Queue State Information Research Articles

Decentralized DNN Task Partitioning and Offloading Control in MEC Systems With Energy Harvesting Devices

In this paper, we study a decentralized deep neural network (DNN) task partitioning and offloading control problem for a multi-access edge computing (MEC) system with multiple wireless devices (WDs) powered by renewable energy sources. Instead of generic computational tasks, we focus on the case when the WDs and the MEC systems are computing DNN inferencing tasks. For each WD, we build a virtual local-computing CPU workload queue and an energy queue to model the DNN computation dynamics and the battery energy dynamics, respectively. We first introduce a new Lyapunov function based on the DNN tasks' cumulative execution latency and the total energy consumptions of the entire MEC system. Using a Lyapunov optimization approach, the DNN task partitioning and offloading control at the WDs are formulated as a Lyapunov drift minimization problem. To facilitate a distributed implementation, we exploit the linear structure of the cumulative latency and the independency of each WD's local-computing CPU rate, and we decompose the centralized Lyapunov drift minimization problem into multiple distributed subproblems, each of which is associated with a single WD. Next, we develop a parametric online learning algorithm such that each sub-Lyapunov drift minimization problem is solved locally at its associated WD. The proposed solution is completely decentralized in the sense that the sequential offloading and DNN task segmentation control at each WD is determined by its local battery energy queue state information (EQSI), the channel state information (CSI), and the DNN inferencing task workload queue state information (TQSI). Numerical results reveal that the proposed online learning algorithm can achieve significant performance gain over various state-of-the-art baselines.

Impatient Queuing for Intelligent Task Offloading in Multiaccess Edge Computing

Multi-access edge computing (MEC) emerges as an essential part of the upcoming Fifth Generation (5G) and future beyond-5G mobile communication systems. It adds computational power towards the edge of cellular networks, much closer to energy-constrained user devices, and therewith allows the users to offload tasks to the edge computing nodes for low-latency applications with very-limited battery consumption. However, due to the high dynamics of user demand and server load, task congestion may occur at the edge nodes resulting in long queuing delay. Such delays can significantly degrade the quality of experience (QoE) of some latency-sensitive applications, raise the risk of service outage, and cannot be efficiently resolved by conventional queue management solutions. In this article, we study a latency-outage critical scenario, where users intend to limit the risk of latency outage. We propose an impatience-based queuing strategy for such users to intelligently choose between MEC offloading and local computation, allowing them to rationally renege from the task queue. The proposed approach is demonstrated by numerical simulations to be efficient for generic service model, when a perfect queue status information is available. For the practical case where the users obtain only imperfect queue status information, we design an optimal online learning strategy to enable its application in Poisson service scenarios.

Mixed-Timescale Request-Driven User Association, Trajectory and Radio Resource Control for Cache-Enabled Multi-UAV Networks

Nowadays, unmanned aerial vehicles (UAVs) are being utilized to extend the coverage and capacity of terrestrial wireless networks by exploiting the more favorable line-of-sight (LOS) propagation. The joint optimization of UAV trajectory, user association and radio resource control remains unsolved under a realistic channel model and the more strict QoS requirement in UAV caching networks. Hence, we propose a two-timescale request-driven user association as well as UAVs trajectory and radio resource optimization by exploiting multi-UAV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$multicasting$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$opportunities$</tex-math></inline-formula> . The short-term UAV trajectory and radio resource control is adaptive to fast-timescale processes such as channel state information (CSI), queue state information (QSI) and energy state information (ESI), while the long-term user association is adaptive to slow-timescale processes such as the user request profile and user mobility. Based on the timescale decomposition, we derive an online reinforcement learning algorithm to obtain the structural short term optimal policy for the fast-timescale sub-problem, and a parameterized policy gradient learning algorithm to obtain the long term optimal policy for the slow-timescale sub-problem. Finally, simulation results compare the proposed solution to the existing baselines and it is shown that the proposed one can achieve significant gains over all the baselines.

