Unmanned Aerial Vehicle (UAV) can enhance communication quality in wireless systems. It is helpful to address the blockage problem in mountainous regions. However, it has high energy consumption. In this paper, we propose a novel blockage model converted blockage region constraints into linear constraint. Then, the joint resource allocation and trajectory design scheme is proposed to maximize energy efficiency (EE). Specifically, we model the blockage effect caused by mountains. The Dinkelbach algorithm is designed to address the fractional objective function for EE. Then, we develop an iterative block coordinate descent (I-BCD) algorithm that alternately optimizes resource allocation and trajectory design subproblems. The resource allocation subproblem employs a dual-loop structure with Lagrangian duality to obtain closed-form solutions for power and bandwidth allocation. Meanwhile, the trajectory optimization uses successive convex approximation (SCA) to transform non-convex constraints into convex optimization problems. Simulation results demonstrate that the proposed scheme improves the EE compared with the benchmark schemes for UAV-assisted communications in mountainous regions.

Exploiting on-device data and computing power for machine learning at the network edge is challenged by constrained device resources, privacy requirements, and local data heterogeneity. To address the above gap, this work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized ML. BagChain integrates blockchain with distributed ML by replacing the computationally costly hash computing in proof-of-work with ML model training and validation, and does not rely on any trusted central servers. Individual miners in BagChain train base models by using their local computing resources and private data and further aggregate these base models, which could be very weak, into strong ensemble models. More specifically, we design a three-layer blockchain structure and associated generation and validation mechanisms to enable distributed ML among uncoordinated miners without revealing raw data. To reduce computational waste due to blockchain forking, we further propose the cross fork sharing mechanism for practical networks with lengthy delay and limited bandwidth. Extensive experiments illustrate the superiority and efficacy of BagChain when handling various ML tasks on both independently and identically distributed (IID) and non-IID datasets. BagChain remains robust and effective even when facing resource constrained mobile devices, heterogeneous private user data, and limited network connectivity. The source code of BagChain is released at: https://github.com/czxdev/BagChain.

This paper considers a real-time scheduling problem disseminating status update of sensors timely from a base station (BS) to users over wireless fading channels. The freshness of the updated status is quantified using the Age of Incorrect Information (AoII) metric. The objective is to minimize the long-term AoII under the constraint of limited transmission power, which necessitates that only a subset of users can successfully receive updates in each time slot. To find an optimal transmission policy, we first model the AoII minimization problem as a multi-action multi-armed bandit problem. After that, we decompose the derived MAB problem into multiple sub-problems. For each sub-problem, we obtain an optimal policy based on multiple threshold, thereby establishing the indexability of the optimal problem. Building upon this, a novel multi-action productivity index (MAPI) is introduced to get the optimal transmission policy. However, due to the computational complexity of the relative value iteration (RVI) algorithm, the exact value of the MAPI remains difficult to determine. To address this challenge, a computationally efficient algorithm is proposed to approximate the indices and derive the transmission policy for each user. Compared with Whittle’s Index and Max Weight policies, MAPI-based policy demonstrates significant performance improvement, particularly in large-scale networks and when the transmission power separation interval is small.

The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications; however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results.

The rapid evolution of blockchain has established it as a critical enabler for decentralized zero-trust services and networks. Without relying on traditional trust mechanisms such as pre-established mutual trust or central authentication, blockchain facilitates trust-free services via smart contract. Smart contracts offer verifiable software trust for various blockchain-enabled services (BESs) while protecting participants’ interests. However, the impact of blockchain on BES remains underexplored and unclear. In this work, we consider a general BES framework suitable for diverse decentralized zero-trust services and assess the role of blockchain in BES. We first build anM/G/1-type queuing model for BES and establish the stability conditions using matrix analytic methods. Based on the stability conditions, we identify the blockchain scalability and server capability as two critical bottlenecks of BES. We further use a tandem queuing model to describe the BES latency of the assembling and service phases. We analytically characterize the properties such as the convexity of service-phase latency with respect to traffic intensity, and highlight the BES pooling effects from traffic offloading and resource sharing. At last, we verify our conclusions through simulations and explore potential pathways for more efficient BES frameworks.

