The traditional goal in remote tracking of a dynamic source is to keep the current estimate at the destination as close as possible to the true state. However, in domains such as surveillance applications, the destination is also interested in reconstructing the past trajectory of states for further processing. This requires striking a balance between providing current versus past state information so that the destination can optimize the trade-off between the metrics of freshness and reconstruction queue length. In this work, we propose a randomized update policy that decides between head-of-line versus tail-of-line packets in the update queue. As such, our policy combines the strength of Last-Come-First-Serve (LCFS) service discipline (which aims at reducing the age) with the strength of First-Come-First-Serve (FCFS) service discipline (which aims at reducing the reconstruction delay). We evaluate the performance of our proposed policy in terms of its randomization parameter, which can be optimized given the system parameters to achieve a better trade-off.

The challenge of classification at the network edge is that due to limited computational resources, the edge must transmit the data to a server for processing. However, the communication constraints at the edge necessitate that these devices compress data before transmission. The question this paper aims to answer is how to efficiently compress and transmit this information in order to achieve timely and accurate edge classification. To that end, we develop scheduling algorithms that optimize age of information (AoI) and classification accuracy. Our analysis reveals that in scenarios with multiple available compression levels, an algorithm that selects at most two compression levels can achieve good theoretical performance guarantees. Numerical results indicate that double-level compression algorithms yield near-optimal performance, suggesting that for many classification tasks, numerous compression levels are unnecessary—only two are sufficient, significantly reducing the storage demands on devices and simplifying the overall system design.

We study an Age-of-Information (AoI) scheduling problem where users can tolerate occasional violations of AoI for each source at the base station. Each user’s AoI is associated with a violation tolerance constraint. We are interested in determining whether a set of users, each with a given AoI deadline, a violation tolerance constraint, and a packet loss rate (due to channel condition) is schedulable, and if so, find a feasible scheduler. For this problem, we study two cases: 1) the stable tolerant case where the tolerance rate is higher than the packet loss rate for each source and 2) the unstable tolerant case where the tolerance rate is lower than the packet loss rate for at least one source. For the stable tolerant case, we design an algorithm called stable tolerant scheduler (STS), which can find a feasible scheduler for any network when the system load is no greater than$\ln 2$(roughly 70%). When the system load is between$\ln 2$and 1, we offer a necessary and sufficient condition for STS to find a feasible scheduler by solving an optimization problem. Likewise, for the unstable tolerance case, we develop a scheduler called unstable tolerant scheduler (UTS) and its corresponding schedulability conditions. Through extensive simulations, we show that STS and UTS match our theoretical results.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to AoI-related problems. These problems vary in specific AoI-based objectives and network settings. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we envision and pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization and decision problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve three important problems: to minimize weighted sum of AoIs, to minimize bandwidth requirement under AoI constraints, and to determine the existence of feasible schedulers to satisfy AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new (or general) results that are not available in the literature.

MU-MIMO beamforming is a key technology for 5G networks, relying on Channel State Information (CSI). However, in practice, the estimated CSI in reality is prone to uncertainty. Further, a MU-MIMO beamforming solution must be derived within a millisecond to be useful for real-time 5G applications. We present ReDBeam—a real-time data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main novelties of ReDBeam are a parallel algorithm and an optimized GPU implementation. ReDBeam delivers a MU-MIMO beamforming solution within 1 millisecond to meet the probabilistic data rate requirements from the users, and minimize a base station’s power consumption. Through extensive experiments, we show that ReDBeam consistently meets the stringent 1-millisecond real-time requirement and is orders of magnitude faster than other state-of-the-art algorithms. ReDBeam conclusively demonstrates that MU-MIMO beamforming with data rate requirements can be achieved in real-time using only limited CSI data samples.

MU-MIMO beamforming is a key technology for 5G/NextG networks. In practice, MU-MIMO beamforming requires Channel State Information (CSI) and is prone to uncertainty. Furthermore, a beamforming solution must be derived within a millisecond (ms) to be useful for real-time (RT) 5G applications. We present ReDBeam-a RT data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main contribution of ReDBeam is a parallel algorithm and an optimized GPU implementation. ReDBeam minimizes the base station (BS)'s power consumption while offering a probabilistic guarantee of users' data rates. It is purposefully designed to take advantage of the vast parallel processing capability in commercial off-the-shelf GPUs. Through extensive experiments, we show that ReDBeam can meet the 1 ms RT requirement and is orders of magnitude faster than other state-of-the-art algorithms for the same problem.

