Wireless caching has attracted substantial attention due to the potential for reducing service delays. However, it will waste energy if the cached content item is not requested by users. In order to improve the energy efficiency of wireless caching, we present a socially motivated wireless caching strategy from the cross-domain perspective in this paper. To this end, we first study the evolutionary requesting model based on users’ preference and their social ties. Then we derive the approximated expression for the delay-outage probability with a given power allocation strategy over Rayleigh block fading channels based on the saddle point technology. Based on the derived expression, a two-layer binary search method is presented to determine a suitable caching volume with a given outage probability and a power constraint. After that, we formulate a cross-layer optimization problem to jointly allocate the storage and transmission resources. By solving this optimization problem, we can determine the caching strategy to minimize the power consumption while promising the hit ratio performance. In addition, we extend the energy-efficient caching system over additive white Gaussian noise channels. Finally, numerical results are presented to demonstrate the potential of the presented caching strategy.

High reliability is crucial for stable and accurate control in wireless Internet of Things (IoT) systems. Although average outage probability is a widely adopted metric for evaluating the reliability of wireless IoT systems, it does not account for the sparsity of outage occurrences, which can be interpreted as the frequency of outage occurrence within a certain time. Compared to an isolated single outage, clustered outages can significantly impact stability. To address this problem, we introduce the concepts of virtual queue and consecutively effective throughput. The virtual queue treats outage packets as arrivals, with its service rate determined by the desired frequency of the single outage. In contrast to the traditional throughput, consecutively effective throughput measures the effectiveness of consecutive success of transmissions. Based on these concepts, we then consider maximizing the consecutively effective throughput under virtual queue constraints. Theoretical analysis is conducted to derive the optimal rate for this problem. Specifically, we explore two scenarios: 1) outages caused by packet decoding errors and 2) outages due to packet decoding errors and latency violations. Considering the nonasymptotic property of the virtual queue, queueing theory is utilized to provide closed-form expressions for virtual queue constraints. Based on the theoretical analysis, efficient and effective algorithms are proposed to obtain the optimal rate based on the above analysis. Numerical comparisons between grid searches and our algorithms validate the correctness of our theoretical analysis. This study underscores the importance of specific scheduling designs to control clustered outages, rather than merely enhancing throughput in wireless IoT systems.

Passive Internet-of-Things (IoT), as a new communication technology, is expected to enable massive device connectivity in a cost-effective and energy-efficient manner. One of the most important topics on Passive IoT is to find a suitable waveform that can be affordable for a wide range of IoT devices. In this paper, we propose a novel waveform design that leverages a low-power on-off keying (OOK) wave. This design cleverly reuses the orthogonal frequency division multiplexing (OFDM) signal generated by the legacy 5G base station, i.e., gNodeB (gNB). Additionally, we enhance the OOK waveform using a central part repetition scheme to acquire frequency diversity gains, thereby improving its resilience against fading channels in realistic environments. Thanks to the repetition being applied only to the central part, no additional bandwidth resources are needed to support the low-power waveform. Simulation results are provided to validate the effectiveness of our approach, demonstrating significant potential for low-power transmissions in passive IoT.

Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal- to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the metric for sensing and communication, where researchers have proposed to utilize mutual information (MI) to measure the sensing performance with deterministic signals. However, the need to communicate in ISAC systems necessitates the use of random signals for sensing applications and the closed-form evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper investigates the SMI and precoder design for sensing applications with random signals. For that purpose, we first derive the closed-form expression for the SMI with random signals by utilizing random matrix theory. The result reveals some interesting physical insights regarding the relation between the SMI with deterministic and random signals. The derived SMI is then utilized to optimize the precoder by leveraging a manifold-based optimization approach. The accuracy of the theoretical analysis and the effectiveness of the proposed precoder design method are validated by simulation results.

