Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information (CSI) in FAS poses challenges due to its complex physical structure. Traditional methods, such as pilot-based interpolation and compressive sensing, are not only computationally intensive but also lack adaptability. Current extrapolation techniques relying on rigid parametric models do not accommodate the dynamic environment of FAS, while data-driven deep learning approaches demand extensive training and are vulnerable to noise and hardware imperfections. To address these challenges, this paper introduces a novel self-supervised learning network (SSNet) designed for efficient and adaptive channel extrapolation in FAS. We formulate the problem of channel extrapolation in FAS as an image reconstruction task. Here, a limited number of unmasked pixels (representing the known CSI of the selected ports) are used to extrapolate the masked pixels (the CSI of unselected ports). SSNet capitalizes on the intrinsic structure of FAS channels, learning generalized representations from raw CSI data, thus reducing dependency on large labeled datasets. For enhanced feature extraction and noise resilience, we propose a mix-of-expert (MoE) module. In this setup, multiple feedforward neural networks (FFNs) operate in parallel. The outputs of the MoE module are combined using a weighted sum, determined by a gating function that computes the weights of each FFN using a softmax function. Extensive simulations validate the superiority of the proposed model. Results indicate that SSNet significantly outperforms benchmark models, such as AGMAE and long short-term memory (LSTM) networks by using a much smaller labeled dataset. A key observation is that the proposed model is more effectively trained using a small unmasked ratio of known CSI. Specifically, the proposed SSNet trained using CSI of 10 % total ports outperforms that trained using CSI of 25 % and 50 % total ports. This is because using a smaller number of known CSIs during training, the proposed model is forced to learn more effective channel correlation for channel extrapolation at the expense of higher training complexities. Ablation experiments reveal substantial performance gains from the MoE module’s integration. Furthermore, zero-shot learning experiments show a moderate performance degradation of about 3-5 dB, underscoring the model’s robust generalization ability. Finally, the inference speed experiments illustrate that the proposed model outperforms the benchmark models dramatically at the expense of a slightly longer execution time of 1.13 ms, 2.9 ms, and 3.12 ms on NVIDIA RTX 4090, 4060, and 3060 graphics processing units (GPU)s, respectively.

Integrating Large AI Models (LAMs) into 6G mobile networks is a key enabler of the AI-Native Air Interface (AI-AI), where protocol intelligence must scale beyond handcrafted logic. This paper presents, to our knowledge, the first standards-compliant emulation of the Radio Resource Control (RRC) layer using a decoder-only LAM (LLAMA-class) fine-tuned with Low-Rank Adaptation (LoRA) on a multi-vendor corpus of real-world traces spanning both 5G and 4G systems. We treat RRC as a domain-specific language and construct a segmentation-safe Question-and-Answer (QA) dataset that preserves Abstract Syntax Notation (ASN.1) structure through linearization prior to Byte Pair Encoding (BPE) tokenization. The proposed approach combines parameter-efficient adaptation with schema-bounded prompting to ensure syntactic and procedural fidelity. Evaluation introduces a standards-aware triad—ASN.1 conformance, field-level coverage analysis, and uplink-to-downlink state-machine checks—alongside semantic similarity and latency profiling across 120 configurations. On 30k 5G request–response pairs plus an additional 4.8k QA turns from 4G sessions, our 8B model achieves a median cosine similarity of 0.97, a 61% relative gain over a zero-shot baseline, while sustaining high conformance rates. These results demonstrate that LAMs, when augmented with protocol-aware reasoning, can directly orchestrate control-plane procedures, laying the foundation for the future Artificial Intelligence (AI)-native Radio Access Network (RAN).

Ambient backscatter communications (AmBC), where a backscatter transmitter (BT) modulates and reflects ambient signals to a backscatter receiver (BR), have been deemed a low-power communication technology for the Internet of Things. Previous work on symbol detection in AmBC assumed perfect time synchronization (TS), which is unrealistic in practice. The residual TS errors (RTSE) causepartial sample mismatch, degrading symbol detection performance. To address this, we propose a new AmBC symbol detection framework that incorporates the BT’s current and adjacent symbols, as well as channel coefficients. Using energy detector (ED) as a case study, we derive both exact and approximate bit error rate (BER) expressions. Our results show that the ED’s BER performance degrades significantly under RTSE, with the symbol detection threshold optimized under the assumption of perfect TS. We then derive a closed-form expression for a near-optimal symbol detection threshold that minimizes BER under RTSE. To estimate the required parameters for the detection threshold, we propose a novel method exploiting the attributes of the BR’s received signal samples. The analytical results are verified by simulation results.

Symbiotic Backscatter Communication (SBC) has emerged as a spectrum-efficient and low-power communication technology, where backscatter devices (BDs) modulate and reflect incident radio frequency (RF) signals from primary transmitters (PTs). While previous studies have assumed a circularly symmetric complex Gaussian (CSCG) distribution for the BD’s signal, this assumption may not be practical because the high complexity of generating CSCG signals is not supported by the low-cost BD. In this paper, we address this gap by investigating SBC for two low-complexity modulation schemes, i.e.,M-ary amplitude-shift keying (MASK) andM-ary phase-shift keying (MPSK), where BD’s signals inherently deviate from CSCG distribution. Our goal is to derive the achievable rate of the PT and BD under the MASK/MPSK and to design MASK/MPSK modulation scheme for maximizing the PT’s rate. Towards this end, we first derive the expressions of both the PT’s rate and BD’s rate. Theoretical results reveal that whether or not the BD improves the PT’s rate depends on the phase of MASK/MPSK modulation, while the BD’s rate is independent of this phase. We then formulate two optimization problems to maximize the PT’s rate by adjusting the phase under the MASK and MPSK modulation schemes, respectively, and derive the optimal phases for each modulation scheme in closed forms. Given that the optimal phase is continuous and thus impractical for real-world BDs, we also propose a practical circuit design that enables BDs to select a discrete phase close to the theoretical optimum. Simulation results demonstrate that the optimal phase of MASK/MPSK can ensure an improvement in the PT’s rate and the performance gain between the ideal continuous phase and the practical implementation with few discrete phases is negligible, and reveal that a low-order ASK modulation is better than a low-order PSK for the BD in terms of improving PT’s rate, especially when the direct link is not significantly weaker than the backscatter link in SBC.

As a theoretical method for solving complex real-life problems, group decision-making (GDM), along with the rapid development of artificial intelligence technology, has led to intricate and complex decision-making situations. This has contributed to the rise and rapid evolution of complex large-scale GDM (LSGDM). In the LSGDM process, the reasonable grouping of decision-makers (DMs) and reaching a consensus are the core links to obtain the optimal decision-making scheme, and these rely heavily on mutual trust among DMs. However, in reality, not all trust is real and effective, and pseudo-trust is a common phenomenon. As such, identifying and managing pseudo-trust behavior by DMs has become a challenge. This study investigates the influence of pseudo-trust on DMs' dimensionality reduction and consensus process and proposes a social network LSGDM method based on feature selection and pseudo-trust behavior. Specifically, it proposes a leader feature selection based on a dual trust relationship to address the efficiency and rationality challenges in large-scale DMs' dimensionality reduction. Through this study, we provide a clear concept of pseudo-trust behavior and create a quantitative assessment system. Furthermore, an adaptive consensus model based on pseudo-trust behavior is constructed to achieve its effective identification and management. Finally, the effectiveness, practicability, and superiority of the proposed method are proven by selecting real-world cases from the UCI database, combined with experimental and comparative analyses.

Mutualistic symbiotic radio (MSR) that allows a primary link and a backscatter link to share resources and benefit each other has been investigated mainly for long-packet communications. In the Internet of Things (IoT), where short packets are often transmitted, the design of MSR needs to consider the distinct features of short packet transmissions. In this work, we study a multi-IoT-node MSR network supporting both long and short packet transmissions, where a primary transmitter sends long packets to a cooperative receiver (CR), while multiple IoT nodes sequentially backscatter their short packets to the CR. We first derive the lower bound expression for the primary transmission rate by considering the error probabilities (EPs) of the IoT nodes’ short-packet communications. On this basis, we propose a resource allocation scheme to maximize the weighted sum throughput of all IoT nodes, subject to the constraints on the quality-of-service requirement and energy causality for each IoT node, as well as the throughput gain for the primary transmission. Specifically, the weighted sum throughput maximization problem is formulated by jointly optimizing the short-packet blocklength, power reflection coefficient and EP of each IoT node, and is solved by proposing an iterative algorithm based on the block coordinate decent method. Simulation results confirm the quick convergence of the proposed iterative algorithm and show that our proposed scheme outperforms the existing scheme in terms of the weighted sum throughput of all IoT nodes.

In a single-antenna fluid antenna system (FAS), the transceiver dynamically selects the antenna port with the strongest instantaneous channel to enhance link reliability. However, deriving accurate yet tractable performance expressions under the fully correlated fading channel remains challenging, primarily due to the absence of a closed-form distribution for the FAS channel. To address this gap, this paper develops a novel performance evaluation framework for FAS under fully correlated Rayleigh fading by modeling the FAS channel using extreme value distributions (EVDs). We first justify the suitability of EVDs and model the FAS channel as a Gumbel distribution, whose parameters, estimated via the maximum likelihood (ML) criterion, are expressed as functions of the number of ports and antenna aperture size. Closed-form approximate expressions for the outage probability (OP) and the ergodic capacity (EC) are then derived. Simulation results show that the Gumbel distribution provides simple yet sufficiently accurate expressions for EC, although slight deviations in OP accur in the low-probability region of practical interest. To further improve accuracy, the FAS channel is modeled using the generalized extreme value (GEV) distribution, yielding closed-form expressions for OP and EC based on ML-estimated parameters. Simulation results confirm that the GEV distribution provides superior accuracy compared to the Gumbel, particularly for OP, while both EVD-based approaches offer computationally efficient and analytically tractable tools for evaluating FAS performance under the fully correlated fading channel.

Considering the mobility of high-speed railway (HSR) trains, we propose an interruption-avoiding transmission paradigm for content delivery in HSR communication systems, enabled by a space-terrestrial integrated network (STIN) that supports both out-carriage and in-carriage communications. Each content delivery task must be fully transmitted from a satellite or terrestrial base station to the corresponding carriage femto-cell access point (FAP) before handoff occurs. To minimize the maximum transmission latency, we formulate the Content delivery task Scheduling and Resource Allocation problem for both Out-carriage and In-carriage communications (CSRA-OI) over a long-term scheduling period. To solve the CSRA-OI problem, we decompose it into two interrelated sub-problems, i.e., a transmission rate control and in-carriage bandwidth allocation (RCIBA) sub-problem, and a content delivery task scheduling and out-carriage resource management (CSORM) sub-problem. We show that the optimal solution to the RCIBA sub-problem can be obtained by solving a series of linear programming problems. To solve the CSORM sub-problem, we propose a multi-agent deep reinforcement learning (DRL)-based framework assisted by a graph attention network (GAT). We present a novel training scheme for the CSORM framework, where the policy networks are updated rapidly in a distributed manner while the critic network and GAT are updated jointly at a slower rate. Extensive experiments validate the advantages of our approach.

The rapid advancement of AI-generated content (AIGC) is poised to reshape content delivery, by enabling AIGC capabilities at the network edge or directly on end-user devices. This will allow content requests to be satisfied with AIGC based on prompts, rather than transmitting the original content. Each option presents unique trade-offs. On-device AIGC minimizes network traffic by transmitting only prompts, but it produces lower content quality than on-edge AIGC, which supports larger AI models. AIGC on the edge, in turn, results in lower quality than the original content. Delivering the original content requires more resource on radio access (and backhaul if not cached), while on-edge AIGC consumes computing power. To optimally exploit these trade-offs, we formulate a utility maximization problem where for each content the system can opt for the original content, on-edge AIGC, or on-device AIGC, accounting for backhaul capacity, computational resources, and radio access constraints. For this discrete optimization problem, we prove that, by applying Lagrangian multipliers to the three resource constraints, the problem relaxation can be efficiently solved to optimality. We then propose a solution approach that combines the problem relaxation with an algorithm for reaching feasible solutions of the overall problem. Simulation results demonstrate that our approach outperforms the baseline strategies and the solutions are close to the global optimum.

With the concept of low-altitude economy (LAE) being released recently, the research on ultra-low latency communication and rich computation capacity technologies to support LAE-enabled internet of things (IoT) networks has attracted interest from industry and academia. One of the key challenges is to reduce the latency of the IoT networks while guaranteeing the quality of service among all user devices (UDs). In this paper, we propose an LAE-enabled IoT network, where a UAV-carried mobile edge computing (MEC) server offers extra computation capacity to all UDs to process their computational tasks remotely. To minimise the total service delay of all UDs, which consists of the transmission delay, processing delay, queueing delay, and UAV mobility delay, we propose a deep-Q-leaning (DQN)-based optimisation algorithm by jointly optimising the task offloading decisions and communication and computation resource allocation for all the UDs in the LAE-enabled IoT network. Simulation results illustrate that our proposed algorithm achieves a much lower total service delay than the benchmarks.

With the rapid expansion of smart applications in 6G-enabled low-altitude economy networks, edge intelligence has emerged as an essential technology to reduce latency and energy consumption by dynamically deploying edge servers close to end users. Unmanned aerial vehicles (UAVs) can each carry a small-sized edge server to offer computation services to multiple end users. In this paper, we consider a non-orthogonal multiple access (NOMA)-based two-layer UAV swarm edge intelligence network, which includes task-generating UAVs (TUAVs) and an edge server UAV (SUAV). SUAV offers extra computation capacity to all TUAVs to process their computational tasks remotely. To minimise the long-term total service delay and the total energy consumption of all TUAVs based on the tasks’ transmission and processing, we propose a proximal policy optimisation (PPO)-based offloading decision and resource allocation joint optimisation algorithm by optimising offloading decisions, communication and computation resources allocation, and transmit power for all TUAVs. Simulations demonstrate that our approach outperforms benchmark solutions in terms of long-term total service delay and total energy consumption of all TUAVs in a UAV swarm system.

