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Most existing optimization objectives considered in non-orthogonal multiple access (NOMA) power allocation schemes are non-delay-sensitive metrics. In order to apply NOMA to various Internet of Things scenarios, the delay must be considered. The effective capacity of users, which characterizes the capacity under specific expiration probabilities, can potentially be a performance metric of statistical delay quality of service (QoS). In this paper, we propose two novel dynamic power allocation schemes with statistical delay QoS guarantee in the uplink NOMA system with paired users. One of the schemes maximizes the sum effective capacity (SEC) of the strong and weak users, which is a non-convex nonlinear optimization problem and is solved by Lagrangian dual decomposition and successive convex approximation (SCA). The other one maximizes the effective energy efficiency (EEE) of uplink NOMA, which is a fractional optimization problem and is solved by integrating the Dinkelbach method, SCA, and Lagrangian dual decomposition. Numerical results show that the SEC and EEE can be significantly improved by the proposed schemes, compared to the existing NOMA and orthogonal multiple access power allocation schemes. [ABSTRACT FROM AUTHOR]

Cooperative caching can unify storage across edge clouds and provide efficient delivery of popular contents under effective content placement. However, the placement and delivery are non-trivial in cooperative caching due to the decentralized property of edge clouds, as well as the temporal and spatial correlation of the placement. We propose a new distributed online learning approach to jointly optimize content placement and delivery without the a-priori knowledge on file popularity and link availability. Content placement and delivery can be asymptotically optimized in real-time by running distributed online learning at individual edge servers by exploiting stochastic gradient descent (SGD). The proposed approach can allow operations at different timescales by integrating mini-batch learning for farsighted content placement. The optimality loss, stemming from the different timescales, can asymptotically reduce, as the SGD stepsize declines. Simulations confirm that the proposed approach outperforms existing techniques in terms of cache hit ratio and cost effectiveness. Insights are shed on the optimal placement of popular contents. [ABSTRACT FROM AUTHOR]

Cooperative caching can unify network storage to improve efficiency, but the effective placement and search of contents are challenging especially in distributed edge clouds with neither a-priori knowledge on content requests nor instantaneous global view. This paper establishes a new profitable cooperative region for every content request admitted at an edge server, within which the content, if cached, can be retrieved with guaranteed profit against a direct retrieval from the network backbone. This narrows down the search for the content. The caching density of the content can also be significantly reduced, e.g., to a cached copy per region. The regions are based on a novel distributed framework which allows individual servers to spontaneously admit/dispatch requests and deliver/forward contents, while asymptotically maximizing the time-average profit of caching. The cooperative region for content is erected at individual servers by comparing the upper and lower bounds for the backlogs of unsatisfied requests of the content. Simulations show the substantially improved profit of the proposed approach over existing solutions. The regions can help automate the placement of contents with reduced density and improved efficiency. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) is promising for increasing connectivity and capacity. But there has been little consideration on the quality of service of NOMA; let alone that in generic fading channels. This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami- $m$ and Rician NOMA channels, by exploiting stochastic network calculus (SNC). The key challenge addressed is to derive the Mellin transforms of the service processes in the NOMA fading channels. The transforms are proved to be stable, and incorporated into the SNC to provide the closed-form upper bounds of the delay violation probability. The paper also applies the Mellin transforms to develop the closed-form expressions for the effective capacity of the NOMA fading channels, which measures the channel capacity under statistical delay guarantees. By further applying the min–max and max–min rules, two new power allocation algorithms are proposed to optimize the closed-form expressions, which can provide the NOMA users fairness in terms of delay violation probability and effective capacity. Simulation results substantiate the derived upper bounds of the delay violation probabilities, and the effective capacity. The proposed power allocation algorithms are also numerically validated. [ABSTRACT FROM AUTHOR]

With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition. [ABSTRACT FROM AUTHOR]

Decentralized orchestration of the control plane is critical to the scalability and reliability of software-defined network (SDN). However, existing orchestrations of SDN are either one-off or centralized, and would be inefficient the presence of temporal and spatial variations in traffic requests. In this paper, a fully distributed orchestration is proposed to minimize the time-average cost of SDN, adapting to the variations. This is achieved by stochastically optimizing the on-demand activation of controllers, adaptive association of controllers and switches, and real-time request processing and dispatching. The proposed approach is able to operate at multiple timescales for activation and association of controllers, and request processing and dispatching, thereby alleviating potential service interruptions caused by orchestration. A new analytic framework is developed to confirm the asymptotic optimality of the proposed approach in the presence of non-negligible signaling delays between controllers. Corroborated from extensive simulations, the proposed approach can save up to 73% the time-average operational cost of SDN, as compared to the existing static orchestration. [ABSTRACT FROM AUTHOR]