WITHDRAWN: Effective adaptive routing for Lossy networks using enhanced RPL in the heterogeneous network

Abstract In this network environment, the different applications of sensor have to generate a huge data traffic which includes basic sensing methods such as air pollution or environment checking. In this huge congested network, the two-standard objective methods, that are objective method 0 (OM0) and objective method 1 OM1 for Congested Networks that has to perform deprived routing paths by picking an already congested parental node and a greater number of retransmissions happened between the networks. So, it is required that to design a new effective Routing Protocol for various congested environments. In our paper, I observe the RPL protocol for various congested networks and developed a new routing protocol technique (QWL-RPL) related with information of queue and node-wise workload. Objective of our developed Routing protocol has to attain an Optimum shortest path in challengeable performance. Our proposed Objective method has to consider connectivity capacity and also representing status of congestion in particular node or device using information of queue in data or packet. Our proposed routing technique was implemented in Cooja environment for comparing with the existing techniques. The results of simulation are shown through Queue status information and balanced Workload routing protocol of RPL that can reduce the data traffic of a various networks as related with OM0 and OM1, exactly specified terms of average delay, ratio of packets transmission and reception, jitter delay and header overhead information. At last, the results shown as on average, 30%–50% reduced header overhead information, 15%–30% reduced in average throughput delay, 10%–30% improvement in PRR and 25%–40% reduced in jitter delay.

A Cross-Layer Design for Dynamic Resource Management of VLC Networks

In this article, we propose a cross-layer resource management mechanism for an indoor multiuser visible light communication (VLC) access network. The physical layer builds upon DC-biased orthogonal frequency division multiple access. Channel state information at the physical layer serves as a performance indicator for better data rate opportunity while queue state information at medium access control layer provides the information about packet urgency. A packet based traffic arrival model is considered such that incoming packets with random inter arrival times join a separate queue dedicated to each user if the admission control accepts them, otherwise packets are dropped. We formulate and solve a stochastic cross-layer optimization problem to optimize the network resources under the constraints of queue stability and power to maximize the average system throughput. By using the Lyapunov optimization technique, we convert the long term time-average optimization problem into series of single time slot online problem. Then, we decompose the problem into independent sub problems and propose packet admission, resource (access point and subcarrier) allocation and power control solutions. We present simulation results to demonstrate the superiority of proposed scheme over existing solutions in the literature.

Reduced-State, Optimal Scheduling for Decentralized Medium Access Control of a Class of Wireless Networks

Motivated by medium access control for resource-challenged wireless Internet of Things (IoT), we consider the problem of queue scheduling with reduced queue state information. In particular, we consider a time-slotted scheduling model with $N$ sensor nodes, with pair-wise dependence, such that Nodes $i$ and $i + 1,~0 < i < N$ cannot transmit together. We develop new throughput-optimal scheduling policies requiring only the empty-nonempty state of each queue that we term Queue Nonemptiness-Based (QNB) policies. We propose a Policy Splicing technique to combine scheduling policies for small networks in order to construct throughput-optimal policies for larger networks, some of which also aim for low delay. For $N = 3,$ there exists a sum-queue length optimal QNB scheduling policy. We show, however, that for $N > 4,$ there is no QNB policy that is sum-queue length optimal over all arrival rate vectors in the capacity region. We then extend our results to a more general class of interference constraints that we call cluster-of-cliques (CoC) conflict graphs. We consider two types of CoC networks, namely, Linear Arrays of Cliques (LAoC) and Star-of-Cliques (SoC) networks. We develop QNB policies for these classes of networks, study their stability and delay properties, and propose and analyze techniques to reduce the amount of state information to be disseminated across the network for scheduling. In the SoC setting, we propose a throughput-optimal policy that only uses information that nodes in the network can glean by sensing activity (or lack thereof) on the channel. Our throughput-optimality results rely on two new arguments: a Lyapunov drift lemma specially adapted to policies that are queue length-agnostic, and a priority queueing analysis for showing strong stability.