Although coexisting passive reconfigurable intelligent surface (RIS) and active relay have been considered for enhancing long-packet communications, their potential in supporting short-packet communications (SPC) where the direct link is blocked has not been sufficiently studied. Motivated by this, this work investigates the performance of the coexisting passive RIS and active relay-assisted SPC network, where a source (S) transmits its short-packet data to the destination (D) with the help of a passive RIS and an active relay (R). More specifically, in the first time slot of the two-hop transmission, S transmits its short-packet signal to both the RIS and R. The RIS then reflects the incident signal towards D. If D is unable to decode the short-packet information while R successfully decodes it, R forwards the decoded signal to D with the assistance of the RIS in the second time slot. Following a decode-and-forward (DF) relaying protocol, we analytically derive the closed-form expression of the block error rate (BLER) for the short-packet transmission. Simulation results verify the correctness of the derived expression and show that the combination of the passive RIS and active relay can enhance the BLER performance as compared with the benchmarks that employ only a passive RIS or an active relay.

ABSTRACT Relay‐assisted symbiotic radio (SR) has been recently proposed to overcome the blocking of the direct link from the primary transmitter (PT) or backscatter node (BN) to the destination node (DN). However, the energy efficiency (EE), which is an important performance metric for SR networks, has been largely ignored in existing studies of the relay‐assisted SR. To fill the gap, this work maximizes the EE of a relay‐assisted parasitic SR network, which comprises a PT, a BN, a relay node (RN), and a DN. More specifically, we formulate a mixed‐integer programming optimization problem that maximizes the system EE by jointly optimizing the transmit power of the PT, the power reflection coefficient of the BN, the transmit power and the power allocation ratio at the RN as well as the successive interference cancellation (SIC) decoding order at the DN. We decompose the formulated non‐convex problem into two subproblems corresponding to the two different SIC decoding orders, respectively. For each subproblem, we convert its objective function from a fractional form into a subtractive form by using a Dinkelbach‐based method, and then utilize the block coordinate descent (BCD) method to further decouple it into two subsubproblems that are proved to be convex. Based on the obtained solutions, we devise an iterative algorithm to solve each subproblem by solving its two subsubproblems alternately. The optimal solution to the subproblem with a higher system EE returns a near‐optimal solution to the original problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and validate the significant advantages of our proposed algorithms over the baseline schemes.

Unmanned-aerial-vehicle (UAV) communications assisted by aerial intelligent reflecting surfaces (A-IRSs) have emerged as a highly regarded technology aiming to increase the communication capacity of cellular networks and improve user communication quality. However, the performance of combining UAV-carried A-IRSs with heterogeneous cellular networks (HetNets) has not been fully investigated. In this article, we develop a novel framework to analyze the downlink (DL) performance of an UAV-carried cooperative A-IRSs-enhanced HetNet. First, we model the cascaded fading of the A-IRS-assisted communication channel as a mixture of gamma variables and approximate its distribution function by using the moment matching method. Next, we obtain the exact closed-form expressions of the user-association probability and the probability density function (PDF) of the distance between any user and its serving BS. Then, we derive the Laplace transform of the received signal and the interference power of all types of users for our proposed A-IRS deployment strategy. Finally, we derive analytic expressions of the DL coverage probability and the energy efficiency of UAV networks, which are validated by the simulation results. The results demonstrate that our proposed theoretical framework significantly enhances the precision of analysis, and aligns closely with real-world scenarios. We also show the performance enhancement brought by cooperative A-IRSs, the advantages of our proposed A-IRS deployment strategy within HetNet, as well as the impact of the interference introduced by A-IRSs on direct transmissions.

The emerging drones enabled air-to-ground (A2G) communications in millimeter-wave (mm-wave) bands are facing the Doppler effect that arises from the inevitable wobbling of the drones. The fast time-varying channel for drones A2G communications can make the channel state information (CSI) from channel estimation outdated, i.e., imperfect CSI. In this article, we introduce two detectors to demodulate the received signal and get the instantaneous bit error probability (BEP) of a mm-wave drones A2G link under imperfect CSI. Based on the designed detectors, we propose an adaptive modulation scheme to maximize the average transmission rate under imperfect CSI by optimizing the signal transmission time subject to the maximum tolerable BEP. A power control policy is proposed in conjunction with the adaptive modulation to minimize the transmission power while maintaining both the BEP under the threshold and the maximized average transmission rate. Numerical results show that the proposed adaptive modulation scheme in conjunction with the power control policy can maximize the temporally averaged transmission rate while reducing the power consumption by up to 50% for wobbling mm-wave drones A2G links.

To support the explosive growth of wireless traffic, the emerging control-and user-plane separation (CUPS) paradigm allows control base stations (CBS) to provide control plane (CP) coverage, while traffic base stations (TBSs) catering to varying mobile traffic and diverse quality of service (QoS) requirements. However, learning-based traffic prediction has yet to utilize the potential advantages provided by CUPS to facilitate energy saving. In this paper, we introduce a novel base station (BS) sleep scheme for CUPS that employs a learning-based approach to determine the TBS’s active/deactive states. First, traffic demands of TBSs are predicted by a novel data-driven learning approach. This approach leverages the receptive fields to explore the spatial characteristics of mobile traffic, and then employs Bidirectional Long Short-Term Memory (Bi-LSTM) networks to extract context feature of mobile traffic. Based on the traffic forecasting, we formulate a long-term network energy efficiency maximization problem that optimizes the active/deactive states of TBSs. Moreover, we introduce a service penalty term into CP to mitigate potential network coverage vulnerabilities of TBS. Then, an improved twin delayed deep deterministic policy gradient (TD3)-based algorithm is employed to solve the above non-deterministic polynomial-time hard (NP-hard) problem. Extensive experiments using a real-world dataset demonstrate that the proposed scheme significantly outperforms the existing methods in terms of traffic forecasting accuracy. Additionally, the proposed energy efficiency maximization algorithm achieves superior performance than other benchmark schemes.

Existing works on mutualistic backscatter communications (MBC) rely on the existence of direct links connecting both the primary user (PU) and the backscatter device (BD) to the destination node (D), rendering them ineffective when these links are blocked. While relay technology offers a solution, the performance of relay-assisted MBC and whether the backscatter link still benefits the primary link or not remain unclear. Additionally, the energy-harvesting capability of BD has been largely ignored in the study of MBC. This paper investigates a relay-assisted MBC network, where a decode-and-forward relay (R) forwards the messages of a PU and an energy constrained BD to D. We propose three forwarding schemes for R, i.e., orthogonal time-division multiple access (OMA), non-orthogonal multiple access (NOMA) with static power allocation (PA), and NOMA with dynamic PA. Considering the energy-causality constraint of BD, we derive the outage probabilities and diversity gains of the primary and backscatter links for each forwarding scheme. Computer simulation verifies the correctness of our analytical results and reveals that different from conventional MBC, in the relay-assisted MBC, whether or not the backscatter link improves the primary link’s outage performance depends on the specific forwarding scheme employed, as well as the location of R and the power reflection coefficient of BD.

The evolution of intelligent transportation systems necessitate the integration of advanced technologies to address challenges in data processing, real-time decision-making, and network coverage. In this paper, we present a novel intelligent transportation system architecture that synergizes Unmanned Aerial Vehicles (UAVs), Digital Twins (DTs), and an Integrated Sensing, Communication, and Computation (ISCC) framework. In this system, UAVs equipped with edge computing capabilities collaborate with the ground-based cloud center to process data collected by perception devices (PDs). Each UAV and PD is mirrored by a Digital Twin (DT) at the base station, enabling real-time monitoring and predictive analytics. We intend to maximize the network lifetime and minimize the economic overhead, which is achieved through the joint optimization of the association policies of UEs, input data caching decisions, UAVs’ flight trajectory and speed, task processing mode selection and data unload proportion allocation under joint processing. To tackle the inherent challenges of nonlinearity, dynamic network conditions, and heterogeneous data sources, the problem is modeled as a Markov Decision Process (MDP) and solved using an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, adapted to handle both continuous and discrete action spaces. The experimental results show that compared to the baseline algorithm, this approach not only exhibits faster convergence but also achieves outstanding performance in maximizing system utility for intelligent transportation systems.

With the proliferation of vehicular user equipment (V-UE) in the Internet-of-Vehicles (IoV) systems, cloud computing alone cannot process all V-UE tasks, especially those latency-sensitive ones. Although static roadside fog nodes have been employed to offload computation from V-UEs, mobile fog nodes carried by vehicles that have the potential to further improve the performance of computation offloading for vehicular tasks have not been sufficiently studied for IoV systems. In this paper, we consider a mixed cloud/vehicular-fog computing (VFC) system that employs vehicle-carried fog nodes (V-FNs) in addition to cloud servers to offload tasks from V-UEs. To minimise the maximum service delay (which includes the transmission delay, queueing delay, and processing delay) among all V-UEs, we jointly optimise the offloading decisions of all V-UEs, the computation resource allocation at all V-FNs, the allocation of resource block (RB) and transmission power for all V-UEs while considering the mobility of V-UEs and V-FNs. The joint optimisation is solved by devising a fireworks algorithm-based offloading decision optimisation scheme, in conjunction with a bisection method-based V-FN computation resource allocation scheme and a clustering-based communication resource allocation scheme. Simulation results show that our proposed schemes outperform the benchmarks in terms of service the maximum delay among all V-UEs.

It is expected that 5G/6G networks will exploit sub-6 GHz, millimetre wave (mmWave) and terahertz (THz) frequency bands simultaneously and will increase flexibility in user equipment (UE)-cell association. In this paper, we introduce a novel stochastic geometry-based framework for the analysis of the signal-to-interference-plus-noise-ratio (SINR) and rate coverage in a multi-tier hybrid sub-6GHz-mmWave-THz network, where each tier has a particular base station (BS) density, transmit power, bandwidth, number of BS antennas, and cell-association bias factor. The proposed framework incorporates the effects of sub-6 GHz, mmWave and THz channel characteristics, BS beamforming gain, and blockages. We investigate the downlink (DL) and uplink (UL) decoupled cell-association strategy and characterise the per-tier cell-association probability. Based on that, we analytically derive the SINR and rate coverage probabilities for both DL and UL transmissions. The analytical results are validated via extensive Monte Carlo simulations. Numerical results demonstrate the superiority of the DL and UL decoupled cell-association strategy in terms of SINR and rate coverage over its coupled counterpart.

Backscattering enables battery-less and perpetually operating Internet-of-Things (IoT) devices to sweep through ambient electromagnetic waves (in terrestrial networks) or acoustic waves (in underwater networks) rather than generate new waves. The emerging underwater acoustic backscatter communication (UABC) has the potential to avoid the need for underwater nodes to actively emit acoustic signals, thus, reducing their energy consumption. However, there is a lack of a clear design, comprehensive modelling or analysis of UABC systems. This paper studies a UABC system where a high-power transceiver transmits acoustic signals and a piezoelectric UABC node modulates and reflects the incident acoustic signals back to the transceiver. Specifically, a detailed new design for UABC nodes is proposed and a circuit model for the nodes is developed to analyze their acoustic impedance under different materials and operating frequencies. Based on the circuit model, a node load design is introduced to maximize the acoustic reflection coefficients; and a comprehensive communication analytical framework is established and is used to analyze how different UABC parameters, such as piezoelectric material properties, operating frequencies of UABC nodes, transmission distances, ambient noise intensities, carrier wave frequencies, and wind speeds, affect the bit error rate of UABC. Simulation results validate the effectiveness of the proposed designs and models as well as identify the key parameters influencing the UABC performance.

Multiaccess edge computing (MEC) empowered air–ground integrated networks (AGINs) hold great promise in delivering accessible computing services for users and Internet of Things (IoT) applications, such as forest fire monitoring, emergency rescue operations, etc. In this article, we present a comprehensive air–ground integrated MEC framework, where edge servers carried by unmanned aerial vehicles (UAVs) will provide efficient computation services to IoT devices and user equipment (UE) (which are collectively referred to as UEs). We aim to minimize the long-term average weighted sum of task completion delay and economic expenditure for all the UEs. This objective is achieved through various strategies, including preinstalling new service instances into UAVs, removing idle service instances from UAVs, task offloading decision making, access control, selecting appropriate service instances for each offloaded service request, and resource allocation optimization. Considering the complexity of the problem and the dynamics of the system, we reformulate the problem as a Markov decision process (MDP) and present a multiagent deep deterministic policy gradient (MADDPG)-based algorithm to enable low-complexity and real-time adaptive decision-making. Since our problem contains integer, binary and continuous variables, it is not straightforward to apply the MADDPG algorithm. Specifically, we first normalize the continuous variables, and then convert the continuous output generated by MADDPG into discrete variables, while ensuring the coupling constraints between different variables are preserved. The simulation results demonstrate the fast convergence of our proposed algorithm and its superior performance in minimizing costs compared with the baseline algorithms.

Vehicular communications in future sixth-generation (6G) networks are expected to leverage integrated sensing and communications (ISACs) and mobile edge computing (MEC) techniques. However, the rapid proliferation of vehicle user equipment (V-UE) and the diversity of ISAC-aided and MEC-empowered vehicular communication and computation services demand a more intelligent and efficient resource allocation framework for the next-generation vehicular networks. To address this issue, we propose a comprehensive ISAC-aided vehicle-to-everything (V2X) MEC framework, where the V-UEs can offload their tasks to the edge server collocated at the roadside unit (RSU). We aim to minimize the long-term average total service delay of all the V-UEs by jointly optimizing the offloading decisions of all the V-UEs, the computation resource allocation at the ISAC-aided RSU, the transmission power, and the allocation of resource blocks for all the V-UEs, where the total service delay of a V-UE includes the task processing delay and the transmission delay if the V-UE offloads its task to the RSU. To solve the formulated mixed integer nonlinear programming problem, we design a multiagent deep deterministic policy gradient (MADDPG)-based offloading optimization and resource allocation algorithm (MADDPG-O2RA2). Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of convergence and the long-term average delay among all the V-UEs.