The widespread adoption of low-cost resource-constrained edge devices and high-performance expensive servers necessitates shifting the complexity burden from edge devices to servers. However, in many applications such as image classification, it is often impractical and communication expensive to transmit full information without any form of compression. To address this issue, this paper introduces a neural network (NN)-based compression technique tailored for resource-constrained edge devices for classification at the network edge. The core idea involves simultaneously training a shallow neural network to-be-implemented by the devices and a deep neural network to-be-implemented by the server. To adapt to the time-varying channel conditions, the compression algorithm at the device side must be able to handle multiple output dimensions. To address this issue, we develop two multi-dimensional compression strategies: the multiple codebook approach, using separate NNs for various dimensions, and the single codebook approach, utilizing one NN for all dimensions. The single codebook approach substantially reduces the storage demands on the device, offering a viable solution for low-cost edge devices. Our analysis offers a theoretical performance guarantee, highlighting that the accuracy of the single codebook approach is comparable to that of the multiple codebook strategy. Through empirical evaluations on real-world datasets, we demonstrate that the single codebook approach achieves near-equivalent performance to the accuracy multiple codebook alternative.

Age of Information (AoI) is a promising metric to measure information freshness and optimizing AoI through scheduling is one of the most intensely studied areas in AoI research. To date, the vast majority of research on AoI scheduling has been based on simplified communication models that often fail to capture the complexities found in real-world network systems such as 5G. While there are some limited efforts on AoI scheduling that have ventured into exploring OFDMA-based data transmission models similar to those in 5G, they tend to neglect essential elements, such as channel-dependent resource block (RB) allocation and modulation and coding scheme (MCS) assignment, rendering limited utility to real-world 5G systems. In this article, we focus on developing 5G-compliant AoI schedulers. We study a specific problem with the objective of minimizing outdated information across all source nodes. This problem arises from practice where there is a specific information freshness requirement, known as AoI deadline for each source node. We present Aequitas, an innovative 5G scheduler designed to optimize this objective through joint RB allocation and MCS assignment, both of which are dependent on frequency and time-selective channel fading. We exploit a property called “uniform fairness,” derived from the analysis of an optimal offline scheduler, to develop Aequitas. To meet stringent timing requirement in 5G, Aequitas leverages the parallel computing capability of a commercial off-the-shelf GPU. Extensive evaluations demonstrate that Aequitas closely approaches the theoretical lower bound in terms of objective performance, while maintaining operational times below the 5G timing requirement.

Supporting ultra reliable low-latency communication (URLLC) is an extremely challenging problem, due to the excessive requirements on reliability and latency. To date, few of the existing research efforts have successfully addressed the ultra reliability problem for URLLC. This article investigates this problem through the design of a URLLC scheduler for industrial automation under the open radio access network (O-RAN) architecture. We cast the URLLC data transmission problem as a resource scheduling problem, where a set of resource blocks (RBs) from a set of O-RAN radio units (O-RUs) must be allocated to a set of user equipments (UEs) for information transmission. The challenge is to find a scheduling solution in each mini-slot (sub millisecond time scale) based on dynamic channel conditions and satisfy the ultra reliability requirement (e.g., 99.9999%, or six-nine). We present Pistis—a novel scheduler design that fully utilizes the three control loops in O-RAN. Pistis exploits channel slow fading and PHY-layer properties to reduce the search space in its design of the near-real time (near-RT) component. It further leverages GPU parallel computing in its design of the real time (RT) component, which takes into account of fast fading in channel dynamics. We implement Pistis on commercial off-the-shelf hardware and demonstrate that Pistis is able to meet the six-nine reliability requirement for 4 O-RUs, 40 RBs, and 40 UEs within 0.5 ms.

This paper investigates a bandwidth minimization problem with Age of Information (AoI) constraints—a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment while, at the same time, there is an expectation of AoI requirement on the application side. We present a novel polynomial-time algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure calledFractional Consecutively Divisible(FCD). In the general case when the given AoI constraint vector is not FCD, we show that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. We validate the performance of Aion through a large number of simulations and all results confirm our theoretical findings. The results from this paper lay a foundation for future research on bandwidth minimization with AoI guarantee.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to various AoI-related optimization problems. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related optimization problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve two important AoI-related problems: to minimize the weighted sum of AoIs and to minimize the bandwidth requirement under AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new results that are not available in the literature.