Pilot-Efficient Scheduling for Large-Scale Antenna Aided Massive Machine-Type Communications: A Cross-Layer Approach

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.

LaSR: A Supple Multi-Connectivity Scheduler for Multi-RAT OFDMA Systems

Network densification over space and spectrum is expected to be key to enabling the requirements of next generation mobile systems. The pitfall is that radio resource allocation becomes substantially more complex. In this paper, we propose LaSR, a practical multi-connectivity scheduler for OFDMA-based multi-RAT systems. LaSR makes optimal discrete control actions by solving a sequence of simple optimization problems that do not require prior information of traffic patterns. In marked contrast to previous work, the flexibility of our approach allows us to construct scheduling policies that achieve a good balance between system cost and utility satisfaction, while jointly operate across heterogeneous RATs, accommodate real-system requirements, and guarantee system stability. Examples of system requirements considered in this paper include (but are not limited to): constraints on how scheduling data can be encoded onto signaling protocols (e.g., LTE's DCI), delays when turning on/off radio units, or on/off cycles when using unlicensed spectrum. We evaluate our scheduler via a thorough simulation campaign in a variety of scenarios with e.g., mobile users, RATs using unlicensed spectrum (using a duty cycle access mechanism), imperfect queue state information, and constrained signaling protocol.

Dynamic Power Allocation and User Scheduling for Power-Efficient and Delay-Constrained Multiple Access Networks

In this paper, we propose a joint dynamic power control and user pairing algorithm for power-efficient and low-latency hybrid multiple access systems. In a hybrid multiple access system, user pairing determines whether the transmitter should serve a certain user by orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA). The proposed optimization framework minimizes the long-term time-average transmit power expenditure while reducing the queueing delay and satisfying time-average data rate requirements. The proposed technique observes channel and queue state information and adjusts queue backlogs to avoid an excessive queueing delay by appropriate user pairing and power allocation. Further, user scheduling for determining the activation of a given user link as well as flexible use of resources are captured in the proposed algorithm. Data-intensive simulation results show that the proposed scheme guarantees an end-to-end delay smaller than 1 ms with high power-efficiency and high reliability, based on the short frame structure designed for ultra-reliable low-latency communications (URLLC).

Closed-Form Delay-Optimal Computation Offloading in Mobile Edge Computing Systems

Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependences and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP) and approximate it to a virtual continuous time system (VCTS) with reflections. Different from most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closed-form approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). Furthermore, we discuss the extension of our proposed scheme to multi-MT multi-server scenarios. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.

Joint Rate and Power Optimization for Multimedia Streaming in Wireless Fading Channels via Parametric Policy Gradient

In this paper, we consider the two-timescale queueaware resource control for wireless video streaming applications. By applying a singular perturbation approach, we study a twotimescale joint optimization of transmission power and streaming rate control for real-time video streaming. The timescale parameter e is introduced to separate the state variables into slow and fast varying groups, and the power control is adaptive to both the channel state information and the playback queue state information (QSI) at the fast timescale, while the streaming rate representing the video quality is only adaptive to the QSI at the slow timescale. We first derive the averaged system by taking the perturbation parameter e → 0, and the limiting problem is a firm approximation for the formal problem with small e. Based on the averaged system, we derive the Hamilton-Jacobi-Bellman equation and show that the optimal control policy has a structural property which can further help us to simplify the problem. We use the parametric policy reinforcement learning algorithm to obtain the optimal parameterized policy, and show the algorithm convergence result. Finally, the proposed scheme's performance is compared with various baselines through simulations and it is shown that significant gain can be achieved.