Integrated sensing and communication (ISAC) has been treated as a key technology for providing high-quality performance on both communication and sensing with higher spectrum efficiency and lower hardware cost. Among the research on ISAC, joint Radar and communication (JRC) is one of the typical scenarios. In this paper, we focus on designing the optimal JRC scheduling policy and characterizing the optimal tradeoff between the performance of communication and detection in a time-sharing JRC system. To this end, an optimization problem of minimizing the average delay with the constraints on the average power and detection probability is proposed. With the help of the constrained Markov Decision Process (CMDP) and linear programming (LP), the optimal cross-layer JRC scheduling policy is presented, which also reveals the optimal tradeoff between the performance of the communication and sensing in this JRC system. Moreover, we demonstrate the condition for detection scheduling not occupying the time resources ought to be allocated to send data in the communication-centric system. When this condition holds, we call this JRC system achieves ‘sensing for free’. For the JRC system with this property, the complexity of scheduling design can be reduced while the time resources can be utilized more efficiently.

Integrated sensing and communication (ISAC) has been treated as a key technology for providing high-quality performance on both communication and sensing with higher spectrum efficiency and lower hardware cost. Among the research on ISAC, joint radar and communication (JRC) is one of the typical scenarios. Due to remaining challenges on the design for full-duplex hardware and interference depression, time-division JRC is believed as the first step toward ISAC by switching communication and sensing functions in the time domain. In this article, we focus on designing the optimal cross-layer JRC scheduling policy and characterizing the optimal tradeoff between the performance of communication and sensing in a time-division JRC system with a buffer. For both active and passive radar detection scenarios. To this end, optimization problems of minimizing the average delay with different constraints on the average power and detection performance are formulated. With the help of constrained Markov decision process (CMDP) and linear programming (LP), optimal JRC scheduling policies are presented, which also reveal the optimal tradeoff between the performance of the communication and sensing. Moreover, we summarize the condition for sensing scheduling not occupying the time resources which ought to be allocated for communication in a communication-centric system. When this condition holds, we say this JRC system achievessensing for free. Based on the analysis ofsensing for freeand the investigation of numerical results, a heuristic policy is proposed for JRC systems with passive radar detection. Some insights are obtained into the scheduling design of time-division JRC systems.

Millimeter-wave (mmWave) communications have attracted substantial attention due to their potential to provide very large bandwidths. Unfortunately, the propagation of millimeter waves suffers from severe path loss and blocking, which limits the coverage of mmWave communication systems. To overcome this, mmWave hotspot empowered two-tier heterogeneous networks are expected to play an important role in the sixth generation (6G) systems. When the deployment of mmWave hotspots is not dense enough, or even sparse, assuring the quality of service (QoS) for mobile users becomes rather challenging. In this paper, we investigate pushing in two-tier heterogeneous networks with mmWave hotspots, in which popular content items are cached by a mobile user when they can be served by a mmWave hotspot. To this end, a unified framework is presented to analyze and optimize the effective throughput of pushing. Based on the effective throughput analysis, pushing policies with different mobility models and/or mmWave hotspot distributions are presented. Both theoretical and numerical results demonstrate the substantial caching gain due to user mobility in mmWave hotspot empowered two-tier networks.

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted significant attention in the envisioned sixth-generation mobile systems (6G). The scheduling of back-logged queues in URLLC is a challenging issue worthy of researchers and engineers to study. Lyapunov optimization theory is regarded as a promising tool to design low-complexity scheduling strategies. However, it is difficult to analyze the tradeoff between the transmission delay and the transmission power with Lyapunov-based scheduling strategies. To solve this problem, we present an approach based on the Markov analysis in this paper. Specifically, we first develop one low-complexity scheduling strategy with heavy traffic based on the Lyapunov optimization theory. Then, uniform quantization is used to quantize the scheduling strategies so that the upper bound and lower bound of transmission delay can be calculated based on the Markov analysis. After that, we further extend this Markov analysis approach to estimate the transmission delay of another Lyapunov-based scheduling strategy with the consideration of a virtual power queue. Finally, simulation results not only verify our theoretical derivation but also demonstrate the potential of the Lyapunov-based scheduling strategies.