By performing fog computing, a device can offload delay-tolerant computationally demanding tasks to its peers for processing, and the results can be returned and aggregated. In distributed wireless networks, the challenges of fog computing include lack of central coordination, selfish behaviors of devices, and multi-hop signaling delays, which can result in outdated network knowledge and prevent effective cooperations beyond one hop. This paper presents a new approach to enable cooperations of $N$ selfish devices over multiple hops, where selfish behaviors are discouraged by a tit-for-tat mechanism. The tit-for-tat incentive of a device is designed to be the gap between the helps (in terms of energy) the device has received and offered; and indicates how much help the device can offer at the next time slot. The tit-for-tat incentives can be evaluated at every device by having all devices broadcast how much help they offered in the past time slot, and used by all devices to schedule task offloading and processing. The approach achieves asymptotic optimality in a fully distributed fashion with a time-complexity of less than $\mathcal {O}(N^{2})$. The optimality loss resulting from multi-hop signaling delays and consequently outdated tit-for-tat incentives is proved to asymptotically diminish. Simulation results show that our approach substantially reduces the time-average energy consumption of the state of the art by 50% and accommodates more tasks, by engaging devices hops away under multi-hop delays. [ABSTRACT FROM AUTHOR]

This article investigates the prominent dilemma between capacity and reliability in heterogeneous ultra-dense distributed networks, and advocates a new measure of effective capacity to quantify the maximum sustainable data rate of a link while preserving the quality of service of the link in such networks. Recent breakthroughs are brought forth in developing the theory of the effective capacity in heterogeneous ultra-dense distributed networks. Potential applications of the effective capacity are demonstrated on the admission control, power control, and resource allocation of such networks, with substantial gains revealed over existing technologies. This new measure is of particular interest to ultra-dense deployment of the emerging 5G wireless networks in the unlicensed spectrum, leveraging the capacity gain brought by the use of the unlicensed band and the stringent reliability sustained by 5G in future heterogeneous network environments. [ABSTRACT FROM AUTHOR]

Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve $(1-\epsilon)$ -approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. $\epsilon $ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an $[\mathcal {O}(\epsilon),\mathcal {O}(1/\epsilon)]$ -tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming. [ABSTRACT FROM AUTHOR]

Plug-and-play femtocells will be an integrating part of future cellular networks. Resource management and interference mitigation become challenging, suffering from severely delayed network control in large-scale deployments. We propose a new game theoretic framework, where fast interference suppression is decoupled from the relatively slow frequency allocation process to tolerate the delayed control. The key idea is to cast femtocell clustering as an outer-loop evolutionary game coupled with bankruptcy channel allocation, which drives the cells to spontaneously switch to less interfered clusters. Within each cluster, we design an inner-loop non-cooperative power control game, such that the requirement of prompt control is eliminated. The two loops interact recursively with analytically confirmed stability. Simulations show that our framework can improve the throughput by 13.2% in a network of 200 cells, compared to the prior art. The gain grows further with the network size. [ABSTRACT FROM PUBLISHER]

Link aggregation, or multi-band striping, has been used in wireless backhaul systems to overcome the limitations of bandwidth and transmission range. Work-conserving and in-sequence delivery are essential but conflicting requirements in multi-band striping systems. Previous schemes can achieve one or the other, but not both. The heterogeneous channel and time varying data rate in multi-band wireless backhaul systems present additional challenges. We aim at designing a multi-band striping algorithm to achieve both work-conserving and in-sequence delivery in the heterogeneous time-varying multi-band wireless backhaul system. We propose a parallel processing architecture for multi-band aggregation, and derive the necessary conditions for such system to achieve sequence preserving and work conserving. An optimum set of timing controls is developed and proved to provide the best performance. We design a work-conserving in-sequence striping algorithm and prove it to be sequence preserving and work conserving. The algorithm is then extended to practical data granularity. Performance analysis and simulation results demonstrate that our designs are able to preserve data sequence, reduce delay, and achieve 100% channel utilization. [ABSTRACT FROM AUTHOR]

Large scale deployment of machine-to-machine (M2M) communication networks hinges on the cost and energy efficient design of the embedded devices. Standard WLAN power save mechanisms, which are designed for human communications, experience performance degradation and unbalanced energy consumptions in M2M communication networks. We develop a novel analytical model that takes into account the fundamentally different network architecture and traffic patterns of M2M communications. Our model accurately characterizes the high contention and long packet delay found in M2M communication networks, while previous models underestimate such measures. To combat such performance decline, we propose a new algorithm that enhances existing power save mechanisms to extend the lifetime of a M2M communication network. We call this the Offset ListenInterval (OLi) Algorithm. The OLi algorithm spreads the M2M traffic evenly with calculated offsets to alleviate network contention and reduce packet delay. Our analytical model is then used to evaluate the energy efficiency of our OLi algorithm and compare with the standard power save mechanisms. Our results show that the proposed OLi algorithm extends the lifetime by up to 40%, and scales up to thousands of nodes in a M2M communication network. [ABSTRACT FROM PUBLISHER]