Mobile edge computing (MEC) has emerged as a disruptive paradigm that facilitates effective offloading from clouds and enables processing tasks near users. With the surge of mobile data traffic and escalating demands for responsive wireless services, it becomes imperative to enhance trust, security, and efficiency within MEC environments. Blockchain technology, renowned for its immutability, transparency, and security, has proven to be a compelling solution for securing data, enhancing supervision, and fostering trusted collaborations among heterogeneous MEC stakeholders. Despite these advantages, integrating blockchain with MEC also introduces a substantial efficiency bottleneck. Specifically, the blockchain consensus process can result in the aging of critical MEC system information, causing users to perform suboptimal service decisions, thus risking a degradation in service performance. To analyze this bottleneck, we first explore a blockchain-based MEC model that ensures secure task processing across diverse stakeholders. We employ the practical Byzantine fault tolerance (PBFT) consensus to validate and share service statuses, providing critical on-chain references for users to select their preferred target MEC servers. We then introduce the age-of-information (AoI) as a metric of freshness to characterize the aging of status reports, identifying variable consensus delay as a key factor affecting AoI. We reveal the critical impact of AoI on MEC service performance through analysis, challenging the notion that a lower AoI always leads to better performance. Finally, we validate our analysis and findings through comprehensive simulations.

Blockchain radio access network (B-RAN) offers a promising solution for trustworthy wireless applications by leveraging blockchain and smart contracts. However, the existing literature falls short in addressing the corresponding modeling and theoretical analysis. In this study, we develop analytical models to characterize the wireless access process in B-RAN, which also sheds light on other blockchain-enabled services. We first construct a two-dimensional queuing model and identify both blockchain scalability and RAN capability as two system bottlenecks, and then introduce the matrix analytic method to reduce complexity and improve efficiency. Furthermore, we build tandem queuing models for obtaining tight latency bounds with closed-form expressions. The performance and complexity of the proposed models are evaluated and compared against existing benchmarks to provide a comprehensive perspective. Finally, we present experimental results from a lab-built B-RAN prototype to demonstrate the efficacy of our models.

Blockchain-based decentralized applications (DApps) offer enhanced security and decentralization features; nevertheless, their maintenance demands substantial computational resources and poses challenges for deployment in mobile networks. To address this, a number of studies have explored offloading blockchain mining tasks from mobile users to mobile edge computing (MEC) servers. However, the existing literature overlooks the fact that MEC servers can not only mine for mobile users but also mine for themselves, potentially explaining why MEC mining offloading has not gained broad acceptance within the industry. In this work, we exploit a more practical case and rethink the question of whether MEC servers lease computing power to mobile users by taking into account that MEC servers can mine for themselves. We establish a game model and apply backward induction to analytically characterize Nash equilibria for mining strategies adopted by MEC and mobile users. Our findings suggest that, if MEC can mine for self, MEC would mine for mobile users only under specific conditions where mobile users possess superior information gathering capability (at least better than the MEC server) or the whole blockchain system exhibits significant network value. We further provide a series of simulations to verify our conclusion and illustrate the impact of network parameters on the strategies of both sides.

The advent of blockchain technology in wireless networking has spawned a novel decentralized paradigm that engenders the establishment of multi-party trust, secure and efficient resource sharing, and equitable distribution of benefits. However, blockchain often results in lengthy access latencies and can hardly guarantee the quality of off-chain services. The problems stem from the intrinsic flaws of blockchain in dealing with off-chain services such as wireless access. In this study, we propose a unified framework that guarantees service quality and low establishment latency for blockchain-based systems, and incentivizes clients and service providers (SPs) to cooperate without a trusted third party. We use wireless access service in blockchain radio access network (B-RAN) as a typical example and provide a detailed implementation design. We model the service delivery process in our framework and identify the necessary condition for achieving trusting cooperation. Interestingly, we find the necessary condition is also sufficient for cooperation if the service framework is properly designed. Furthermore, we uncover a trade-off between system robustness and transmission efficiency, which offers insightful guidance for framework design in practice. Finally, we present simulation results to illustrate the effectiveness of our proposed wireless access scheme.