Delay-aware spectrum sharing solutions for mixed cellular and D2D links

In this paper, we dynamically determine the spectrum share of cellular and overlay device to device communication (D2D) links to improve queuing delay performance. Along changing the portion of resource blocks (RBs) dedicated to each cellular and D2D network, we assign the required power and RBs to each link. We consider a cross-layer optimization problem taking channel state information (CSI) and queue state information (QSI) into account. Two spectrum access mechanisms of overlay D2D mode, known as orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are investigated. First, the dynamic spectrum sharing and power assignment problem is formulated as a general stochastic problem, then it is transformed into a deterministic one using Lyapunov optimization. As the cellular and D2D links should be scheduled prior to each frame transmission, low-complexity approaches are necessary to deal with the dynamic nature of the optimization problem. Thereby, we obtain closed-form-iterated solutions for OMA and NOMA with low design complexity because they do not need to know the probability functions of the channel state and traffic arrival process. Furthermore, for computational complexity reduction, a linear-complexity heuristic approach is proposed. Numerical results demonstrate that the proposed dynamic spectrum sharing approaches outperform static approaches in terms of average delay and average power. In addition, the performance of dynamic NOMA is superior to the dynamic OMA approach at the expense of higher computational complexity which can be compensated by the heuristic approach with a small power computation increase.

Delay Scaling in Many-Sources Wireless Networks without Queue State Information

Delay Scaling in Many-Sources Wireless Networks without Queue State Information

Cross-Layer Optimization &amp;amp; VGALA Structure for 5G Grid

Heterogeneous cloud radio access network (HCRAN) promises higher energy efficiency (EE) than the conservative cellular networks by centralizing the baseband signal processing into the baseband unit (BBU) pool hosted by cloud computing platforms. In existing energy-efficient networking mechanisms designed for conservative cellular networks cannot fully leverage the parameters like latency, load balancing, power consumption and the network energy consumption. In this work ,Cross-layer based VGALA optimization structure is proposed which endeavours a balance between network utilities. The average traffic delivery latency, green energy utilization are techniques used to optimize the packet dropping policy, power allocation policy and bandwidth allocation policy to minimize the transmit power under the QoS constraint. The optimal solution is obtained, depends upon both channel and queue state information. The final result show that recommended framework empowers an adaptable trade off among the on grid power consumption and the normal traffic latency. And finally saves a significant amount of on-grid power and the optimal solution obtained depends on both channel and queue state information. It also shows that setting the three packet loss probabilities as equal causes marginal power loss caused in H-CRAN design.

A QoE-Oriented Scheduling Scheme for Energy-Efficient Computation Offloading in UAV Cloud System

Air ground integrated mobile cloud computing (MCC) provides UAVs with more flexibility and resilience from the cloud computing architecture. However, the increasing aerial mobile data requires heterogeneous quality of experience (QoE) for aerial accessing network. In addition, for the persistent flying, energy efficiency during the computation offloading should also be under consideration. This paper proposes an energy-efficient resource allocation scheme with the ability of QoE enhancement. Various aerial offloading data with different QoE requirements is stored and relayed in the multi-queueing architecture. Hence offloading rate differentiation is utilized to ensure the high-priority data a better QoE. The satisfaction function is designed with respect to energy efficiency and actual performance experienced by UAV. By using the Lyapunov optimization technique, the problem can be decoupled into two independent sub-problems. The first one is rate control associated with multi-queueing architecture in ground base-station (GBS) that manages the aerial offloading data from the UAVs according to the queue state information. The second one is resource allocation associated with the strategy of subcarrier assignment and power allocation according to the channel state information. The experiments demonstrate the algorithm has great properties such as maximization of the UAVs' satisfaction, the reliable heterogeneous QoE support and enhancement of the UAVs' transmission energy efficiency.

Energy-Efficient Cross-Layer Resource Allocation for Heterogeneous Wireless Access

In this paper, an uplink cross-layer resource allocation problem based on imperfect channel state information (CSI) is modeled as min-max fractional stochastic programming for heterogeneous wireless access. The resource allocation is subject to constraints in delay, service outage probability, system radio bandwidth, and total power consumption. The joint bandwidth and power allocations are based on CSI at the physical layer and queue state information (QSI) at the link layer. In order to determine the transmission rate of each mobile terminal according to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay in terms of a required transmission rate is presented based on M/D/1 model. Then, the bandwidth and power allocation problem is transformed into bi-convex programming, and an optimal distributed bandwidth and power allocation algorithm is proposed. To reduce computational complexity, a suboptimal distributed bandwidth and power allocation algorithm is presented. Simulation results demonstrate that the proposed algorithms improve the energy efficiency greatly.