Reconfigurable intelligent surfaces (RISs) and millimeter-wave (mmWave) communications have been considered for providing wireless connectivity to mobile robots used in industrial plants and other indoor environments. However, the existing works have not sufficiently studied how the number and deployment locations of RISs should be optimized for serving a mobile robot. In this article, we study RIS-assisted mmWave communications for a robot moving around fixed obstacles in an indoor industrial environment. For a fixed total number of reflecting elements, we formulate an optimization problem to minimize the transmission energy consumption of the access point (AP) while ensuring the robot’s received signal-to-noise ratio (SNR) above a threshold throughout its journey by jointly optimizing the number, positions, and phase shifts of RISs and the beamforming vectors of the AP. To solve the formulated nonconvex optimization problem, we devise an iterative algorithm that decomposes it into two subproblems (i.e., optimizing the phase shifts of RISs and the beamforming vector of the AP, and optimizing the number and locations of RISs) and solves them alternately. Simulation results show that the proposed algorithm converges fast and can obtain the best number and locations of RISs that lead to a transmission energy consumption of the AP much lower than the benchmark schemes.

Intelligent omni-surfaces (IOSs) have been considered for assisting outdoor-to-indoor millimeter-wave (mmWave) communications. Nevertheless, the existing works have not adequately investigated how the number or the deployment locations of IOSs should be optimized for serving multiple indoor users. In this paper, we study IOS-assisted outdoor-to-indoor mmWave communications where IOSs are installed in an exterior wall of a building to refract mmWave signals from an outdoor base station (BS) to indoor users that locate among indoor blockages. Given a fixed total number of refracting elements, we formulate an optimization problem to maximize the downlink energy efficiency of the outdoor BS while satisfying the dowlink data rate requirements of the indoor users by jointly optimizing the number, locations and phase shifts of IOSs and the beamforming vectors of the BS. To address the varying dimensionality and the non-convexity of the optimization problem, we decompose it into two subproblems that optimize the IOSs’ phase shifts together with the BS beamforming vectors and the number and locations of IOSs, respectively, and devise successive convex approximation and Continuous Population-Based Incremental Learning-based algorithms to solve them alternately. Simulation results demonstrate that the proposed algorithms can obtain the optimal number and locations of IOSs, resulting in significantly enhanced energy efficiency of the outdoor BS compared to benchmark schemes.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where multiple ground devices offload portions of their latency-sensitive and mission-critical computational tasks to a UAV-carried MEC server for remote computing and compute the remaining portions locally. To meet the low-latency requirements of the MEC, ultra-reliable and low-latency communication (URLLC) is used to offload tasks from the devices to the UAV. We minimize the maximum computation latency among all devices by jointly optimizing the computing times and CPU frequencies of the devices and the UAV, the offloading bandwidths of the devices, and the three-dimensional location of the UAV. We propose an algorithm that decomposes the joint optimization problem into three subproblems, which optimize the UAV’s horizontal location, the UAV’s altitude, and the offloading bandwidths and computing CPU frequencies, respectively. In solving the subproblems, the data rate expression of the devices’ finite-blocklength offloading is accurately approximated by a tractable logarithmic function, and the successive convex approximation technique is applied to tackle the non-convex structure. Furthermore, a semi-closed-form solution to the subproblem that optimizes the bandwidths and CPU frequencies is derived to reduce the complexity. Simulation results show that the proposed algorithm can significantly reduce the system’s computation latency compared to the benchmark schemes.

In this article, we study the collocated and distributed deployment of intelligent reflecting surfaces (IRS) for a fixed total number of IRS elements to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services inside a factory. We build a channel model that incorporates the line-of-sight (LoS) probability and power loss of each transmission path, and propose three metrics, namely, the expected received signal-to-noise ratio (SNR), expected finite-blocklength (FB) capacity, and expected outage probability, where the expectation is taken over the probability distributions of interior blockages and channel fading. The expected received SNR and expected FB capacity for extremely high blockage densities are derived in closed-form as functions of the amount and height of IRSs and the density, size, and penetration loss of blockages, which are verified by Monte Carlo simulations. Results show that deploying IRSs vertically higher leads to higher expected received SNR and expected FB capacity. By analysing the average/minimum/maximum of the three metrics versus the number of IRSs, we find that for high blockage densities, both eMBB and URLLC services benefit from distributed deployment; and for low blockage densities, URLLC services benefit from distributed deployment while eMBB services see limited difference between collocated and distributed deployment.

In this article, we propose a novel system model for a wireless-powered mobile edge computing (MEC) network, where the Internet of Things (IoT) nodes perform partial offloading to the MEC server via hybrid backscatter communication (BackCom) and active radio (AR) following an orthogonal frequency division multiple access protocol, and maximize the system computation bits (SCBs). For the case of the system having more subchannels than IoT nodes, we formulate the SCB maximization problem that requires the joint optimization of the transmit power and time, subchannel allocation, computation frequency, and time of the MEC server, as well as the IoT nodes’ BackCom time and reflection coefficients, transmit power and time for AR-based offloading, local computing time and frequencies, subject to the MEC server’s computation capacity and the Quality of Service (QoS) and energy-causality constraints of each IoT node. By applying the proof by contradiction and time-sharing relaxation, we transform the formulated problem into a convex one and then solve it by using the existing convex tools. For the case of the system having less subchannels than IoT nodes, we propose a dynamic subchannel allocation scheme that allows each IoT node to choose one task-offloading mode from three modes: 1) HAPR; 2) BackCom only; and 3) AR only, while ensuring that no more than one IoT node occupies a subchannel at any time. The SCB is maximized by first determining the subchannel allocation and mode selection of each IoT node and then optimizing the remaining resource allocation for the MEC server and all IoT nodes under the obtained subchannel assignment and mode selection. Simulations validate the superior performance of the proposed schemes over several benchmark schemes from the SCB perspective.

The performance of indoor small-cell networks (SCNs) is affected by the indoor environment, such as walls, blockages, etc. In this article, we investigate the effect of interior wall attenuation on the performance of an indoor SCN. Specifically, the spatial distribution of interior walls is modeled based on the random shape theory, and the indoor base stations (BSs) are distributed following a homogeneous Poisson point process. The channel model includes the path loss, Rayleigh fading, and wall attenuation. We analytically derive the downlink coverage probability under the strongest received signal user association strategy, which is validated by Monte Carlo simulations for three typical interior wall layouts (i.e., random layout, binary orientation layout, and Manhattan grid). The analytical results show that for a given density of interior walls and signal strength attenuation per wall, there is an optimal BS density that maximizes the coverage probability, and the optimal BS density increases as the wall attenuation increases.

Group decision making (GDM) is a useful uncertain decision theory and tool. With the evolution of time, GDM problems become more and more complex. In reality, there are plenty of GDM problems with diversified attributes and heterogeneous information. In this article, the diversified attributes describe the differences in attributes given by different decision makers, and heterogeneous information refers to the differences in data types. Nonetheless, few existing GDM studies simultaneously focus on the above two characteristics of GDM problems. Moreover, the scenario of multiple decision preferences and the attribute and expert weights based on heterogeneous information are also seldom discussed. Given this, by considering a kind of GDM problem with the characteristics of multisource, heterogeneity, diversity, and multiple fuzzy decisions, this article builds a novel GDM method from the perspective of multigranulation rough sets. First, we construct a new weighted multigranulation generalized fuzzy rough set (WMGGFRS) over multisource heterogeneous fuzzy decision systems. Subsequently, in response to heterogeneous information, the extended entropy weight methods are given to calculate the weight of each attribute and the weight of each expert. After that, we establish a GDM method using the WMGGFRS model to realize multisource information fusion. Finally, we apply the established method to the management decision-making problem of gout diagnosis. The results of comparative and sensitivity analyses demonstrate the feasibility, effectiveness, stability, and superiority of the method.

In this paper, we propose a meta-learning based cell-level network traffic prediction framework (ML-TP), which can provide the proper initial weight vector for the learning model of a new prediction task based on the prediction task’s meta-features. In the ML-TP, each prediction task forms a base-task, and a multi-layer long short-term memory (LSTM) network is constructed as its base-learner. Through fast Fourier transform (FFT) analyses of real-world network traffic data, we find that the five most dominating frequency components can capture the cell-level traffic variations, and hence can be used as a base-task’s meta-features. We prove that the well-trained weight vector of a previous base-task’s base-learner is likely to be a proper initial weight vector of a new base-task’s base-learner if the meta-features of the two base-tasks are close to each other in the Euclidean space. Accordingly, we propose a K-nearest neighbours (KNN) algorithm based meta-learner to deal with the meta-task in the ML-TP. Numerical tests show that the ML-TP can significantly increase the base-learners’ after-training prediction accuracy and learning efficiency in terms of the number of base-samples and the number of epochs needed in each base-learner’s fine-tuning progress.

We consider a heterogeneous network (HetNet), where multiple access points (APs) of potentially different transmission capacities serve users simultaneously via beamforming in the same spectrum band. We propose a beamforming framework that exploits multi-agent deep reinforcement learning (DRL) for the HetNet to maximize the system downlink sum-rate. In our framework, each AP acts as an agent, which is equipped with an online policy deep neural network (DNN) and an online Q-function DNN. The former generates an AP’s beamforming vector based only on local observations in a time slot, while the latter evaluates the appropriateness of this beamforming vector. We present a distributed-updating-centralized-rewarding scheme to train the policy DNNs and Q-function DNNs of all the APs in an online trial-and-error way. Under this scheme, all the APs take the system downlink sum-rate in a recent time slot (informed by a central controller) as their identical one-step reward. Trained by the experience items with centralized rewards in every time slot, the weight vectors of each AP’s local DNNs will be updated in the direction to the global optimum. Simulation results demonstrate that the proposed framework converges fast and outperforms the benchmark beamforming methods in terms of the system downlink sum-rate performance.

The increasingly stringent requirement on quality-of-experience in 5G/B5G communication systems has led to the emerging neural speech enhancement techniques, which however have been developed in isolation from the existing expert-rule based models of speech pronunciation and distortion, such as the classic Linear Predictive Coding (LPC) speech model because it is difficult to integrate the models with auto-differentiable machine learning frameworks. In this paper, to improve the efficiency of neural speech enhancement, we introduce an LPC-based speech enhancement (LPCSE) architecture, which leverages the strong inductive biases in the LPC speech model in conjunction with the expressive power of neural networks. Differentiable end-to-end learning is achieved in LPCSE via two novel blocks: a block that utilizes the expert rules to reduce the computational overhead when integrating the LPC speech model into neural networks, and a block that ensures the stability of the model and avoids exploding gradients in end-to-end training by mapping the Linear prediction coefficients to the filter poles. The experimental results show that LPCSE successfully restores the formants of the speeches distorted by transmission loss, and outperforms two existing neural speech enhancement methods of comparable neural network sizes in terms of the Perceptual evaluation of speech quality (PESQ) and Short-Time Objective Intelligibility (STOI) on the LJ Speech corpus.

In mutualistic cooperative ambient backscatter communications (AmBC), Internet-of-Things (IoT) device sends its information to a desired receiver by modulating and backscattering the primary signal, while providing beneficial multipath diversity to the primary receiver in return, thus forming a mutualism relationship between the AmBC and primary links. We note that the hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we consider a mutualistic cooperative AmBC network under HIs, and study the impacts of HIs on the achievable rates of the primary link and the AmBC link. In particular, we theoretically prove that although HIs degrades the rate of each link, the mutualism relationship between the AmBC and primary links is maintained, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. The closed-form rate expressions of both the AmBC and primary links are derived. Computer simulations are provided to validate our theoretical analysis.

In order to support delay-sensitive applications of vehicle equipment (V-UE) in the Internet-of-Vehicles (IoV) systems, it is necessary to allow V-UEs to offload their computationally intensive applications to a cloud or fog computing server. Existing works mainly focused on minimising the transmission and processing delays while ignoring the mobility of V-UEs and/or the queueing delays at the cloud or fog servers. In this paper, we consider a vehicular network supported by a mixed fog and cloud computing system, where the queues at the fog node (FN) and the cloud centre are modelled following the M/M/1 and M/M/C queueing models, respectively. To minimise the maximum service delay (which includes the transmission delay, queueing delay and processing delay) among the V-UEs, we propose to jointly optimise the offloading decisions of all V-UEs and the computation resource allocation at the FN while considering the V-UEs’ mobility and queueing delays at the FN and cloud centre. This is achieved by devising a fireworks algorithm-based offloading decision optimisation algorithm in conjunction with a bisection method-based FN computation resource allocation scheme. Simulation results demonstrate that our proposed algorithm achieves a much lower maximum service delay than the benchmarks.

Due to the limited computing capacity in mobile devices, device-to-device (D2D) computation offloading has been proposed as a promising solution to improving the quality of service in the Internet of Things (IoT) networks, by allowing mobile devices to exploit spare computing resources in nearby user devices. However, a major challenge to realizing this new paradigm is how to effectively motivate user devices to participate as computation providers (CPs) for computation requesters (CRs), which is further exacerbated by the fact that user incentives are usually coupled with information asymmetry between the network operator and user devices. This has not been sufficiently studied for D2D computation offloading. In this article, we propose a signaling-based incentive mechanism that leverages contract theory to address information asymmetry for D2D computation offloading. Based on the proposed contract-based incentive mechanism, we also solve the many-to-many CP-CR pairing problem by devising a polynomial-complexity matching scheme. Simulation results show that our proposed algorithm can effectively motivate user devices to participate in D2D computation offloading and select the most appropriate CPs to perform the computation tasks for corresponding CRs.

The authors regret the errors in[1, eqs. (13), (27), (34), and (35)]. The corrections for these equations are given as follows:

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate.