Recently, has been recognized that there is a practical limitation with the original notion of Age of Information (AoI) metric in terms of quantifying the freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. In this article, we introduce the notion of AoII+ metric by modifying AoII with practical considerations. Then, we investigate a scheduling problem to minimize AoII+ in an IoT data collection network. We derive a theoretical lower bound for the minimum AoII+. Then, we present Heh—a low-complexity online scheduler to minimize AoII+. The design of Heh is based on the estimation of a novel offline scheduling priority metric without any future knowledge. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes AoII+. Through extensive simulations, we show that the lower bound is very tight and that the AoII+ obtained by Heh is close to optimal.

Task offloading is a powerful tool in Mobile Edge Computing (MEC). However, in many practical scenarios, the number of required processing cycles of a task is unknown beforehand and only known until its completion. This poses a serious challenge in making offloading decisions as the number of processing cycles is a key parameter to determine whether a task’s deadline can be met. To cope with such processing uncertainty, we formulate a Chance-Constrained Program (CCP) that offers probabilistic guarantees to task deadlines. The goal is to minimize energy consumption for the users while meeting the probabilistic task deadlines. We assume that only the means and variances of the random processing cycles are available, without any knowledge of distribution functions. We employ a powerful tool called Exact Conic Reformulation (ECR) that reformulates probabilistic deadline constraints into deterministic ones. Subsequently, we design an online solution called EPD (Energy-minimized solution with Probabilistic Deadline guarantee) for periodic scheduling and schedule updates during run-time. We show that EPD can address the processing uncertainty with probabilistic deadline guarantees while minimizing the users’ energy consumption.

Hybrid beamforming (HB) architecture has been widely considered for 5G mmWave systems. It reduces hardware complexity by allowing the number of RF chains to be far fewer than the number of antennas. A major practical challenge for HB is to obtain a beamforming solution in real-time. In 5G NR, new frame structures with short TTIs are employed to support mmWave communications. Under such frame structures, it is necessary to obtain a beamforming solution with a time resolution varying from 1 ms to 125$\mu$s – an extremely stringent time requirement considering the complexity involved in HB. In this paper, we present the design and implementation ofTurbo-HB– a novel beamforming design under the HB architecture that is capable of offering the beamforming matrices in less than 500$\mu$s. The key ideas of Turbo-HB include: (i) reducing the complexity of computation-intensive SVD operations by exploiting channel sparsity at mmWave frequencies, and (ii) achieving large-scale parallel computation with minimal memory access. We implement Turbo-HB on an off-the-shelf Nvidia GPU and conduct extensive experiments. Our experimental results demonstrate that Turbo-HB can obtain a beamforming solution in 500$\mu$s for up to 100 RBs and 10 MU-MIMO users on each RB while offering competitive throughput performance compared to state-of-the-art (non-real-time) algorithms.

Degree-of-freedom (DoF)-based models have been proven to be highly successful in modeling and analysis of MIMO systems. Among existing DoF-based models, the number of DoFs used for interference cancellation (IC) is solely based on the number of interfering data streams. However, from both experimental and simulation results, we find that signal strengths of an interference link vary significantly in different directions in the eigenspace. In this paper, we exploit the difference in interference signal strengths in the eigenspace and perform IC with DoFs only on those directions with strong signals. To differentiate interference signal strengths on an interference link, we introduce a novel concept called “effective rank threshold.” Based on this threshold, DoFs are consumed only to cancel strong interferences in the eigenspace while weak interferences are treated as noise in throughput calculation. To better understand the benefits of this approach, we study a fundamental trade-off between network throughput and effective rank threshold for an MU-MIMO network. Our simulation results show that network throughput under optimal rank threshold is significantly higher than that under existing DoF IC models. To ensure the new DoF IC model is feasible at PHY layer, we propose an algorithm to set the weights for all nodes that can offer our desired DoF allocation.

Underlay coexistence is a spectrum efficient mechanism to roll out 5G picocells within a macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocells, it is impossible to have complete knowledge of channel conditions between them. Under such a circumstance, chance-constrained programming (CCP) has been shown to be an ideal optimization tool to address such a channel uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G’s timing requirement (125$\mu s$). To address this problem, we propose a novel scheduler called GPU-based Underlay Coexistence (GUC) with the goal of finding an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small subproblems that are suitable for parallel computation on GPU platforms. By selecting a subset of promising subproblems and solving them in parallel with fast algorithms, we are able to leverage GPU parallel computing and develop a real-time solution. Through extensive experiments, we show that GUC meets the 125$\mu s$requirement while achieving 90% optimality on average.