Delay Scaling in Many-Sources Wireless Networks without Queue State Information

We examine a canonical scenario where several wireless data sources generate sporadic delay-sensitive messages that need to be transmitted to a common access point. The access point operates in a time-slotted fashion, and can instruct the various sources in each slot with what probability to transmit a message, if they have any. When several sources transmit simultaneously, the access point can detect a collision, but is unable to infer the identities of the sources involved. While the access point can use the channel activity observations to obtain estimates of the queue states at the various sources, it does not have any explicit queue length information otherwise. We explore the achievable delay performance in a regime where the number of sources n grows large while the relative load remains fixed. This scaling is particularly pertinent in Internet of Things (IoT) scenarios, where a key challenge is to achieve low delay when the overall traffic activity is dispersed across massive numbers of highly intermittent sources. We establish that, under any medium access algorithm without queue state information, the average delay must be at least of the order of n slots when the load exceeds some threshold λ* &lt; 1$. This demonstrates that bounded delay can only be achieved if a positive fraction of the system capacity is sacrificed. Furthermore, we introduce a scalable Two-Phase algorithm for low-delay IoT applications which achieves a delay upper bounded uniformly in n when the load is below e -1 , and a delay of the order of n slots when the load is between e -1 and 1. Additionally, this algorithm provides robustness against correlated source activity, which is also prevalent in IoT scenarios.

Delay Scaling in Many-Sources Wireless Networks without Queue State Information

We examine a canonical scenario where several wireless data sources generate sporadic delay-sensitive messages that need to be transmitted to a common access point. The access point operates in a time-slotted fashion, and can instruct the various sources in each slot with what probability to transmit a message, if they have any. When several sources transmit simultaneously, the access point can detect a collision, but is unable to infer the identities of the sources involved. While the access point can use the channel activity observations to obtain estimates of the queue states at the various sources, it does not have any explicit queue length information otherwise. We explore the achievable delay performance in a regime where the number of sources n grows large while the relative load remains fixed. We establish that, under any medium access algorithm without queue state information, the average delay must be at least of the order of n slots when the load exceeds some threshold lambda* &lt; 1. This demonstrates that bounded delay can only be achieved if a positive fraction of the system capacity is sacrificed. Furthermore, we introduce a scalable Two-Phase algorithm which achieves a delay upper bounded uniformly in n when the load is below e -1 , and a delay of the order of n slots when the load is between e -1 and 1. Additionally, this algorithm provides robustness against correlated source activity.

Wireless Power Transfer and Data Collection in Wireless Sensor Networks

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using wireless power transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, whereas the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semidecentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.

Security-Aware Resource Allocation With Delay Constraint for NOMA-Based Cognitive Radio Network

In this paper, a downlink security-aware resource allocation problem with delay constraint via spectrum sensing is modeled as a mixed integer non-linear problem for non-orthogonal multiple access-based cognitive radio network. The security-aware resource allocation is subject to constraints in required delay for each secondary user, maximum number of accessed secondary users at each subchannel, total interference power threshold introduced to primary users, and total power consumption at secondary BS. The security-aware resource allocation is based on channel state information at the physical layer and queue state information at the link layer. According to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay based on M/D/1 queuing model is analyzed in terms of a required minimum secrecy transmission rate. Then, a secondary user scheduling problem and a power allocation problem are solved, separately. Finally, the secondary user scheduling problem is solved via greedy algorithm, and a power allocation algorithm is proposed by successive convex approximation method. The simulation results demonstrate that the performance of proposed algorithms can be improved significantly.