In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio system and maximize the secondary user (SU) rate by jointly optimizing the transmit power of secondary transmitter (ST) and the IRS’s reflect beamforming, subject to the constraints of the minimum required signal-to-interference-plus-noise ratio at the primary receiver, the ST’s maximum transmit power, and the unit modulus of the IRS reflect beamforming vector. This joint optimization problem can be solved suboptimally by the non-convex optimization techniques, which however usually require complicated mathematical transformations and are computationally intensive. To address this challenge, we propose an algorithm based on the deep deterministic policy gradient (DDPG) method. To achieve a higher learning efficiency and a lower reward variance, we propose another algorithm based on the soft actor-critic (SAC) method. In these proposed algorithms, a reward impact adjustment approach is proposed to improve their learning efficiency and stability. Simulation results show that the two proposed algorithms can achieve comparable SU rate performance with much shorter running time, as compared to the existing non-convex optimization-based benchmark algorithm, and that the proposed SAC-based algorithm learns faster and achieves a higher average reward with lower variance, as compared to the proposed DDPG-based algorithm.

With the dense deployment of small cells, the impact of height difference between base stations (BSs) and user equipment (UE) on the performance of heterogeneous networks (HetNets) becomes significant. The traditional two-dimensional models are no longer sufficient to capture the three-dimensional (3D) features of dense HetNets. On the other hand, deploying multiple antennas on BSs is a promising approach to improve network capacity. In this paper, we propose a 3D model for a$K$-tier HetNet with multi-antenna BSs, where different tiers share the same frequency band but may differ in BS height, BS density, number of antennas per BS, BS transmit power, association bias, and path loss exponent. We analytically derive the per-tier association probability under both the strongest received signal and the closest BS cell-association strategies. Based on that, we derive the expressions for the downlink ergodic rate, area spectral efficiency (ASE) and energy efficiency. The numerical results reveal that in the presence of macrocell BSs, for low to medium small-cell BS (SBS) densities, the closest BS cell-association strategy leads to low ergodic rate, ASE and energy efficiency regardless of the SBS height; while at very high SBS densities, under both cell-association strategies, SBSs should be deployed at the same height as UE to achieve high ergodic rate, ASE and energy efficiency. Moreover, we find that for a given SBS height, there exists an optimal combination of SBS density and number of antennas per SBS that maximizes the system energy efficiency.

Wireless powered communication via backscatter and/or harvest-then-transmit (HTT) has been considered a promising solution to connecting nodes in the Internet-of-things (IoT) networks. However, the harvested energy at an IoT node is heavily limited by the distance between the node and the power beacon (PB) due to the high propagation loss. In this paper, we propose to employ an unmanned aerial vehicle (UAV) as a mobile PB to provide energy signals on demand to the IoT nodes, which convey their information to a reader via backscattering or active transmission using the harvested energy. We maximize the total energy efficiency (EE) of all the IoT nodes powered by the UAV by jointly optimizing the UAV’s transmit power and trajectory, the IoT nodes’ backscatter reflection coefficients and their transmit power for active transmission, and the time allocation between backscattering and active transmission. To solve the formulated non-linear fractional programming problem, we use the generalized fractional programming theory and a block coordinated decent method to decompose it into two sub-problems: one optimizes the communication resource allocation under a fixed UAV trajectory, and the other optimizes the UAV trajectory for given communication resource allocation. We then devise a Dinkelbach-based iterative algorithm to solve the two sub-problems by employing a Lagrangian dual method and a successive convex programming technique, respectively and iteratively. Simulation results show that our proposed iterative algorithm converges very fast, and the optimized UAV-enabled hybrid backscatter-HTT communication achieves a much higher EE of all the IoT nodes than the benchmark schemes including the UAV-enabled backscatter, UAV-enabled HTT, and hybrid BackCom-HTT with a fixed PB.

In this paper, we consider a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple Internet-of-Things (IoT) nodes harvest energy from the energy signals transmitted by a power beacon (PB) and utilize the harvested energy for local computing and task offloading via hybrid backscatter communication (BackCom) and active transmission (AT). Considering the limited computation capacity of the MEC server and the quality-of-service (QoS) and energy-causality constraints per IoT node, we propose two resource allocation schemes to maximize the total computation bits of all the IoT nodes and the system computation energy efficiency (EE), respectively, by jointly optimizing the computation frequency and time of the MEC server and each IoT node, the transmit power of the PB and each IoT node, and the BackCom power reflection coefficient and the time for energy harvesting (EH), BackCom, and AT of each IoT node. The non-convex computation bits maximization problem is transformed to a convex one by introducing a series of auxiliary variables and proof by contradiction, and then solved by the existing convex tools. The system computation EE maximization is a non-convex nonlinear programming problem. We propose a two-layer iterative algorithm to solve it optimally and devise a reduced-complexity iterative algorithm to solve it sub-optimally by leveraging the block coordinate decent technique. Computer simulations validate the convergence of the proposed iterative algorithms and their superior performance over the benchmark schemes in terms of the computation bits or EE.

With the ever-increasing demand for wireless connections from vehicles, vehicle-to-everything (V2X) communication has become an emerging technology to enable vehicles to communicate with other vehicles, pedestrians, and communication infrastructures. In the meantime, most communication demands are initiated by human users, whose social-activities and attributes would influence their communication demands and requirements. However, the existing resource allocation schemes for V2X communications mainly focus on the physical domain, and have largely ignored the influence of the social domain. In this paper, we propose a social-aware clustering resource allocation (SACRA) algorithm to maximise the sum vehicle-to-infrastructure (V2I) capacity while guaranteeing the reliability of all vehicle-to-vehicle (V2V) links. In SACRA, firstly, we classify all vehicles into different social communities according to their social attributes. Secondly, in each social community, the V2I links are allocated with orthogonal spectrum resources, and the V2V links are divided into different clusters, each sharing the spectrum resource of a distinct V2I link, via a graph partitioning algorithm that minimises the intra-cluster interference. Compared with non-social-aware algorithms, simulation results demonstrate that the sum V2I capacity is improved by our proposed SACRA.

Interior wall blockage affects the performance of indoor cellular networks but has not been sufficiently studied. In this paper, by modelling the spatial distribution of interior walls using the random shape theory and incorporating the attenuation caused by wall blockages into the path loss model, we develop a tractable approach to analyse the performance of indoor small cell networks under the influence of interior wall blockages. Then, the proposed analytical approach is applied to study the downlink coverage probability of two user association strategies: the closest line-of-sight (LOS) base station (BS) strategy, and the closest non-line-of-sight (NLOS) BS strategy. The analytical results are validated by Monte Carlo simulations in comparison with those obtained under the existing wall blockage model that assumes impenetrable walls. Our results reveal the importance of considering the wall attenuation for the accurate analysis of indoor cellular networks. The coverage probability is more sensitive to wall attenuation under the closest NLOS BS user association strategy than under the closest LOS BS user association strategy, and that there is an optimal BS density that maximizes the coverage probability under the closest LOS BS user association strategy, but the coverage probability under the closest NLOS BS user association strategy monotonically decreases with the increase of BS density.

Transmission delay is critical to time-sensitive and power-limited Internet of Things (IoT) systems. This work proposes a hybrid transmission scheme, which enables energy-constrained sensor nodes (SNs) to deliver data to an information fusion via a hybrid of backscatter communications (BackCom) and wireless powered active communications (WPAC). Considering a non-linear energy harvesting (EH) model for each SN, we formulate a non-convex optimization problem to minimize the total transmission time of all SNs while satisfying the minimum throughput requirement for each SN, by jointly optimizing the time allocation between BackCom and active communications (AC), the transmit power and the power reflection coefficients of each SN, and the transmit power of the energy source (ES). We first determine the optimal transmit power of the ES by contradiction and then transform the non-convex problem into a convex one by introducing a series of auxiliary variables. We theoretically prove that the minimum total transmission time is achieved when each SN exhausts all the harvested energy and does not work in the pure BackCom mode. Simulation results show that the proposed scheme achieves a much shorter total transmission time than the existing schemes, e.g., binary transmission scheme, WPAC, and pure BackCom.

With the rapid growth of mobile devices and applications, fog radio access network (F-RAN) has been proposed as a promising paradigm for the 5th generation of mobile networks. Meanwhile, F-RAN can be further enhanced by edge caching underpinned by device-to-device (D2D) communications, where the download delay can be reduced by edge caching users (EUs) providing content though D2D links to content requesting users (RUs). In this paper, to mitigate the impact of limited storage at each EU, we propose an EU classification based content caching policy for F-RAN, where the EUs are divided into two groups, each group caching a different content set. To maximize the cache hit probability at EUs, we present a caching policy selection algorithm that allows the fog radio access point (F-AP) to choose between the proposed EU classification based content caching policy and the conventional probability based caching policy. By modeling the content request queue at each EU as an independent M/D/1 queue model at EUs, we analyze the average download delay under the proposed caching policy selection algorithm. The simulation results show that the proposed algorithm can significantly increase the cache hit probability of EUs and can also reduce the average download delay for RUs.

Emerging smart Internet-of-Things (IoT) applications are increasingly relying on mobile-edge computing (MEC) networks, where the energy efficiency (EE) of computation is one of the most pertaining issues. In this article, considering the limited computation capacity at the MEC server and a practical nonlinear energy harvesting (EH) model for IoT devices, we propose a scheme to maximize the system computation EE (CEE) of a wireless power transfer (WPT) enabled nonorthogonal multiple access (NOMA)-based MEC network by jointly optimizing the computing frequencies and execution time of the MEC server and the IoT devices, the offloading time, the EH time and the transmit power of each IoT device, as well as the transmit power of the power beacon (PB). We formulate the joint optimization into a nonlinear fractional programming problem and devise a Dinkelbach-based iterative algorithm to solve it. By means of convex theory, we derive closed-form expressions for parts of the optimal solutions, which reveal several instrumental insights into the maximization of the system CEE. In particular, the system CEE increases as the optimal computing frequencies of both the IoT devices and the MEC server decrease, and the system CEE is maximized when the MEC server and the IoT devices use the maximum allowed time to complete their computing tasks. Simulation results demonstrate the superiority of the proposed scheme over benchmark schemes in terms of system CEE.

In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user's traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes.

Mobile data traffic has been largely generated indoors. However, indoor cellular networks have been studied either on a two-dimensional (2D) plane or as an intractable optimization problem for a multi-story building. In this paper, we develop a tractable three-dimensional small-cell network model for a multi-story building. On each story, the small-cell base stations (BS) are distributed following a 2D homogeneous Poisson point process. We analytically derive the downlink coverage probability, spectral efficiency (SE) and area spectral efficiency for the indoor network as functions of the story height, the penetration loss of the ceiling and the BS density. Our tractable expressions show that a higher penetration loss of the ceiling leads to a higher coverage probability and a higher SE. Meanwhile, with the increase of the story height or the BS density, the downlink coverage probability first decreases and then increases after reaching a minimum value, indicating that certain values of story height and BS density should be avoided for good indoor wireless coverage.

Content caching has limitations on achieving cache gains (e.g., cache hit ratio) in small cell networks, due to limited storages of small base stations (SBSs) and inherent user demand patterns (i.e., initial content preferences). Two effective approaches have been proposed to exploit the potential of content caching: SBS cooperation to utilize cache storage, and proactive content recommendation to shape user demand. In this paper, we investigate cooperative content caching and recommendation to maximize cache gains, while guaranteeing users' satisfaction by recommending appealing content items. We propose a generic framework for cooperative content caching and recommendation, based on which we propose an online and distributed scheme by designing a continuous-time Markov chain (CTMC). In particular, online content caching (a.k.a., content replacement) is implemented by hopping from one cache state to another in the CTMC, while content recommendation is performed heuristically through sequential fixing at each cache state. Besides, we characterize the performance gap between our proposed scheme and the theoretical optimum in terms of cache hit ratio. Simulation results demonstrate that the proposed scheme achieves better cache hit ratios than other schemes in single-BS scenarios, and provides a competitive solution in multiple-BS scenarios.

In ultra-dense networks, the increasing popularity of computation intensive applications imposes challenges to the resource-constrained smart mobile devices (SMDs), which may be solved by offloading these computation tasks to the nearby mobile edge computing centers. However, when massive SMDs offload computation tasks in a dynamic wireless environment simultaneously, the joint optimization of their offloading decisions becomes prohibitively complex. In this paper, we firstly model the joint optimization problem as a multi-user non-cooperative dynamic stochastic game, then propose a mean field game based algorithm to solve it with a drastically reduced complexity. We derive the two partial differential equations ruling the optimal strategies of the mean field game, namely the Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations, which are solved in an iterative manner in our proposed algorithm. Numerical results demonstrate that the proposed mean field game-based offloading algorithm requires a lower cumulated cost than the conventional strategies under the latency constraints of computation tasks, with perfect prediction of future channel states. It also appears that the performance of the mean field game-based offloading strategy depends on the accuracy of the future channel knowledge provided to the system, as the uncertainty may compromise its cumulated cost performance.

Due to the intermittency of renewable energy generation, base stations (BSs) powered by renewable energy harvesters will experience power surpluses and power deficits over time. To use the renewable power more efficiently, energy harvesters with anti-correlated energy generation profiles should be deployed at BSs that are connected by transmission lines and power should be transmitted from surplus BSs to deficit BSs via transmission lines. In this paper, we develop an optimization algorithm to determine how energy harvesters with anti-correlated energy generation profiles should be deployed to every BS by taking into account the topology of the cellular network, i.e., whether or not a transmission line exists between a pair of BSs. Therefore, a BS is deployed more likely with an energy harvester type that is anti-correlated to those deployed at its connected neighboring BSs with our proposed algorithm. In addition, our proposed algorithm takes into account the distance-dependent power loss in the transmission lines. As a result, the shorter the transmission line between a pair of BSs, the more likely that these two BSs are deployed with anti-correlated energy harvesters. The renewable power that can be transmitted from the surplus BSs to the deficit BSs in the cellular network is on average around 40% higher with our proposed optimization algorithm in comparison with randomly deploying anti-correlated energy harvesters to the BSs.