Millimeter wave (mmWave) communications has attracted significant attention due to its potential for providing very large bandwidths. Unfortunately, the propagation of millimeter waves suffers from severe path loss and blocking, which limits the coverage of mmWave systems. To overcome this, mmWave hotspot empowered two-tier heterogeneous networks are expected to play an important role in the sixth generation (6G) of mobile communication systems. When the deployment of mmWave hotspots is not dense enough, or even sparse, assuring the quality of service (QoS) for mobile users becomes rather challenging. In this paper, we present a mobility-enabled pushing scheme, in which popular content items are cached by a mobile user when he/she can be served by an mmWave hotspot. Optimal pushing policies with statistical mobility models and predeter-mined trajectories are presented and analyzed respectively. Both theoretical and numerical results demonstrate the substantial caching gain due to user mobility in mmWave hotspot empowered two-tier networks.

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

The coronavirus disease 2019 (COVID-19) has recently attracted extensive attention due to its serious impact on public health worldwide. In this paper, we study and verify that the popularity of virus-related content has a negative correlation with the epidemic spread by means of statistical analysis. Inspired by this result, a practical solution of recommender system is proposed for pushing virus-related content, aiming to gain insight about the newly discovered virus for people and thus reduce the epidemic spread to the utmost extent. First, we formulate the optimization of recommendation policy subject to quality of experience (QoE) loss constraints as a finite-horizon Constrained Markov Decision Problem (CMDP). To solve this problem, then, we present both enumeration and heuristic methods, from perspectives of achieving optimal recommendation policy and reducing computational complexity, respectively. Finally, our simulations validate the benefit of our solution by showing that to recommend virus-related content following our strategy does help slow down the spread of the epidemic.

Large-Scale Antenna System (LSAS) has played an important role in the emerging fifth-generation mobile systems (5G) due to its potential for excellent spectral efficiency. However, it may cause a mass of pilot overhead that is not conducive to the application of LSAS in massive Machine-Type Communications (mMTC), one of three typical traffic modes of 5G. In this paper, we present a pilot-efficient scheduling strategy for mMTC systems, in which the Base Stations (BS) are equipped with large-scale antennas, from a cross-layer perspective. Our scheme can not only schedule the massive devices to access the spectrum, but also allocate the BS' power without the need for much pilot overhead. More particularly, the users allowed to access the spectrum can be selected based on their queue state information without any channel estimations, while the power allocation only needs the channel estimations for scheduled users. We shall show the optimality of the presented policy based on the Lyapunov optimization theory. To solve the Lyapunov optimization problem, we present a low complexity two-layer iteration algorithm for more practical purposes. Simulation results demonstrate the substantial gain of our presented method over existing scheduling protocols of massive MIMO.

Massive multiple input and multiple output (massive MIMO) has attracted wide attention since it holds the promise of providing excellent spectral efficiency. One of the basic issues of single-cell multi-user massive MIMO system is how to design efficient scheduling methods under the quality-of-service (QoS) requirements. In this paper, we present a joint channel and queue aware scheduling method based on Lyapunov optimization theory. Specifically, the channel gain can be estimated by default according to the characteristic of channel hardening so that we can jointly optimize the user selection and power allocation. What remains to be done is only to measure the direction between the base station and the active users, which is in contrast to the conventional process. To this end, we formulate a cross-layer control problem, which is a mixed integer nonlinear programming problem. To obtain the optimal solutions to this problem efficiently, we present a two-layer iteration algorithm. The inner layer is a dynamic programming algorithm based on water-filling in cellars and the outer layer is a low-complexity one-dimensional search algorithm.

Edge caching has attracted great attention recently due to its potential for reducing the service delays and the peak rate demand, especially when the quality-of-service (QoS) and data rate requirements of mobile users are ever increasing. One of the key issues in edge caching is the storage efficiency. To achieve high storage efficiency, we present an edge caching policy with time domain buffer sharing. More particularly, our scheme allows a Base Station (BS) to determine whether and how long a content item should be cached at the buffer of the BS. To this end, we formulate a queue-theoretic model, in which the storage cost can be determined by the maximum caching time of content items via Little's Law. Based on this model, we present a probabilistic caching policy with random maximum caching time to strike the optimal tradeoff between the storage cost and the overall hit ratio of content items. For content items having different popularity, we further investigate how the storage resources should be allocated among these content items. An efficient two-layer iteration algorithm is presented to solve the storage allocation problem, which is a nonconvex optimization problem.