Federated Learning (FL) is a promising technique to enhance the safety and efficiency of intelligent transportation systems. While FL has been extensively studied, the communication and networking challenges related to the operations of FL in dynamic yet dense vehicular networks remain under-explored. Limited storage and communication capacities of individual vehicles throttle the timely training of an FL model in distributed vehicular networks. In this paper, we present a communication framework for FL (CF4FL) in transportation systems. CF4FL aims to accelerate the convergence of FL training process through the innovation of two complementary networking components: (i) a deadline-driven vehicle scheduler (DDVS), and (ii) a concurrent vehicle polling scheme (CVPS). DDVS identifies a subset of vehicles for local model training in each iteration of FL, with the aim of minimizing data loss while respecting the deadline constraints derived from vehicles’ storage, computation, and energy budgets. CVPS takes advantage of multiple antennas on an edge server to enable concurrent local model transmissions in dynamic vehicular networks, thereby reducing the airtime overhead of each FL iteration. We have evaluated CF4FL through a blend of experimentation and simulation. Trace-driven simulation shows that, compared to existing scheduling and transmission schemes, CF4FL reduces the convergence time of FL training by 39%.

Recently, it has been recognized that there is a serious limitation with the original Age of Information (AoI) metric in terms of quantifying true freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. By further refining this new metric with practical considerations, we introduce Age of Outdated Information (Ao2I) metric. In this paper, we investigate a scheduling problem for minimizing Ao2I in an IoT data collection network. We derive a theoretical lower bound for the minimum Ao2I that any scheduler can achieve. Then we present Heh—a low-complexity online scheduler. The design of Heh is based on the estimation of a novel offline scheduling priority metric in the absence of knowledge of the future. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes Ao2I. Through extensive simulations, we show that the lower bound is very tight and that the Ao2I obtained by Heh is close-to-optimal.

With the increasing diversity in the requirement of wireless services with guaranteed Quality of Service (QoS), radio access network (RAN) slicing becomes an important aspect in implementation of next-generation wireless systems (5G). RAN slicing involves the division of network resources into many logical segments where each segment has specific QoS and can serve users of the mobile virtual network operator (MVNO) with these requirements. This allows the network operator (NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining the number of users and therefore, the number of MVNOs that a NO can support. In this work, we study the problem of the modulation and coding scheme (MCS)-aware RAN slicing (MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel quality indicator (CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of a user being served. For this reason, we consider the channel conditions to be dynamic where the choice of the MCS level varies at each time instant. We model the MaRS problem as a NonLinear Programming problem and show that it is NP-Hard. Next, we propose a solution based on the greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of the proposed solution by comparing it against the upper bound under various channel and network configurations.

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when there is an AoI requirement for each source node, which we call Maximum AoI Threshold (MAT). Specifically, we want to determine whether or not a vector of MATs corresponding to the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure calledCyclic Scheduler Detection(CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, calledFictitious Polynomial Mapping(FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under$\ln 2$. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm.

We study an Age of Information (AoI) scheduling problem where AoI for each source at the base station (BS) can tolerate occasional violations, which we define as a violation tolerance constraint. The problem is to determine whether a set of users with given AoI deadlines, tolerance rates, and packet loss rates (due to each source's channel condition) is schedulable, and if so find a feasible scheduler. We study two cases: (i) the stable tolerant case where the tolerance rate is higher than the packet loss rate for all sources; (ii) the unstable tolerant case where the tolerance rate is lower than the packet loss rate for at least one source. For stable tolerant case, we design an algorithm called stable tolerant scheduler (STS), which can find a feasible scheduler for any network when system load is no greater than ln 2. For unstable tolerance case, we develop unstable tolerant scheduler (UTS) and identify a schedulability condition for it. Through extensive simulations, we show that STS and UTS match our theoretical results.

This paper investigates bandwidth minimization under AoI constraints - a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment when AoI is used as a constraint. We present a novel fast algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure called Fractional Consecutively Divisible (FCD). In the general case when the given AoI constraint vector is not FCD, we prove that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. The results from this paper lay the foundation for future research on bandwidth minimization with AoI guarantee.