Millimeter-wave (mmWave) communication plays a significant role in 5G networks. In this paper, we model a mmWave multicell network with three-dimensional (3D) large-scale antenna arrays, including the path loss and inter-cell interference. In general, the antenna downtilt of the base station (BS) determines the beamforming direction in both horizontal and vertical planes. The distance between the BS antenna and a typical user is modeled more accurately in 3D while considering the height of the BS than modeling all network nodes in a 2D plane. We investigate the joint impact of the BS antenna height and antenna downtilt on the performance of the mmWave downlink transmission in terms of the user data rate averaged over all possible locations of the user within the cell coverage area. We present numerical results to show that it is critical to jointly optimize the BS antenna height and downtilt in order to maximize the average downlink data rate across the cell coverage area, and provide useful guidelines into the 3D deployment of mmWave networks.

To better characterize indoor small cell networks (SCN), we consider the blockages caused by interior walls and employ the bounded path loss model to derive the expression for energy efficiency (EE) of a downlink massive multiple-input multiple-output (MIMO) SCN. Our EE expression demonstrates that a higher penetration loss of interior walls leads to a higher EE. For the purpose of maximizing EE, we propose a novel genetic algorithm (GA) based scheme to jointly optimize the number of antennas per base station (BS), the number of users per cell, and the transmission power per antenna. Numerical results show that our proposed scheme can achieve almost the identical EE as the optimal greedy search algorithm, while significantly reducing the computational time.

To prolong the time duration of smart mobile devices (SMDs) or enable low‐latency tasks, mobile edge computing (MEC) has emerged as a promising paradigm by offloading tasks to nearby MEC servers (MECSs). In this study the authors propose an optimisation problem to minimise the weighted sum of the total delay and energy consumption of all SMDs in a multi‐MECS‐multi‐SMD network via multi‐dimensional optimisation on offloading strategy making, load balancing, computation resource allocation and transmit power control. Since the problem is NP‐hard, the authors decompose it into three subproblems to solve. First, they propose a low complexity heuristic algorithm to obtain the offloading strategies while guaranteeing load balancing between the multiple MECSs. Then they solve computation resource allocation subproblem using Lagrange dual decomposition. Finally, employing fractional programming, the authors transform the transmit power control subproblem into a convex programming problem where the closed‐form solution is obtained. The proposed simulation results verify the convergence of the proposed iterative algorithms, and demonstrate that the proposed joint optimisation could achieve good performance in both delay and energy reduction.

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user equipments, such as head-mounted displays (HMDs), ultralow-latency viewport rendering, and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multiaccess edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich terahertz (THz) communication is expected to enable ultrahigh-speed wireless data transmission. In this article, we propose to minimize the long-term energy consumption of a THz wireless access-based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning-based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor-critic (A3C)-based joint optimization algorithm is proposed. The simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.

Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

Due to the limited battery capacity in mobile devices, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve the energy efficiency (EE) in Internet-of-Things (IoT) networks, i.e., device-to-device (D2D) networks, by allowing mobile devices to harvest energy from ambient radio-frequency (RF) signals. However, the nonlinear behavior of RF energy harvesters has largely been ignored in the existing works on SWIPT. In this article, we propose to maximize the sum EE of all D2D links in a D2D underlaid cellular network by optimizing the resource and power allocation based on a nonlinear energy harvesting (EH) model. Toward this end, we first propose a prematching algorithm to divide the D2D links into a SWIPT-enabled group and a non-EH group that cannot meet the EH circuit sensitivity. We then develop a two-layer iterative algorithm to jointly optimize the D2D transmission power and the power splitting ratio to maximize the EE for each SWIPT-enabled D2D link. On this basis, we build the preference lists for both SWIPT-enabled D2D links and cellular user equipment (CUE), and propose a one-to-one constraint stable matching algorithm to maximize the sum EE of all SWIPT-enabled D2D links by optimizing the spectrum resource sharing between D2D links and CUEs. The sum EE of non-EH D2D links is maximized through an iterative power control algorithm and a one-to-one stable matching algorithm. Simulation results show that our proposed algorithms achieve a much higher sum EE than the existing matching-based energy-efficient resource allocation scheme for SWIPT-enabled D2D networks.

Ambient backscatter communications (AmBackComs) have been recognized as a spectrum- and energy-efficient technology for the Internet of Things, as it allows passive backscatter devices (BDs) to modulate their information into the legacy signals, e.g., cellular signals, and reflect them to their associated receivers while harvesting energy from the legacy signals to power their circuit operation. However, the co-channel interference between the backscatter link and the legacy link and the nonlinear behavior of energy harvesters at the BDs have largely been ignored in the performance analysis of AmBackComs. Taking these two aspects, this article provides a comprehensive outage performance analysis for an AmBackCom system with multiple backscatter links, where one of the backscatter links is opportunistically selected to leverage the legacy signals transmitted in a given resource block. For any selected backscatter link, we propose an adaptive reflection coefficient (RC), which is adapted to the nonlinear energy harvesting (EH) model and the location of the selected backscatter link, to minimize the outage probability of the backscatter link. In order to study the impact of co-channel interference on both backscatter and legacy links, for a selected backscatter link, we derive the outage probabilities for the legacy link and the backscatter link. Furthermore, we study the best and worst outage performances for the backscatter system where the selected backscatter link maximizes or minimizes the signal-to-interference-plus-noise ratio (SINR) at the backscatter receiver. We also study the best and worst outage performances for the legacy link where the selected backscatter link results in the lowest and highest co-channel interference to the legacy receiver, respectively. Computer simulations validate our analytical results and reveal the impacts of the co-channel interference and the EH model on the AmBackCom performance. In particular, the co-channel interference leads to the outage saturation phenomenon in AmBackComs, and the conventional linear EH model results in an overestimated outage performance for the backscatter link.

The tremendous popularity of Internet-of-Things (IoT) applications and wireless devices has prompted a massive increase of indoor wireless traffic. To further explore the potential of indoor IoT wireless networks, creating constructive interactions between indoor wireless transmissions and the built environments becomes necessary. The electromagnetic (EM) wave propagation indoors would be affected by the EM and physical properties of the building material, e.g., its relative permittivity and thickness. In this article, we construct a new multipath channel model by characterizing wall reflection (WR) for an indoor Line-of-Sight (LOS) single-user multiple-input-multiple-output (MIMO) system and derive its ergodic capacity in the closed-form. Based on the analytical results, we define the wireless friendliness of building material based on the spatially averaged indoor capacity and propose a scheme for evaluating the wireless friendliness of building materials. Monte Carlo simulations validate our analytical results and manifest the significant impact of the relative permittivity and thickness of building material on indoor capacity, indicating that the wireless friendliness of building materials should be considered in the planning and optimization of indoor wireless networks. The outcomes of this article would enable appropriate selection of wall materials during building design, thus enhancing the capacity of indoor LOS MIMO communications.

Exoskeleton robots can assist humans to perform activities of daily living with little effort. In this paper, a hierarchical control scheme is presented which enables an exoskeleton robot to achieve cooperative manipulation with humans. The control scheme consists of two layers. In low-level control of the upper limb exoskeleton robot, an admittance control scheme with an asymmetric barrier Lyapunov function-based adaptive neural network controller is proposed to enable the robot to be back drivable. In order to achieve high-level interaction, a strategy for learning human skills from demonstration is proposed by utilizing Gaussian mixture models, which consists of the learning and reproduction phase. During the learning phase, the robot observes and learns how a demonstrator performs a specific impedance-based task successfully, and in the reproduction phase, the robot can provide the subjects with just enough assistance by extracting human skills from demonstrations to prevent the motion of the robot end-effector deviating far from desired ones, due to variation in the interaction force caused by environmental disturbances. Experimental results of two different tasks show that the proposed control scheme can provide human subjects with assistance as needed during cooperative manipulation.

Mobile edge computing enabled vehicular networking has appeared as a promising solution to the emerging resource hungry vehicular applications. In this paper, we study the computation offloading in a cognitive vehicular network that reuses the TV white space (TVWS) bands. We propose to maximize the average economical profit of the service provider by jointly considering communication and computation resource allocation, while guaranteeing network stability and the QoS of TVWS primary users. Based on Lyapunov optimization, we design an per-frame algorithm to tackle the joint optimization problem, where we first derive the closed-form solution for computation resource allocation, and then develop a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for radio resource allocation. Simulation results demonstrate that the proposed algorithm can flexibly balance the profit-delay tradeoff, and can improve the economical profit of the service provider significantly as compared with the existing schemes.

The 1000 fold capacity enhancement is one of the key requirements in the future 5G networks, stimulating the interest in jointly adopting several advanced techniques (e.g. multiple antennas and heterogeneous networks (HetNets)). Analysis of the performance of the HetNets jointly with multiple antennas becomes crucial. In this paper, we analyse the K-tier multi-antenna HetNets from a downlink coverage perspective. The coverage probability is derived using the Gil-Pelaez inversion theorem under the stochastic geometry framework. Moreover, a closed form approximated result is obtained for observing the influence of the normalized range bias (NRB). The result shows that our proposed result closely match the Monte Carlo simulation, and the approximation result is effective for searching the optimal NRB.

The Long Term Evolution-Unlicensed (LTE-U) scheme has been proposed to exploit the unlicensed spectrum, especially the 5 GHz band, for cellular networks to further increase capacity. Since the 5 GHz band has already been used by WiFi networks, the carrier sense adaptive transmission (CSAT) scheme has been proposed LTE-U access points (LAPs) to harmoniously coexist with WiFi access points (WAPs), where duty cycles are used by LAPs to leave certain time slots that only allow WAPs to access the unlicensed band. However, the performance of the CSAT scheme has not been sufficiently analyzed for multiple unlicensed channels (UCs) in a large-scale network. In this work, we derive the explicit expressions of downlink successful transmission probabilities (STPs) of LAP users and WAP users for a large-scale multi-UC network using stochastic geometry tools. Based on the derived STPs, the fairness between the LTE-U network and the WiFi network, which is defined as the minimum throughput of LTE-U and WiFi users, are analysed versus the duty cycle and the LAP density. Furthermore, the optimal duty cycle is obtained based on the derived STPs in the duty-cycle and non-duty-cycle durations. Our results show that for a given number of UCs and WAP density, and with the optimal duty cycle, the optimal LAP density increases with the increasing number of UCs for maximizing the fairness, but leads to a poorer minimum throughput performance.

The dense deployment of small cells has been considered as a promising approach to providing high quality of service (QoS) to mobile users. However, the resulting dense short-range line-of-sight (LoS) links bring new challenges to interference management, because the interfering links from neighbouring cells are also likely to be in LoS, thus leading to a severe inter-cell interference. In this paper, we propose a user-centric base station (BS) clustering strategy for a non-coherent joint transmissions (JTs) in dense small-cell networks. The main idea is to cluster BSs in LoS with a user to serve it, by selecting the BSs with their LoS probabilities to such user above a predefined threshold. As a result, strong LoS links serve the user, while non-line-of-sight (NLoS) links interfere it. The proposed BS clustering strategy is then evaluated in four different communication environments, as specified by their corresponding path loss models. Our simulation results show that the coverage probability and spectrum efficiency achieved by the proposed user-centric BS clustering strategy increase with the BS density even at extremely high BS densities in all four considered environments.

In recent years, the explosion of wireless data traffic has resulted in a trend of large scale dense deployment of small cells, with which the rising cost of energy has attracted a lot of research interest. In this paper, we present a novel sleeping mechanism for small cells to decrease the energy consumption of heterogeneous networks. Specifically, in the cell-edge area of a macrocell, the small cells will be put into sleep where possible and their service areas will be covered by the range-expanded small cells nearby and the macrocell; in areas close to the macrocell, the user equipments associated with a sleeping small cell will be handed over to the macrocell. Furthermore, we use enhanced inter-cell interference coordination techniques to support the range expanded small cells to avoid QoS degradation. Using a stochastic geometry-based network model, we provide the numerical analysis of the proposed approach, and the results indicate that the proposed sleeping mechanism can significantly reduce the power consumption of the network compared with the existing sleeping methods while guaranteeing the QoS requirement.

Mobile edge computing (MEC) has emerged as a potential paradigm to enhance the processing capabilities of mobile user equipments (MUEs), while edge caching has become a promising means of alleviating traffic in the backhual. In this paper, we formulate a stochastic optimization problem to maximize the average economical profit of MEC server by jointly optimizing offloading decision and caching decision making, and the allocation of radio, computing, and caching resources in a cellular network, with network stability taken into account. To tackle this problem, we develop an online algorithm referred to as dynamic joint computation offloading, resource allocation, and content caching algorithm (DJORC) based on Lyapunov optimization theory. Specifically, the proposed DJORC only needs the current states of the system, and without requiring any prior-knowledge. By further using 0-1 integer programming and linear programming, the closed-form solution of the formulated problem is obtained. Simulation results are presented to verify the performance of DJORC under different parameter settings, as well as the performance gains obtained by DJORC over other existing schemes.

Internet of things (IoT) are envisioned to be an essential in our daily lives, but most IoT devices (IDs) are battery powered and have limited resource. Recently, fog computing (FC) has been proposed to support IoT systems, where part or all of the data are offloaded from IDs to fog nodes for processing or computation. In this paper, we propose to optimise the partial computation offloading in an OFDMA based FC IoT system, while ensuring fairness among IoT links with respect to their energy consumption. In particular, we minimize the energy consumption of the worst-case link by jointly optimizing the size of offloaded data and the assignment of subcarriers, while guaranteeing the rate requirement. The formulated min-max energy efficiency optimization problem (MEP) is solved using Lagrangian dual decomposition and subgradient projection, bases on which we propose an iterative algorithm. Our simulation results show that the proposed resource allocation algorithm is more energy efficient than the existing algorithms for FC supported IoT, while achieving fairness among IoT links.