Recently, blockchain radio access network (B-RAN) arises as an innovative paradigm for the sixth-generation (6G) wireless communications to build cooperative trust, aggregate wireless resources, and schedule inter- and intra-network tasks among independent network entities. It establishes an open platform based on blockchain to provide diverse wireless services and applications, such as radio access, Internet of Things (IoT), and mobile-edge computing, via trusted interactions with enhanced security and efficiency. As a distinctive feature, B-RAN enables secure and efficient resource sharing and trading by aggregating, pooling, and coordinating resources from multiple resource hosts and owners across subnetworks. Therefore, an implementable architecture along with various functional modules shall be delicately designed. This work aims to establish a unified architecture with enhanced efficiency, security, compatibility, and flexibility for resource sharing and trading in B-RAN. Specifically, we develop a six-layer architecture that incorporates a number of novel features, such as enhanced blockchain structures, secure interaction methods, efficient service mechanisms, and scalable transaction patterns. We design a number of pluggable functional modules in each layer to support diverse functions, services, and applications of resource sharing and trading. Finally, we implement a practical prototype based on the layered architecture for resource-limited devices. Multiple experiments are presented to verify the performance of the proposed architecture from different aspects.

Visible light communication (VLC) emerges as a promising technology for the explosively growing wireless services and demands. However, the system performance is severely impaired by the inherent nonlinearity of the VLC channel. In existing studies, the Hammerstein and Wiener models are widely used and often assumed for VLC channels due to the simple structure and low complexity. Yet, their effectiveness remains unclear and controversial. This work aims to figure out which one between the Hammerstein and Wiener models is more suitable for characterizing the VLC channel. We first design a single-tone test for qualitative analysis and further conduct an experiment based on multi-level pseudorandom sequences for quantitative evaluation. From the two well-designed experiments, we obtain a consistent conclusion that the Hammerstein model is more proper for describing the VLC nonlinearity and also more effective for post-distortion in VLC systems.

Blockchain radio access network (B-RAN) is a decentralized, trustworthy wireless networking paradigm spurred by distributed ledger technologies (DLTs). In B-RAN, even though the blockchain builds trust in upper layers, the absence of trust between client devices still causes the problem with open access, or the so-called Rogue’s dilemma, and degrades the network performance. Therefore, Hash Access was proposed for B-RAN to address the trust issue between clients and enforce client devices to obey the grant-free access rule. However, the characteristics and performance of Hash Access in B-RAN remain unclear. In this work, we dive deep into the Rogue’s dilemma from a game-theoretic model to emphasize the necessity of Hash Access. We establish an analytical model to comprehensively evaluate the performance of B-RAN using Hash Access regarding transmission success probability, access delay, and network throughput. Based on the analytical model, we further optimize the Hash Access protocol for network throughput and provide useful practical guidelines. Simulation results are presented to validate our proposed model and insights.

The bound of the information transmission rate of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communication (VLC) with finite-alphabet inputs is yet unknown, where the corresponding spectral efficiency (SE) and energy efficiency (EE) stems out as the open research problems. In this paper, we derive the exact achievable rate of the DCO-OFDM system with finite-alphabet inputs for the first time. Furthermore, we investigate SE maximization problems of the DCO-OFDM system subject to both electrical and optical power constraints. By exploiting the relationship between the mutual information and the minimum mean-squared error, we propose a multi-level mercury-water-filling power allocation scheme to achieve the maximum SE. Moreover, the EE maximization problems of the DCO-OFDM system are studied, and the Dinkelbach-type power allocation scheme is developed for the maximum EE. Numerical results verify the effectiveness of the proposed theories and power allocation schemes.

The continually rising demand for wireless services and applications in the era of Internet of things (IoT) and artificial intelligence (AI) presents a significant number of unprecedented challenges to existing network structures. To meet the rapid growth need of mobile data services, blockchain radio access network (B-RAN) has emerged as a decentralized, trustworthy radio access paradigm spurred by blockchain technologies. However, many characteristics of B-RAN remain unclear and hard to characterize. In this study, we develop an analytical framework to model B-RAN and provide some basic fundamental analysis. Starting from block generation, we establish a queuing model based on a time-homogeneous Markov chain. From the queuing model, we evaluate the performance of B-RAN with respect to latency and security considerations. We uncover a more comprehensive picture of the achievable performance of B-RAN by connecting latency and security. At last, we present experimental results via an innovative prototype and validate the proposed model.