Mobile Edge Computing (MEC) has emerged to be an integral component of 5G infrastructure due to its potential to speed up task processing and reduce energy consumption for mobile devices. However, a major technical challenge in making offloading decisions is that the number of required processing cycles of a task is usually unknown in advance. Due to this processing uncertainty, it is difficult to make offloading decisions while providing any guarantee on task deadlines. To address this challenge, we propose EPD---Energy-minimized solution with Probabilistic Deadline guarantee to task offloading problem. The mathematical foundation of EPD is Exact Conic Reformulation (ECR), which is a powerful tool that reformulates a probabilistic constraint for task deadline into a deterministic one. In the absence of distribution knowledge of processing cycles, we use the estimated mean and variance of processing cycles and exploit ECR to the fullest extent in the design of EPD. Simulation results show that EPD successfully guarantees the probabilistic deadlines while minimizing the energy consumption of mobile users, and can achieve significant improvement in energy saving when compared to a state-of-the-art approach.

The Age of Information (AoI) is a key metric to measure the freshness of information for IoT applications. Most of the existing analytical models for AoI are overly idealistic and do not capture state-of-the-art transmission technologies such as 5G as well as channel dynamics in both frequency and time domains. In this article, we present Kronos, a real-time 5G-compliant scheduler that minimizes AoI for IoT data collection. Kronos is designed to cope with highly dynamic channel conditions. Its main function is to perform RB allocation and to select the modulation and coding scheme for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we develop a GPU-based implementation of Kronos on commercial off-the-shelf Nvidia GPUs. Through extensive experimentation, we show that Kronos can find near-optimal solutions under submillisecond time scale. To the best of our knowledge, this is the first real-time AoI scheduler that is 5G compliant.

We consider an underlay coexistence scenario where secondary users (SUs) must keep their interference to the primary users (PUs) under control. However, the channel gains from the PUs to the SUs are uncertain due to a lack of cooperation between the PUs and the SUs. Under this circumstance, it is preferable to allow the interference threshold of each PU to be violated occasionally as long as such violation stays below a probability. In this article, we employ Chance-Constrained Programming (CCP) to exploit this idea of occasional interference threshold violation. We assume the uncertain channel gains are only known by their mean and covariance. These quantities are slow-changing and easy to estimate. Our main contribution is to introduce a novel and powerful mathematical tool called Exact Conic Reformulation (ECR), which reformulates the intractable chance constraints into tractable convex constraints. Further, ECR guarantees an equivalent reformulation from linear chance constraints into deterministic conic constraints without the limitations associated with Bernstein Approximation, on which our research community has been fixated on for years. Through extensive simulations, we show that our proposed solution offers a significant improvement over existing approaches in terms of performance and ability to handle channel correlations (where Bernstein Approximation is no longer applicable).

Hybrid beamforming (HB) architecture has been widely recognized as the most promising solution to mmWave MIMO systems. A major practical challenge for HB is to obtain a solution in ~1 ms, which is an extremely stringent but necessary time requirement for its deployment in the field. In this paper, we present the design and implementation of Turbo-HB, codename for a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 1 ms. The key ideas in Turbo-HB include (i) reducing the complexity of SVD techniques by exploiting the limited number of channel paths at mmWave frequencies, and (ii) designing and implementing a parallelizable algorithm for a large number of matrix transformations. We validate Turbo-HB by implementing it on an off-the-shelf Nvidia GPU. Through extensive experiments, we show that Turbo-HB can meet ~1 ms timing requirement while delivering competitive throughput performance compared to state-of-the-art algorithms.

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when each source node has an AoI requirement (which we call Maximum AoI Threshold (MAT)). Specifically, we want to determine whether or not a vector of MATs for the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure called Cyclic Scheduler Detection (CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, called Fictitious Polynomial Mapping (FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under ln 2. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm.

This article investigates the dynamic multiplexing of enhanced mobile broadband (eMBB) and ultrareliable and low latency communications (URLLC) on the same channel in 5G NR. Due to significant difference in transmission time scale, URLLC employs a preemptive puncturing technique to multiplex its traffic onto eMBB traffic for transmission. The optimization problem to solve is to minimize the adverse impact of such preemptive puncturing on eMBB users. We present DEMUX - a model-free deep reinforcement learning (DRL)-based solution to this problem. The essence of DEMUX is to use deep function approximators (neural networks) to learn an optimal algorithm for determining the preemption solution in each eMBB transmission time interval (TTI). Our novel contributions in the design of DEMUX include the first use of the DRL method with a large and continuous action domain for resource scheduling in NR, a mechanism to ensure fast and stable learning convergence by exploiting the intrinsic properties of the problem, and a mechanism to obtain a feasible preemption solution from the unconstrained output of a neural network while minimizing loss of information. The experimental results show that DEMUX significantly outperforms state-of-the-art algorithms proposed in the 3GPP standards body and the literature.