In this paper, we develop computation offloading scheme based on device-to-device (D2D) communications. The scheme is proposed for effective computation execution where some mobile devices (MDs) could offload their computation intensive tasks to appropriate nearby MDs where necessary, with the assistance of the base station. Accordingly, we formulate a stochastic optimization problem to minimize the average expenses (e.g., wireless communication expense, computation service expense) of MDs in task offloading, while considering the computation resource budget constraint to restraint the behavior of the overuse of computation resource and guarantee mobile users' motivation for collaboration not impaired. To solve this problem, we propose an algorithm that does not need any prior- knowledge of available resources of MDs, referred to as the SEEP. To address a couple and mixed combinational subproblem in the SEEP, we decouple optimization variables for suboptimal. By doing so, both task scheduling and subcarrier assignment are obtained in closed forms, while power allocation is solved by developing efficient iterative algorithm that exploits D.C. (difference of convex functions) structure. Simulation results show the convergence of the SEEP, and illustrate SEEP can flexibly coordinate the tradeoff between expenses and delay, and can substantially reduce expenses of MDs against other existing schemes.

Traditional pricing of a cellular network operator considers the bandwidth utilization for users and operational cost. In this paper, we re-examine this critical subject through a holistic engineering view taken energy efficiency into account. We treat this issue as a two-stage Stackelberg game between the operator and mobile users, where the operator determines the price of unit bandwidth, and accordingly the users choose the amount of requested bandwidth to maximize their payoff. To reach a general result, randomly deployed base stations in large scale have been taken into consideration. By stochastic geometry, we derive the operator's profit and the energy efficiency per unit area, while considering the actual bandwidth demand of users based on their acceptable price. Simulation results show that the operator's profit and the energy efficiency can be achieved their maximum at nearly the same price (i.e., optimal price), and the optimal pricing strategy varies with the deployment density of base stations.

Energy sharing among energy harvesting base stations (BSs) has the potential to improve the utilization of the harvested renewable energy. However, not much work has been done to optimize the power sharing among BSs while considering the topology of the cellular network and the distance-dependent power loss (DDPL) in the transmission lines. In this paper, we propose two power sharing optimization algorithms: the min-cost-max-flow (MCMF) algorithm and the max-flow (MF) algorithm. The MCMF algorithm optimizes the power sharing considering the DDPL, and therefore shares the power over much shorter distances whereas the MF algorithm optimizes the power sharing without considering the DDPL. Our numerical results show that for cellular networks with moderate DDPL value, the MCMF saves up to 10%, 22%, and 30% more power than the MF algorithm for 5, 10, and 15 BSs uniformly distributed in a square of unit length where every pair of BSs can share power, respectively. In contrast, for cellular networks with very high or very low DDPL value, the performance difference between the two algorithms is negligible. In addition, the performance gain of MCMF over MF increases with the BS density.

Power splitting (PS) scheme design is one of the most important challenges in simultaneous wireless information and power transfer-based two-way multiplicative amplify-and-forward relay networks. In this letter, we propose a novel dynamic asymmetric PS (DAPS) scheme to minimize the system outage probability by exploiting the asymmetric instantaneous channel gains between the relay node and the destination nodes. As the formulated optimization problem is a nonconvex problem and difficult to solve, we reformulate it as a fractional programming problem and propose a Dinkelbach-based iterative algorithm to obtain the optimal asymmetric PS ratios. Both the analytical and simulation results demonstrate that the proposed DAPS scheme with the same channel state information overhead can significantly reduce the system outage probability as compared with the existing static symmetric PS scheme.

Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power, and radio bandwidth while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization, and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC, and the number of beneficial UEs over existing algorithms.

In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for the multiple nonholonomic mobile robots formation. First, a model-based monocular vision method is developed to obtain the location information of the leader. Then, a separation-bearing-orientation scheme (SBOS) control strategy is proposed. During the formation motion, the leader robot is controlled to track the desired trajectory and the desired leader-follower relationship can be maintained through the SBOS method. Finally, the model predictive control (MPC) is utilized to maintain the desired leader-follower relationship. To solve the MPC generated constrained quadratic programming problem, the neural-dynamic optimization approach is used to search for the global optimal solution. Compared to other existing formation control approaches, the proposed solution is that the NMPC scheme exploit prime-dual neural network for online optimization. Finally, by using several actual mobile robots, the effectiveness of the proposed approach has been verified through the experimental studies.

Device-to-device (D2D) communications in cellular networks enable direct transmissions between user equipments (UEs). If the source UE (SUE) and the destination UE (DUE) are far away from each other or the channel between them is too weak for direct transmission, then multi-hop D2D communications, where relay UEs (RUEs) forward the SUE's data packets to the DUE, can be used. In this paper, we propose an energy-efficient optimal adaptive forwarding strategy (OAFS) for multi-hop D2D communications. OAFS adaptively chooses between the best relay forwarding (BRF) mode and the cooperative relay beamforming (CRB) mode with the optimal number of RUEs, depending on which of them provides the higher energy efficiency (EE). To reduce the computational complexity for selecting the optimal RUEs for CRB mode, we propose a low-complexity sub-optimal adaptive forwarding strategy (SAFS) that selects between the BRF and the CRB with two RUEs by comparing their EE. Furthermore, a distributed forwarding mode selection approach is proposed to reduce the overhead for forwarding mode selection. The analytical and simulation results show that OAFS and SAFS exhibit significantly higher EE and spectral efficiency than BRF, CRB, direct D2D communications, and conventional cellular communications. SAFS is almost as energy- and spectral-efficient as OAFS.

Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations and to improve the spectral and energy efficiency of cellular networks. The success of D2D communications relies on the willingness of users to share contents. In this paper, we study an economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D-assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Txs, the operator's incentives, and the popularity of contents. The closed-form expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D-assisted offloading network can be significantly improved with respect to the conventional cellular networks, where the D2D communications are disabled.

Photovoltaic (PV) cell powered base stations (BSs) have been widely considered for reducing the cellular network's environmental footprint in the future. An inherent challenge is to match the energy generation profile with the energy consumption profile. In this paper, we develop a Markov chain based algorithm to determine the optimal orientation angle of the PV cell for matching the two profiles, given the energy generation profile of the geo-location, the load profile of the BS and the battery capacity. We investigate the effects of different battery capacities on the optimized PV cell orientation angle for BSs located in a business district in London in summer. Our results show that the optimal orientation angle for a small battery capacity (10000 Joules at a small BS) is in the range from 35° to 60°, whereas a wider range from -50° to 60° could be chosen for a large battery capacity (50000 Joules at a small BS). This reveals that PV cell orientation angle optimization is more important for PV cell powered BSs with small battery capacities than for large battery capacities.

Licensed-assisted access (LAA) is a promising technology to meet the exponential increase of traffic demand by exploiting the 5GHz unlicensed spectrum. However, without proper coexistence schemes, the performance of WiFi in coexistence with LAA will be degraded significantly. Equipped with listen-before-talk (LBT) schemes, LAA applies a channel access mechanism similar to distributed coordination function (DCF) in WiFi, which is not optimal in terms of spectrum efficiency. In this paper, we propose an joint optimization scheme to find the optimal combination of WiFi and LAA contention windows (CWs) that maximizes LAA throughput, while guarantees WiFi throughput above a predefined threshold. The accuracy and efficiency of the proposed scheme is evaluated by comparing with the exhaustive search: almost the same combination of CWs are achieved with much lower complexity by using the proposed scheme than the exhaustive search. Numeric results show that the proposed adaptive scheme is more effective in dense scenario, where high system (up to 40%) and LAA throughput gain (up to 100%) are achieved. The trade-off between LAA (system) throughput and WiFi throughput is also revealed.

Device-to-device (D2D) communications in cellular networks facilitate direct transmissions between user equipment (UEs). In the case that direct transmission is not favourable due to long distances or poor channel conditions between the source UE (SUE) and the destination UE (DUE), multi-hop D2D communications can be deployed, where relay UEs (RUEs) forward the data received from SUE to the DUE. In this paper, we propose an adaptive forwarding strategy for multi-hop D2D communications to optimally choose between the best relay forwarding (BRF) mode and the cooperative relays beamforming (CRB) mode with the optimal number of RUEs, depending on which of them provides the higher instantaneous energy efficiency (EE). We analyse the associated average EE taking into account the circuit power consumption and the overhead for obtaining channel state information (CSI), forwarding mode selection and cooperative beamforming. In order to reduce the overhead for mode selection, we propose a distributed adaptive forwarding mode selection strategy. Simulation results reveal that multi-hop D2D communications utilizing the proposed adaptive forwarding strategy achieves significantly higher EE than the BRF, CRB with the optimal number of RUEs, direct D2D communications, and conventional cellular communications.

Traffic offloading via device-to-device (D2D) communications has been proposed to improve the network capacity and alleviate the increasing traffic burden on cellular base stations (BSs). However, the success of D2D communications largely relies on the D2D transmitters' (D2D-Txs) willingness of sharing contents (due to the energy consumption for transmission). In this paper, we model and analyze wireless powered D2D-assisted offloading (WPDO) in underlying cellular networks, where the D2D-Tx is allowed to receive power from the nearest BS as well as other interfering BSs, and then D2D-Tx broadcasts the popular contents to nearby users. The average received power at D2D-Tx and the success probability of D2D-Tx transmission are derived. Furthermore, based on the proposed model, we maximize the network energy efficiency while guaranteeing users' required data rates. Our results confirm that the maximum energy efficiency of the WPDO network can be achieved by jointly optimizing the fraction of time for wireless power transfer and the offloading range of D2D-Tx.

Despite the increasing interest in photovoltaic (PV) cell powered small-cell base stations (SBSs), it has not been sufficiently studied yet how different PV cell angles can be utilized to achieve a good match between the energy arrival and consumption at the SBS. This is especially important in an urban environment where cellular network operators often struggle to apply optimal angles to the PV cells due to implementation constraints or shadowing effects of surrounding buildings. We develop an energy generation, storage and consumption model of a PV cell powered SBS, which includes the effects of PV cell orientations. A linear optimization problem is derived to optimize the energy performance of the SBS throughout the day. The effects of different PV cell orientations on the green energy utilization are evaluated assuming deployment in London in summer and winter. Our results show that west orientated PV cells (45° misalignment to the southern direction) are preferred in London during summer, whereas south orientated ones are preferred during winter. This reveals that the PV cell orientation needs to be optimized for not only the location and weather of the SBS deployment site but also the network traffic distribution in time (or energy consumption profile) of the deployment location.

In this paper, we investigate profit maximization problem in wireless powered communication (WPC) systems, where the profit is defined as the difference between the revenue earned from uplink data transmission and total energy cost of the system. Since the efficiencies of energy harvesting (EH) circuits and battery storage are both non-linear, conventional linear models will lead to suboptimal resource allocation. In this paper, we propose to maximize the profit of a WPC system under nonlinear energy harvesting and storage models. In order to solve the non-convex optimization problem, we develop a piecewise linear EH model, which is more accurate than conventional linear EH models and existing non-linear EH models. Numerical results show that the power and time allocation design based on the non-linear energy harvesting and storage models outperforms that based on existing linear models in terms of the profit.

License-assisted access (LAA) is a candidate feature in 3GPP Rel-13 to meet the explosive growth of traffic demand. The main idea of LAA is to deploy LTE in the unlicensed band (mainly the 5GHz band), which is abundant with available spectrum. However, the major concern is the coexistence between WiFi and LAA in the same band. This paper presents a new framework to evaluate the downlink performance of coexisting LAA and WiFi networks. By using Markov chain, analytical models are established based on WiFi distributed coordination function (DCF) and two listen-before- talk (LBT) schemes. These two LBT schemes are Cat 3 and Cat 4 LBT, which mainly differ in medium access schemes in terms of backoff procedure. Unlike most existing works, which focus on the impact on WiFi performance posed by LAA, the performance of LAA is also evaluated. Our analysis shows that throughput of a WiFi network can be enhanced by adding or replacing WiFi access points (APs) with LAA E-UTRAN Node Bs (eNBs), at the expense of different levels of WiFi performance degradation. A trade-off between WiFi protection and LAA-WiFi system performance enhancement is observed. WiFi throughput and delay are less affected by Cat 4 LBT scheme, while Cat 3 LBT scheme provides higher LAA-WiFi system throughput. The choice of LBT schemes relies on the network planning priority, WiFi performance protection and LAA system performance requirements.

LTE License-Assisted Access (LTE-LAA) in unlicensed spectrum has drawn a lot of attention due to its appealing data traffic offloading potential. However, the coexistence of LTE and WiFi technologies in the same unlicensed bands needs careful design to avoid severe interference between them. In this paper, we propose a coexistence scheme to create opportunities for LTE-LAA small cells and WiFi devices to transmit simultaneously. We combine Multiple Signal Classification (MUSIC) direction of arrival (DOA) estimation with null steering techniques to avoid collisions between LTE-LAA and WiFi transmissions. We assume that the LTE-LAA small cells are equipped with the latest 802.11 receivers for monitoring WiFi transmissions and for capturing simultaneous transmission timing. The performance of the proposed scheme in terms of collision avoidance and channel occupancy time ratio is evaluated via simulations. The results show that with DOA estimation and null steering, LTE-LAA small cells can transmit simultaneously with nearby WiFi devices without causing significant interference to them. As a result, LTE-LAA small cells can gain much more channel access opportunities and longer channel occupancy time while being “invisible” to coexisting WiFi networks.

In recent years, the explosion of data traffic usage in wireless industry has resulted in a trend of hyper-dense small cells. With the large scale deployment of small cells, the rising cost of energy has attracted lots of interests. In this paper, we present a novel activation and sleep mechanism for small cells to decrease the energy consumption in heterogeneous networks (HetNets). The main idea of our proposed method is explained as follows: in the cell-edge area of a macrocell, the service area of a sleeping small cell will be covered by a range expanded small cell nearby; in areas close to the macro BS, UEs associated with a sleeping small cell will be handed over to the macrocell. Furthermore, we use enhanced inter-cell interference coordination (eICIC) techniques to support range expanded small cells to avoid QoS degradation. The simulation results indicate that the proposed sleeping mechanism can significantly reduce the energy consumption of the network compared with the conventional methods while guaranteeing the QoS requirement.

