With the rapid development of the internet-of-things (IoT), the application of IoT terminals (ITs) has been growing exponentially. To address this challenge, ultra-dense networks have been widely regarded as an effective solution. However, under the constraints of task latency and resource limitations, how to achieve the joint offloading and caching of energy efficiency and security remains a critical issue. To address it, we first propose two types of secure collaborative offloading modes for this network framework, i.e., secure collaborative computation offloading with caching and non-caching. Under these two modes, we then strive to minimize the overall local energy consumption (EC) of all ITs, subject to the constraints of computational resources, latency, security cost, and caching capacity. This is achieved by jointly optimizing device association, cache decision-making, channel selection, executing decision-making, power control, secure service allocation, and multi-step task offloading. To solve the formulated nonlinear fractional problem efficiently, we put forward an improved football team training algorithm (IFTTA), which integrates a diversity-guided mutation strategy into the original football team training algorithm (FTTA). Furthermore, we conduct an in-depth analysis of the convergence properties and computational complexity of the proposed algorithm. Simulation results demonstrate that the IFTTA achieves lower total local EC and task-processing delay compared to the FTTA, while satisfying system constraints, and generally outperforms existing state-of-the-art methods.

In industrial internet of things (IIoT) scenarios, the energy efficiency of task offloading is challenged by the quasi-periodic fading of wireless channels and the energy constraints of IIoT devices. To address this, we propose a multi-stage offloading framework, which allows intelligent reflecting surfaces (IRS)-assisted unmanned aerial vehicles (UAV) to dynamically reflect transmitted signals between a small base station (SBS) and a macro base station (MBS), aiming to mitigate inter-tier and cross-tier interference. However, achieving efficient offloading while minimizing energy consumption remains a critical challenge due to the complex interplay between device offloading decisions, IRS phase shift design, subchannel allocation, and power control. To tackle this, we first formulate a mixed-integer nonlinear programming problem based on uplink communication and computational models. Then, an improved escape optimization algorithm (IESC) is developed to solve the problem, which achieves efficient convergence through dynamic solution space exploration. Finally, simulation results demonstrate that our proposed scheme significantly outperforms existing benchmarks in terms of energy efficiency and offloading performance.

Edge content caching of Internet of Vehicles (IoVs) is a key technology for alleviating backhaul strain and reducing access latency. To protect the privacy of vehicular users, Federated learning (FL) is employed by sharing vehicles’ local models instead of data. However, vehicles may leave the coverage range of serving node before completing the local model training. To enhance model aggregation efficiency, asynchronous Federated learning (AFL) is employed, which allows asynchronous aggregation without waiting for all vehicles to update their local models. In practice, the local models are susceptible to malicious tampering during the global aggregation process. To solve this problem, we propose a secure AFL (SAFL) framework by incorporating a Z-score-based weight detection method within AFL. Moreover, To improve caching efficiency and adapt to the highly dynamic IoV environments, we introduce an innovative proactive caching approach by combining a conditional variational autoencoder and generative adversarial network to predict popular contents, thereby improving the cache hit ratio. Additionally, based on the prediction results of popular content, we optimize intelligent decision-making using multiagent deep reinforcement learning (DRL) to reduce the content transmission delay. Extensive simulations are performed based on real-world datasets and experimental results demonstrate that the proposed SAFL and multiagent DRL hybrid technique outperforms other baseline approaches.

Cache-assisted ultradense mobile-edge computing (MEC) networks are a promising solution for meeting the increasing demands of numerous Internet of Things mobile devices (IMDs). To address the complex interferences caused by small base stations (SBSs) deployed densely in such networks, this article exploits the combination of orthogonal frequency-division multiple access (OFDMA), nonorthogonal multiple access (NOMA), and base station (BS) clustering. Additionally, security measures are introduced to protect IMDs’ tasks offloaded to BSs from potential eavesdropping and malicious attacks. Within this network framework, a computation offloading scheme is proposed to minimize IMDs’ energy consumption while considering constraints, such as delay, power, computing resources, and security costs, optimizing channel selections, task execution decisions, device associations, power controls, security service assignments, and computing resource allocations. To solve the formulated problem efficiently, we develop a further improved hierarchical adaptive search (FIHAS) algorithm, providing some insights into its parallel implementation, computation complexity, and convergence. Simulation results demonstrate that the proposed algorithms can achieve lower total energy consumption and delay compared to other algorithms when strict latency and cost constraints are imposed.