Underlay coexistence is an effective mechanism to improve spectrum effïciency by having picocells coexist with macrocell on the same spectrum. Due to a lack of cooperation between the primary users (PUs) in the macrocell and secondary users (SUs) in the picocell, it is impossible to have complete knowledge of channel gains between them. Under such circumstance, chance-constrained programming (CCP) is shown to be the ideal optimization tool to address such uncertainty. However, solutions to CCP are computationally intensive and cannot meet 5G's timing requirement. To address this problem, we propose a novel scheduler called GUC (stands for GPU-based Underlay Coexistence) to find an approximate solution to CCP in real-time. The essence of GUC is to decompose the original optimization problem into a large number of small problems that are suitable for parallel computation on GPU platforms. Through extensive experiments, we show that GUC reduces the scheduling computation time by at least 10,000 times comparing to commercial solvers (on CPU) while achieving an average of 90% optimality.

Age of information (AoI) is a powerful new metric to quantify the freshness of information and has gained increasing popularity in IoT applications. Existing models on AoI remain primitive and do not consider state-of-the-art transmission technologies such as 5G. They also fail to consider the impact of dynamic channel conditions. In this paper, we present Kronos, a 5G-compliant AoI scheduling algorithm that can cope with highly dynamic channel conditions. Kronos is capable of performing RB allocation and selecting MCS for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we propose a GPU-based implementation of Kronos on low-cost offthe-shelf GPUs. Through simulations and experiments, we show that Kronos can find near-optimal AoI scheduling solutions in sub-millisecond time scale. To the best of our knowledge, this is the first 5G-compliant real-time AoI scheduler that can cope with dynamic channel conditions.

Degree-of-Freedom (DoF) based models have been widely used to study MIMO networks. To cancel interference, the number of DoFs used in the state-of-the-art DoF models is solely based on the number of interfering data streams. However, by decomposing an interference into the eigenspace, we find that signal strengths varies significantly in different directions for the same interference link. In this paper, we exploited the difference in interference signal strength in the eigenspace and differentiate strong and weak interference signals via their singular values. By introducing a concept of effective rank threshold, we propose to use DoFs only to cancel strong interference in the eigenspace based on this threshold while treating weak interference signals as noise in throughput calculation. We explore a fundamental tradeoff between network throughput and effective rank threshold. Using simulation results on MU-MIMO networks, we show that network throughput under optimal rank threshold setting is significantly higher than that under existing DoF IC models. To ensure feasibility at the PHY layer, we present an algorithm that can find Tx and Rx weights at each node that can offer our desired DoF allocation.

Recently, a new metric, called Age of Information (AoI), has become popular to quantify the freshness of information collected at network edge. AoI research is still in its infancy and most prior efforts assume overly simplified models in their investigation. In this paper, we consider a more general model for AoI research that is closer to what happens in the real world. Specifically, we consider general and heterogeneous sampling behaviors among source nodes, varying sample size, and multiple data transmission units in each time slot. Under this much general setting, we develop new theoretical results (in terms of properties and performance bounds) and a new near-optimal low-complexity scheduling algorithm. Our results make a major advance of AoI research in terms of more realistic models.

In underlay coexistence, secondary users (SUs) attempt to keep their interference to the primary users (PUs) under a threshold. Due to the absence of cooperation from the PUs, there exists much uncertainty at the SUs in terms of channel state information (CSI). An effective approach to cope such uncertainty is to introduce occasional interference threshold violation by the SUs, as long as such occasional violation can be tolerated by the PUs. This paper exploits this idea through a chance constrained programming (CCP) formulation, where the knowledge of uncertain CSI is limited to only the first and second order statistics rather than its complete distribution information. Our main contribution is the introduction of a novel and powerful technique, called Exact Conic Reformulation (ECR), to reformulate the intractable chance constraints. ECR guarantees an equivalent reformulation for linear chance constraints into deterministic conic constraints and does not suffer from the limitations associated with the state-of-the-art approach -- Bernstein Approximation. Simulation results confirm that ECR offers significant performance improvement over Bernstein Approximation in uncorrelated channels and a competitive solution in correlated channels (where Bernstein Approximation is no longer applicable).

In recent years we have witnessed the rapid development of smart bicycles. For example, Mobike1 is able to interact with smartphones. As we all known, accurate bicycle localization system is one of the most critical technologies for the development of smart bicycles. However, GPS's error is at meter-level and it performs poorly under skyscrapers and in tunnels.