Energy efficient wireless communications have recently received much attention, due to the ever-increasing energy consumption of wireless communication systems. In this paper, we propose a new energy-efficient cooperative relaying scheme that selects a subset of relays before data transmission, through proactive participation of available relays using their local timers. We perform theoretical analysis of energy efficiency under maximum transmission power constraint, using practical data packet length, and taking account of the overhead for obtaining channel state information, relay selection, and cooperative beamforming. We provide the expression of average energy efficiency for the proposed scheme, and identify the optimal number and location of relays that maximize energy efficiency of the system. A closed-form approximate expression for the optimal position of relays is derived. We also perform overhead analysis for the proposed scheme and study the impact of data packet lengths on energy efficiency. The analytical and simulation results reveal that the proposed scheme exhibits significantly higher energy efficiency as compared to direct transmission, best relay selection, all relay selection, and a state-of-the-art existing cooperative relaying scheme. Moreover, the proposed scheme reduces the signaling overhead and achieves higher energy savings for larger data packets.

Device-to-device (D2D) communications utilizing licensed spectrum have been considered a promising technology to improve cellular network spectral efficiency and offload local traffic from cellular base stations (BSs). In this paper, we develop Iunius: a peer-to-peer (P2P) system based on harvesting data in a community utilizing multi-hop D2D communications. The Iunius system optimizes D2D communications for P2P local file sharing, improves user experience, and offloads traffic from the BSs. The Iunius system features cross-layer integration of: 1) a wireless P2P protocol based on the BitTorrent protocol in the application layer; 2) a simple centralized routing mechanism for multi-hop D2D communications; 3) an interference cancellation technique for conventional cellular (CC) uplink communications; and 4) a radio resource management scheme to mitigate the interference between CC and D2D communications that share the cellular uplink radio resources while maximizing the throughput of D2D communications. Simulation results show that the proposed Iunius system can increase the cellular spectral efficiency, reduce the traffic load of BSs, and improve the data rate and energy saving for mobile users.

Exponentially growing demand for mobile data is pushing the existing mobile networks to the limit. In addition to offloading traffic onto WiFi access points (APs), dual-band femtocells (DBFs) that can simultaneously access both the licensed and unlicensed bands have been proposed for cellular networks to further exploit the unlicensed spectrum. However, the coexistence between DBFs and WiFi in the unlicensed spectrum is not without difficulty, given the potentially severe co- channel interference. In this paper, we first derive the probability of coverage overlap between DBF and WiFi APs by modelling the network nodes using a spatial bivariate Poisson point process, and then propose a novel framework for DBFs to effectively coexist with WiFi APs in the unlicensed spectrum considering various possibilities of coverage overlap between them. The performance of the proposed framework in terms of DBF channel occupancy in the unlicensed band is evaluated through both theoretical analysis and simulations.

Energy efficiency of wireless communications has received a lot of attention recently owing to the tremendous energy demands resulting from widely deployed wireless networks and mobile personal devices. In this paper, we propose an energy-efficient cooperative relaying scheme for location-aware clustered relays. Energy efficiency is achieved through a low-overhead timer-based relay selection algorithm and by removing the destination feedback of channel state information (CSI) that is usually required for cooperative beamforming. In order to avoid possible collisions between relay transmissions, we propose a distributed mechanism for the selected relays to appropriately insert guard intervals before their transmissions. We analyse the system energy efficiency considering the energy utilized to obtain CSI, to select best relays and to perform cooperative beamforming. Moreover, we investigate the impacts of the number of available relays, the number of selected relays and the location of relay cluster on energy efficiency. The simulation results reveal that the proposed cooperative relaying scheme achieves higher energy efficiency than direct communication, best relay selection, and all relay selection for relay clusters located close to the source. Independent of relay cluster location, the proposed scheme exhibits significantly higher energy efficiency compared to an existing cooperative relaying scheme.

Multi-hop cooperative communications have been considered to increase the coverage range of device-to-device (D2D) communications underlaying cellular networks. Whilst network coding (NC) has been proven as an efficient technique to improve the throughput of ad-hoc networks, it has not been adapted to D2D networks. In this paper, we propose a NC-D2D system, in which we apply NC into the multi-hop D2D communications to enhance its throughput. We then develop a radio resource management (RRM) mechanism that jointly optimises the power control and subchannel allocation for NC-D2D communications. The proposed RRM maximises the throughput of NC-D2D communications while guaranteeing the quality of service (QoS) requirements of conventional cellular (CC) UEs. We also investigate how caching capabilities at relay nodes would impact the system performance. Simulation results show that by employing NC and our proposed RRM mechanism, the throughput of multi-hop D2D communications is significantly improved and the time required for exchanging large data files between a D2D pair is dramatically reduced as compared to existing multi-hop D2D schemes.

Reliability is one of the most concerned issues in wireless communications for smart grid, especially in the home area network(HAN) where the smart meter data are collected. In this paper, we model the HAN as a wireless mesh network that deploys cooperative transmissions for improving link reliability. We then define a new reliability performance metric that depends not only on the channel model but also on the locations of smart meters. Based on the reliability performance metric, we propose a smart meter deployment strategy to maximize the communication reliability. The performance of the proposed HAN deployment strategy is applied to a real home environment. The simulation results show that the proposed deployment strategy is effective in guaranteeing reliability for smart-grid communications.

Energy efficiency has become a crucial performance metric in wireless communications due to the increasing energy consumption resulting from increasingly widely deployed wireless communication systems and devices. We propose a timer based relay selection scheme that selects a subset of available relays to perform cooperative beamforming for data transmission in an energy-efficient way. We analyse the resulting energy efficiency taking into account the additional energy consumed by the overhead required to obtain channel state information, to select relay subset and to perform cooperative beamforming. We also study the impact of the number of cooperating relays, their location and different data packet length on energy efficiency. The simulation results show that for relays located close to the source the proposed scheme is more energy efficient than direct communication, conventional best relay selection, all relay selection and an existing cooperative relaying scheme. Moreover, through simulation it is revealed that increasing data packet length leads to significant energy savings.

The split‐symbol moment estimation (SSME) algorithm is a conventional method to estimate the signal‐to‐noise ratio (SNR) in communication systems for M ‐ary phase shift keying signals. However, the conventional SSME is based on the assumption of symmetric pulse‐shaping waveforms, and becomes no longer proper when the shaping pulse is asymmetric. In this study, the authors propose a modified SSME algorithm for asymmetric pulse‐shaped signals, based on an odd‐even symbol splitting approach. Simulation results show that the modified SSME outperforms the conventional SSME for both symmetric and asymmetric pulse‐shaped signals in terms of SNR estimation accuracy.

Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.

Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, in this paper, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and Lambert-W function. Moreover, we theoretically prove the existence, uniqueness, and fairness of the solution to this game model. Accordingly, a cooperative Nash bargaining resource allocation algorithm is developed, which is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithms for efficient and fair resource allocation in cognitive small cell networks.

With the introduction of femtocells, cellular networks are moving from the conventional centralized network architecture to a distributed one, where each network cell should make its own radio resource allocation decisions, while providing inter-cell interference mitigation. However, realizing such distributed network architecture is not a trivial task. In this paper, we first introduce a simple self-organization rule, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern. Based on such self-organization rule and taking realistic resource allocation constraints into account, we also propose two novel resource allocation algorithms, being autonomous and coordinated, respectively. Performance of the proposed self-organization rule and resource allocation algorithms are evaluated using system-level simulations, and show that power efficiency is not necessarily in conflict with capacity improvements at the network level. The proposed resource allocation algorithms provide significant performance improvements in terms of user outages and network capacity over cutting-edge resource allocation algorithms proposed in the literature.

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.

In this paper, a novel parameter named cross-tier interference to signal ratio (CISR) is defined to characterize cross-tier interference in the co-channel deployed two-tier macro and femto networks. Cross-tier interference to signal budget (CISB) is derived to characterize the tolerance of cross-tier interference for given per-tier downlink signal to interference ratio (SIR) targets and outage probability constraints. Network load is defined to show how the two-tier network is affected by parameters of the femto tier. Based on the analysis of CISRs and network load, the maximum density of simultaneously transmitting co-channel femtocells, tight upper and lower bounds of transmission power of femtocells for guaranteeing reliable macro and femto coverage in the two-tier network are obtained. A decentralized FAP regulation strategy which aims to maximize the number of co-channel transmitting femtocells under consideration of guaranteeing reliable macro and femto coverage is proposed. Simulation results verify our theoretical claims and demonstrate the effectiveness of the proposed femto regulation scheme in terms of spatial reuse efficiency.

This paper addresses the dichotomy that exists in the analysis of wireless channels, which exists between employing: the tractable Shannon capacity bound and the accurate simulated capacity of modulation and coding schemes (MCSs). By considering the effects of mutual information saturation in modulation schemes and the efficiency of error correction codes, the paper proposes a new tractable capacity expression that can accurately represent any MCS and adaptive combinations. To demonstrate this methodology, the paper considers the adaptive MCS capacity for the Long-Term-Evolution (LTE) system. The potential benefit is that system-level optimization can employ the more accurate capacity expression. In the second part of the paper, the proposed capacity expression is applied to optimize power allocation to the parallel channels of a MIMO system. The results show that when mutual information saturation occurs, neither the water-filling nor channel inversion schemes are optimal. In fact the optimal power allocation is a combination of the two aforementioned schemes, switching adaptively between their relative merits.

In cellular networks, balancing the throughput among users is important to achieve a uniform Quality-of-Service (QoS). This can be accomplished using a variety of cross-layer techniques. In this paper, the authors investigate how the down-tilt of base-station (BS) antennas can be adjusted to maximize the user throughput fairness in a heterogeneous network, considering the impact of both a dynamic user distribution and capacity saturation of different transmission techniques. Finding the optimal down-tilt in a multi-cell interference-limited network is a complex problem, where stochastic channel effects and irregular antenna patterns has yielded no explicit solutions and is computationally expensive. The investigation first demonstrates that a fixed tilt strategy yields good performances for homogeneous networks, but the introduction of HetNet elements adds a high level of sensitivity to the tilt dependent performance. This means that a HetNet must have network-wide knowledge of where BSs, access-points and users are. The paper also demonstrates that transmission techniques that can achieve a higher level of capacity saturation increases the optimal down-tilt angle. A distributed reinforcement learning algorithm is proposed, where BSs do not need knowledge of location data. The algorithm can achieve convergence to a near-optimal solution rapidly (6-15 iterations) and improve the throughput fairness by 45-56% and the energy efficiency by 21-47%, as compared to fixed strategies. Furthermore, the paper shows that a tradeoff between the optimal solution convergence rate and asymptotic performance exists for the self-learning algorithm.

We propose a distributed and coordinated radio resource allocation algorithm for orthogonal frequency division multiple access (OFDMA)-based cellular networks to self-organize efficient and stable frequency reuse patterns. In the proposed radio resource allocation algorithm, each cell independently and dynamically allocates modulation and coding scheme (MCS), resource block (RB), and transmit power to its users in a way that its total downlink (DL) transmit power is minimized, while users' throughput demands are satisfied. Moreover, each cell informs neighboring cells of the RBs that have been scheduled for its cell-edge users' DL transmissions through message passing. Accordingly, the neighboring cells abstain from assigning high transmit powers to the specified RBs. Extensive simulation results attempt to demonstrate that DL power control on a per-RB basis may play a key role in future networks, and show that the distributed minimization of DL transmit power at each cell, supported by intercell interference coordination, is able to provide a 20% improvement of network throughput, considerably reduce the number of user outages, and significantly enhance spatial reuse, as compared to cutting-edge resource allocation schemes.

In this paper, we consider the problem of secure resource allocation in orthogonal frequency division multiple access (OFDMA) two-way relay networks. Multiple sources exchange information with the assistance of an amplify-and-forward (AF) relay node in the presence of an eavesdropper. The joint subcarrier allocation, subcarrier pairing and power allocation problem aims to maximize the secrecy capacity for legitimate sources subject to limited power budget and orthogonal subcarrier allocation constraints. The optimization problem is modeled as a mixed integer programming problem, and then solved in an asymptotically optimal manner based on the dual method. Moreover, a suboptimal algorithm is proposed to reduce the complexity. Simulations are conducted to evaluate the effectiveness of the proposed near optimal and suboptimal algorithms.

In this paper, we consider the joint subchannel and power allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing macrocells and femtocells. A joint subchannel and power allocation scheme for co-channel femtocells is proposed, aiming to maximize the capacity for delay-tolerant users subject to delay-sensitive users' quality of service and interference constraints imposed by macrocells. The joint subchannel and power allocation problem is modeled as an mixed integer programming problem, then transformed into a convex optimization problem by relaxing subchannel sharing, and finally solved by a dual decomposition approach. The effectiveness of the proposed approach is verified by simulations and compared with existing scheme.

In this paper, we characterize the relation between handover failure and ping-pong rates in a 3GPP heterogeneous network scenario as a function of relevant system parameters such as time-to-trigger, user equipment velocity, range expansion bias, etc. Under the assumptions that the picocell coverage and radio link failure areas are circular regions, and that users follow linear trajectories, handover failure and ping-pong rates are derived in closed-form expressions. Simulation results verify the accuracy of our analytical derivations, which shed new light on key aspects of the handover process in a heterogeneous network.

