms from this framework.

We consider N transmitter-receiver pairs that share a wireless channel and we address the problem of obtaining a schedule for activating subsets of these links so as to empty the transmitter queues in minimum time. Our aim is to provide theoretical insights for the optimality characterization of the problem, using both a cross-layer model formulation, which takes into account the effect of interference on achievable transmission rates, as well as a collision-based model, which does not incorporate the physical layer realities into the problem. We present the basic linear programming formulation of the problem and establish that the optimal schedule need not consist of more than N subset activation frames. We then prove that the problem is NP-hard for all reasonable continuous rate functions. Finally, we obtain sufficient and/or necessary conditions for optimality in a number of special cases.