Ultradense networks are widely regarded as a promising solution to explosively growing applications of Internet of Things (IoT) mobile devices (IMDs). However, complicated and severe interferences need to be tackled properly in such networks. To this end, both orthogonal multiple access (OMA) and non-OMA (NOMA) are considered under base station (BS) clustering. Then, in order to attain a goal of green and secure computation offloading, under the proportional allocation of computation resources, and the constraints of latency and security cost, joint device association, channel selection, security service assignment, power control, and computation offloading are performed for minimizing the overall energy consumed by all IMDs. It is noteworthy that multistep computation offloading is concentrated to balance the network loads and fully utilize computation resources. Since the finally formulated problem is in a nonlinear mixed-integer form, it may be very difficult to find its closed-form solution. To solve it, an improved whale optimization algorithm (IWOA) is designed. As for this algorithm, the convergence, computation complexity, and parallel implementation are analyzed in detail. Simulation results show that the designed algorithm may achieve lower energy consumption than other existing algorithms under strictly satisfying constraints of latency and security cost.

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices’ latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices’ latency constraints.

The energy reduction for wireless systems becomes more and more important due to its impact on the operation cost and global carbon footprint. In this paper, we design two kinds of energy-efficient association schemes under an open loop power control for uplink heterogeneous cellular networks (HCNs), which are formulated as problems with maximizing sum energy efficiency (EE) and EE utility respectively. In them, the second scheme integrates with the load balancing level and user fairness. Since the first problem is in a simple form, we can easily solve it without any iteration. As for the second problem, we first introduce a dual variable to decouple the constraint and then develop a distributed algorithm using dual decomposition. In addition, we also give some convergence proofs for the proposed algorithms. In the simulation, we investigate the influences of different parameters on the association performance of designed association schemes.

Caching popular contents in the storage of base stations (BSs) has emerged as a promising solution for reducing the transmission latency and providing extra‐high throughput. This study tackles the optimal trade‐off problem between the sum of effective rates and backhaul saving through joint service improvement and content placement in cache‐enabled heterogeneous cellular networks while guaranteeing the quality‐of‐service requirements of all user terminals (UTs) and backhaul traffic constraints of all BSs. However, there exists an intractable issue of mixing the integer nature into the feasible region in the nonlinear optimisation problem. To this end, the authors decompose the optimisation problem into three subproblems by alternately fixing two of three classes of variables (i.e. UT association, power control, and content placement). Aiming at these subproblems, they, respectively, convert them into the tractable forms and propose the corresponding algorithms. By combining them, they propose a three‐tier iterative algorithm for jointly optimising UT association and cache placement. Finally, numerical results have verified the effectiveness of proposed schemes.

This paper investigates the maximal ratio combining (MRC) performance of an amplify and forward (AF) relay system in Nakagami- m fading environments. The study considers a general scenario with distinct m fading parameters for the following three links, source to relay link, and source to destination link and relay to destination link. We derive new closed form expressions for the statistics of important performance metrics, including the moment generating function, outage probability, higher order moments of equivalent signal to noise ratio (SNR), ergodic capacity, and average symbol error probability (SEP) of common modulation types. In particular, we focus on analytical SEP expressions in the context of an additive white generalized Gaussian noise (AWGGN). As an active area of research, generalized noise receives much attention for its flexible model. However, analytical performance of modulation scheme in generalized noise type has not been found in open literature for AF relaying with MRC despite its practical usefulness. Without the help of analytical solutions, the SEP in generalized noise can only be obtained by a large number of repeated simulation experiments. Therefore, we present the general SEP expression by using special Fox’s H function. Simulation results verify the accuracy of our theoretical analysis and show that the diversity order of MRC criterion linearly depends upon Nakagami parameters of three links.

This paper is devoted to an investigation of an exact average symbol error probability (SEP) for amplify and forward (AF) relaying in independent Nakagami‐m fading environments with a nonnegative integer plus one‐half m, which covers many actual scenarios, such as one‐side Gaussian distribution (m = 0.5). Using moment generating function approach, the closed‐form SEP is expressed in the form of Lauricella multivariate hypergeometric function. Four modulation modes are considered: rectangular quadrature amplitude modulation (QAM), M‐ary phase shift keying (MPSK), M‐ary differential phase shift keying (MDPSK), and π/4 differential quaternary phase shift keying (DQPSK). The result is very simple and general for a nonnegative integer plus one‐half m, which covers the same range as integer m. The tightness of theoretical analysis is confirmed by computer simulation results.