Macrocells may suffer serious uplink interference introduced by the deployment of co-channel femtocells. In this paper, an interference-aware pricing-based resource allocation algorithm for co-channel femtocells is proposed to alleviate their interference to macrocells without degrading the femtocell's capacity. The subchannel and power allocation problem is modeled as a non-cooperative game. A suboptimal subchannel allocation algorithm and an optimal power allocation algorithm are proposed to implement the resource allocation game. Simulation results show that the proposed algorithm not only improves the capacity of the macrocell but also the total capacity of the two-tier network, as compared with the unpriced subchannel allocation and Modified Iterative Water Filling (MIWF) power allocation algorithm.

In order to mitigate inter-cell interference and cope with network, traffic, and channel fluctuations in space and time, Orthogonal Frequency Division Multiple Access (OFDMA) based cellular networks should be equipped with dynamic frequency and power allocation algorithms. Although dynamic frequency and power allocation has been well studied separately, very few results have been reported on dynamic joint frequency and power allocation. In this paper, we propose a dynamic frequency and power allocation algorithm for the DownLink (DL) of OFDMA-based cellular networks, where frequency resources are allocated to cells by a central broker to minimize long-term cell-edge inter-cell interference. Moreover, each cell independently assigns its available frequency resources and power to users in a way that its total DL transmission power is minimized while meeting the throughput demands of its users. Simulation results show that the proposed algorithm provides significant improvements in network outage performance, capacity, and throughput as compared with existing schemes.

Femtocells have been considered by the wireless industry as a promising solution not only to improve indoor coverage, but also to unload traffic from already overburdened macrocells. In spite of potentially excessive interference caused by densely deployed femtocells, macrocells may have to share the same spectrum with femtocells, due to spectrum availability and network infrastructure considerations. In this paper, we derive closed-form expressions for downlink outage probabilities (OP) of collocated spectrum-sharing macrocell and femtocells, based on the stochastic geometry theory. Orthogonal frequency division multiple access (OFDMA) is considered in the downlink of both macro- and femto-cells. Simulation results show that the derived OP expressions are able to predict OP performance of collocated spectrum-sharing macro- and femto-cells, and provide insights on the effects of different system and channel parameters on co-channel femtocell deployments.

Because following current cell selection procedures, User Equipments (UEs) connect to the cell that provides the strongest DownLink (DL) Received Signal Strength (RSS), in a Heterogeneous Network (HetNet) comprised of macrocells and picocells, the large difference in DL transmit powers among macro Base Stations (BSs) and pico BSs may significantly reduce the DL service coverage area of picocells. In order to expand the DL coverage of picocells in the presence of macrocells, the concept of range expansion has been recently proposed, in which a positive offset is added to the DL RSSs of picocell pilot signals to increase their DL footprints. Although range expansion may increase the DL coverage of picocells, it may also create significant DL inter-cell interference in the Expanded Region (ER) of picocells, since pico ER UEs do not connect to the cells that provide the strongest DL RSSs. In this paper, we propose a novel cooperative macrocell-picocell scheduling approach to mitigate macrocell DL inter-cell interference towards pico ER UEs. Simulation results show that expanded region picocells supported by the proposed cooperative macrocell-picocell scheduling approach are able to mitigate UE outages and significantly improve spatial reuse of spectrum.

With the introduction of femtocells, cellular networks are moving from the conventional centralised architecture to a distributed one, where each network cell should make its own radio resource management decisions, while providing inter-cell interference mitigation. However, realising this distributed cellular network architecture is not a trivial task. In this paper, we first introduce a simple self-organisation rule under which a distributed cellular network is able to converge into an efficient resource allocation pattern, then propose a novel resource allocation model taking realistic resource allocation constraints into account, and finally evaluate the performance of the proposed self-organisation rule and resource allocation model using system-level simulations.

Due to considerations of spectrum availability and network infrastructure, existing macrocell networks may have to share the spectrum with overlaid femtocells. In spectrum-sharing macro and femto networks, different transmit powers used by macro base stations (MBS) and femto access points, together with potentially densely deployed femtocells, may create dead spots where reliable coverage cannot be guaranteed to either macro or femto users. In this paper, we devise a decentralized strategy to regulate femtocell management of transmit power and usage of radio resources depending on its distance from the closest MBS. Simulation results for the orthogonal frequency division multiple access (OFDMA) downlink (DL) of spectrum-sharing macro and femto networks show that the proposed decentralized femtocell regulating strategy is able to guarantee reliable DL coverage over targeted macro and femto service areas while providing superior spatial spectrum reuse, for even a large number of spectrum sharing femtocells deployed per cell site.

Femtocells have been considered by the wireless industry as a cost-effective solution not only to improve indoor service providing, but also to unload traffic from already overburdened macro networks. Due to spectrum availability and network infrastructure considerations, a macro network may have to share spectrum with overlaid femtocells. In spectrum-sharing macro and femto networks, inter-cell interference caused by different transmission powers of macrocell base stations (MBSs) and femtocell access points (FAPs), in conjunction with potentially densely deployed femtocells, may create dead spots where reliable services cannot be guaranteed to either macro or femto users. In this paper, based on a thorough analysis of downlink (DL) outage probabilities (OPs) of collocated spectrum-sharing orthogonal frequency division multiple access (OFDMA) based macro and femto networks, we devise a decentralized strategy for an FAP to self-regulate its transmission power level and usage of radio resources depending on its distance from the closest MBS. Simulation results show that the derived closed-form lower bounds of DL OPs are tight, and the proposed decentralized femtocell self-regulation strategy is able to guarantee reliable DL services in targeted macro and femto service areas while providing superior spatial reuse, for even a large number of spectrum-sharing femtocells deployed per cell site.

Assessing and quantifying the effect of fundamental propagation mechanisms is essential for the accurate design and planning of wireless networks in metropolitan areas. In this paper, the finite-difference time-domain (FDTD) method is employed to investigate basic propagation mechanisms in simplified urban environments. In particular, we study propagation effects for WiMAX signals in the presence of simple obstacles. We compare FDTD results with empirical model ITU-R P.526-10, which considers only the effect of diffraction. Our goal is to investigate and interpret discrepancies between the empirical model and the FDTD results, which can be attributed to multiple reflection and transmission effects. To this end, we also introduce more complicated obstacles than previous studies. Our results confirm that the impact of multiple reflections becomes more dominant as the channel becomes more non-Line-Of-Sight.

In this paper, we present an innovative ARQ approach for mobile multicast, which utilizes packet-combining to reduce the number of retransmissions. We adopt the proposed ARQ scheme jointly with adaptive modulation and coding (AMC) in a cross-layer design framework to improve the average spectral efficiency per receiver under specific QoS constraints. Expressions are derived for the proposed joint ARQ-AMC schemes, which are then compared with existing multicast schemes under Rayleigh fading channels for different Signal-to-Interference-and-Noise-Ratios.

The log-normal probability distribution has been commonly used in wireless communications to model the shadowing and, recently, the small-scale fading for indoor ultrawideband (UWB) communications. In this paper, a tight closed-form approximation of the ergodic capacity over log-normal fading channels is derived. This expression can be easily used to evaluate and compare the ergodic capacities of communication systems operating over log-normal fading channels. We also utilize this expression to show that the capacity of a multi-antennas UWB system operating over the IEEE 802.15.3a channel can be improved mainly through receive diversity.

In this paper, we propose a pilot-channel-assisted log-likelihood-ratio selective combining (PCA-LLR-SC) scheme for Rake receivers that will be used in long-range low-rate UWB communications targeted by the IEEE 802.15.4a specification. The pilot and data channels are constructed using quadrature sinusoidal bursts that have the same Gaussian envelope. The system parameters are optimized through jointly minimizing the channel estimation mean square error and maximizing the receiver output signal-to-noise ratio. Simulation results show that the proposed PCA-LLR-SC scheme is able to provide robust low-rate UWB communications in fast-fading multipath channels.

In this paper, we provide performance analysis for a pilot-channel-assisted log-likelihood-ratio selective combining (PCA-LLR-SC) scheme that is designed to support robust long-range low-rate UWB communications targeted by the IEEE 802.15.4a. Including the loss of orthogonality between data and pilot pulses caused by the UWB multipath channel, a closed-form bit-error-rate expression is derived, and verified by comparisons with simulation results. Our analytical result shows its accuracy in performance evaluation for the PCA-LLR-SC scheme and confirms that PCA-LLR-SC is able to support robust low-rate UWB communications in fast-fading multipath channels.

The log-normal probability distribution is commonly used in wireless communications to model the shadowing and, more recently, the small-scale fading for indoor ultra-wideband communications. In this paper, an accurate closed-form approximation of the ergodic capacity over log-normal fading channels is derived. This expression can be easily used to evaluate and compare the ergodic capacity of communication systems operating over log-normal fading channels.

For ultra-wideband (UWB) communications with signal energy dispersed by a large number of multipath compnents, the design of a Rake receiver that can provide a desirable output signal-to-noise ratio (SNR) using only a moderate number of fingers becomes an important issue. In this paper, we propose a pilot-channel-assisted log-likelihood-ratio selective combining (PCA-LLR-SC) scheme for UWB Rake receivers to be used in long-range low-rate UWB communications envisioned by the IEEE 802.15.4a PHY specification. The pilot and data channels are constructed using quadrature sinusoidal bursts that have the same Gaussian envelope. The system parameters are optimized through jointly minimizing the channel estimation mean square error and maximizing the receiver output SNR. Extensive simulations confirm that the proposed PCA-LLR-SC scheme is capable of providing robust low-rate UWB communications in fast-fading multipath channels and in the presence of multi-user interference.

Providing diversity is one of the two possible objectives (the other one is providing multiplexing) of a multi-input multi-output (MIMO) system. For ultra wideband (UWB) impulse radio systems using rake receivers, there are already richly inherent multipath diversities. However, there may be UWB applications making use of more than one UWB transmit antenna and/or receive antenna, which leads to UWB MIMO systems. The difference between the diversity analyse of a UWB MIMO system from that of a narrow band system lies in the different choice of propagation channel model for the branches linking each transmit antenna and receive antenna. Similar to the narrow band system, the existence of the correlation between the branches can severely degrade the diversity performance, and complicate the performance analyse. In this paper, the wideband propagation channel for the UWB MIMO system, with pre-defined correlation model for the branches, is broken down into several uncorrelated elementary narrow-band propagation channels with equivalent diversity performance. Based on this, a novel method for evaluating the diversity performance of a MIMO UWB impulse radio system is presented.

In this paper, we propose a pulse-coded orthogonal M-ary pulse position modulation (PPM) scheme that employs coded equivalent pulses, for non-coherent receivers operating in low-data-rate ultra-wideband (UWB) communications. Major system parameters, such as the size of pulse coding and the temporal spacing between adjacent pre-coding pulses, are provided to fully exploit the FCC average- and peak-power constraints. Simulation results show that the performance of the proposed scheme in conjunction with a square-law detector applying fixed large integration duration compares favourably with that of a selective Rake receiver, under realistic UWB channel models

In this paper, we provide performance analysis for direct-sequence multiple-access ultra-wideband (UWB) systems operating in multipath environments when the effect of overlap between received pulses is included. Pulse overlap occurs when the pulse width is larger than the fine delay resolution of the UWB channel and is particularly important to consider in multidimensional pulse modulations. A closed-form bit error rate expression is derived and compared with simulations that employ UWB signals complying with the FCC UWB indoor emission mask and the channel model recommended by IEEE 802.15.3a. The detailed theoretical analysis and simulations reveal that channel-induced pulse overlap has a significant impact on UWB system performance. The results also show that compared with binary modulations, multidimensional biorthogonal pulse keying can improve power efficiency for multiple-access UWB systems under multipath fading, thereby providing an advantage for power-limited UWB communications.

We propose a pilot-channel assisted modulation (PCAM) for pulse-based ultra-wideband systems that require accurate channel estimation for RAKE combining. The pilot channel is constructed with pulses that are orthogonal to the data-bearing pulses and each pilot pulse is transmitted together with a data pulse. PCAM parameters are designed to optimize the channel estimation and data detection performance jointly. Simulation results show that PCAM is able to provide desirable performance under channel time-variations and multi-user interference, without sacrificing the data rate.

In this paper, we develop multidimensional modulation for ultra-wideband (UWB) multiple-access (MA) impulse-radio communications in wireless multipath environments. An important advantage of multidimensional modulation over single-dimensional modulation is the possibility of higher power efficiency, and this may be critical in UWB communications. For this reason, we propose a 2MN-ary biorthogonal keying (BOK) scheme, in which N orthogonal pulse shapes in conjunction with M orthogonal codes are employed to construct MN-dimensional biorthogonal modulation. The direct sequence (DS) MA performance of the proposed scheme is analyzed. A closed-form bit-error-rate expression is derived and compared with simulations based on the Intel Labs' UWB channel model. Orthogonal pulse shapes that comply with the Federal Communications Commission UWB indoor-emission limits are used in simulations. Our results confirm the power-efficiency improvement achieved by multidimensional modulation for UWB MA communications under multipath fading.

This letter presents an analysis of the effect of narrowband interference (NBI) on ultrawideband (UWB) time-hopping (TH) systems in the presence of multipath fading using both analytical derivations and simulations. Our analysis demonstrates that NBI may be an issue in some instances. In addition, we suggest three NBI suppression schemes for combating NBI in UWB TH systems. Single-link performance of these schemes in conjunction with a Rake-type receiver structure is estimated for both the ideal all-Rake receiver and the simpler partial-Rake receiver in an indoor environment. Two UWB pulse shapes that meet the Federal Communications Commission rules for UWB communications are considered in the investigation.

This paper introduces a quadrature pulse amplitude modulation (QPAM) scheme for ultrawideband (UWB) wireless communications by utilizing two orthogonal pulses. An important advantage of QPAM over binary PAM is that it is more power efficient and this is critical in UWB communications. In addition we investigate the use of M-ary binary-coded QPAM signals to further increase the bit transmission rate supported by UWB communication systems. The power efficiencies of bipolar QPAM and M-ary binary-coded QPAM are compared. We also present a comparison of the role of time hopping (TH) and direct-sequence (DS) spreading in providing robust performance in multipath environments for UWB communications.