In this paper, a deep learning (DL) approach for enhancing the bit-interleaved coded modulation (BICM) receiver is designed to mitigate the clipping distortion in the low-density parity-check (LDPC) coded direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) systems. This work aims to combine the neural network (NN) with the physical layer communications by using a model-driven DL architecture. We first develop a non-iterative NN-aided BICM (NN-BICM) receiver, where the NN is trained with the loss function of cross-entropy to output the modified conditional probability through the softmax activation function, thereby assisting in a log-likelihood ratio (LLR) improvement. Then, we propose two iterative NN-BICM receivers for iterative demapping and decoding. The single iterative design feeds the soft decisions from the LDPC decoder back to the demapper only, while the joint iterative design feeds the soft decisions back to the demapper and NN jointly. By adopting the iteration-wise pre-training strategy, the joint iterative design has been improved by representing the intractable relationship between the conditional probability and the a priori probability with a deeper NN architecture. We further investigate an efficient bit loading algorithm for DCO-OFDM systems employing the NN-BICM receiver. Both NN-BICM receivers and iterative schemes can obtain remarkable performance gains over the existing benchmarks.

Multi-color visible light communication (MC-VLC) is able to achieve high date rate via multiple color channels. To separate different colors, optical filters have to be adopted. In conventional MC-VLC systems, the center wavelength (CWL) and bandwidth (BW) of optical filters is carefully designed for each transmitted color component at normal angles of incidence (AOIs). However, at non-normal AOIs, the CWL of optical filters shifts to shorter wavelength namely the filter CWL shift, leading to serious cross-color interference. In this paper, we propose a novel optical filter bank structure, which consists of a series of narrow-band optical filters, to address the issues caused by the CWL shift. We first establish the corresponding channel model for the VLC links based on optical filter bank, and then design the optimal combining receiver for different AOIs. Simulation results demonstrate that the proposed optical filter bank structure exhibits high and stable performance, and shows strong compatibility for different transmitter configurations.

In visible light communications (VLC), a neural network-aided bit-interleaved coded modulation (NN-BICM) receiver is designed to mitigate the clipping distortion for the LDPC coded DC biased optical orthogonal frequency division multiplexing (DCO-OFDM) systems. Taking the cross-entropy as loss function, the feed forward network is trained by backprop-agation algorithm to output the condition probability through the softmax activation function, thereby assisting in a modified log-likelihood ratio (LLR) improvement. This feed-forward network simplifies the input layer with a single symbol and the corresponding Gaussian variance instead of focusing on the inter-carrier interference over multiple subcarriers. To achieve a further gain, a staked NN-BICM (S-NN-BICM) iterative receiver is proposed by calculating the condition probability with the aid of apriori probability that derived from the extrinsic LLRs in the LDPC decoder at the previous iteration. Both NN-BICM and S-NN-BICM schemes achieve significant performance gains over the existing counterparts, especially more than 0.8 and 1.4 dB in the case of 16-QAM and 511 occupied subcarriers.

Visible light communication (VLC) equipped with multiple light-emitting diodes (LEDs) can provide near ubiquitous indoor coverage for both communication and illumination. This paper introduces the concept of biased beamforming to explore the full potential of multicarrier multi-LED VLC systems. Biased beamforming includes two components to be jointly designed: a direct current (DC) bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact on clipping in multi-LED VLC systems. Next, we consider the joint optimization of beamforming and biasing to maximize data rate. We find the optimal beamformer and analytically characterize its structure. We further optimize the bias on each LED and provide the globally optimal solution in closed form for flat channels, which leads to several critical insights that are helpful for practical systems. We also derive a simplified near-optimal solution for dispersive channels and develop an efficient method for biased beamforming. The performance of the proposed biased beamforming design outperforms existing solutions.

This paper proposes a novel concept of biased beamforming to explore the full potential of multicarrier visible light communication (VLC) systems equipped with multiple light-emitting diodes (LEDs). Biased beamforming requires joint optimization of both: a direct current bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact of clipping in multi-LED multicarrier systems, before carrying out the joint optimization of beamforming and biasing. We derive the optimal biased beamforming in closed form, along with several critical insights that are helpful for practical systems. The proposed biased beamforming strategy outperforms existing solutions, as shown by our simulation results.

This paper considers a visible light system that serves the dual purpose of illumination and communication. We maximize the joint offset and power optimization in direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communications (VLC). We take both the optical and electrical power constraints into account. We analytically characterize the optimal solutions along with new insights on setting the direct current (DC) offset and information-carrying power in DCO-OFDM systems. We also investigate the relationship and impact of the optical and electrical power constraints, along with numerical results.