This study considers an improved decode and forward (DF) protocol in an orthogonal frequency division multiplexing‐based cooperative system, where the improved DF protocol implies that the source node is allowed to emit the same symbol in the second phase as that in the first phase, irrespective to whether the relay is idle or not. Because rate provision is one of the main design goals in wireless network, the authors construct an optimisation problem to improve the overall sum rate of the system and propose a joint power allocation and subcarrier pairing algorithm. Total power constraint and individual power constraints at the source node and the relay node will be treated differently. Both theoretical analysis and simulation results demonstrate that the authors' proposed joint algorithm for the improved DF protocol drastically harvests remarkable gains from the extra repeat transmission in the second phase and is superior to other existing methods.

In this study, the authors propose an energy‐efficient user association scheme to maximise the overall energy efficiency for downlink heterogeneous cellular networks, and formulate it as a non‐linear and mixed‐integer optimisation problem. Such a problem includes user association problem and power control problem. Since the formulated problem is in a fractional and mixed‐integer form, it is challenging for designers to achieve the optimal solutions of this problem. To this end, they design an effective three‐layer iterative algorithm. In the first layer, the energy efficiency parameter is found via bisection method. In the second layer, association index and transmit power are optimised alternately. In the third layer, the user association problem is solved via dual decomposition method and the transmit power is updated by employing a power update function. In addition, they further give some convergence analyses for some parts (user association algorithm and power control algorithm) of the proposed algorithm, and also give some complexity analyses for the whole algorithm. Numerical results show that, compared with non‐energy‐efficient association, the energy‐efficient association has significant superiorities on load balancing level, system throughput and energy efficiency.

To make full utilize the limited resources in heterogeneous cellular networks (HCNs), a proper offloading scheme is widely advocated. However, such scheme often lead to a bad result that the offloaded users achieves lower signal-to-interference-plus-noise-ratios (SINRs) than these users in macro-cells. To partially alleviate the SINR degradation, we consider an interference avoidance technique, i.e., a resource (frequency) partitioning strategy that turns off some fraction of such resource in a macrocell and saves it for low-power base stations (BSs). Naturally, an optimal offloading scheme should be closely coupled with the resource partitioning, and in turn an optimal partition decides the offloading performance. In this paper, we maximize a sum-utility with joint offloading and interference avoidance for HCNs. Considering that the formulated problem is in a nonlinear mixed-integer form and difficult to tackle, we introduce a dual decomposition method to develop an effective distributed algorithm. We reveal that load balancing, by itself, is insufficient, and additional interference avoidance is required for improving the system performance. Meanwhile, we show that the proposed scheme can provide a load balancing gain and an interference avoidance gain.

In this study, the authors propose a user association scheme with quality of service support for load balancing in heterogeneous cellular networks (HCNs), which jointly considers user's achievable rate and load level of each BS instead of only utilising the former. To reveal how HCNs should self‐organise, the authors formulate it as a network‐wide weighted utility maximisation problem. Note that the formulated problem is a non‐linear mixed‐integer one, and its optimal solutions may be very difficult to be found when it is large‐scale. To solve the proposed problem, the authors design a low‐complexity distributed algorithm via dual decomposition. Numerical results show that, compared with the range expansion association (REA) and best power association (BPA), the strategy has a higher load balancing level (LBL) and a lower call blocking probability (CBP). Meanwhile, the proposed algorithm occupies a very fast convergence rate when its parameters are set properly.

In heterogeneous cellular networks (HCNs), the user association is a challenging topic since some different base stations coexist. Moreover, because of the asymmetric uplink and downlink in HCNs, a joint uplink and the downlink association algorithm should be designed to improve the system performance. For the practical implementation, the authors need to ensure that the algorithm is highly effective. Thus, they try to design an association strategy that jointly maximises downlink sum rate and minimises uplink sum power, and formulate it as a sum‐utility maximisation problem. To solve this problem, they design a centralised association algorithm via a gradient descent method, and develop a distributed association algorithm via dual decomposition. Simulation results show that, compared with the signal strength‐based association, range expansion association and the method proposed by Ye, their scheme has some significant advantages in the mass.

In this paper, we propose a load-aware and QoS- aware user association strategy that jointly considers the load of each BS and user's achievable rate instead of only utilizing the latter, and formulate it as a network-wide weighted utility maximization problem to reveal how a heterogeneous cellular network should self-organize. This is a nonlinear mixed-integer optimization problem, and its optimum solutions are very difficult to be obtained when it is large scale one. To solve the proposed problem, we relax association indicator variables and adopt a gradient descent method to find optimum solutions. Then, each user is associated with some BS with a maximum association indicator taken from solutions of the relaxed optimization problem. Experimental results show that, compared with the best power association and range expansion association, our strategy has a lower call blocking probability and a higher load balancing level.