A Lightweight Auction Framework for Spectrum Allocation with Strong Security Guarantees

The use of a combinatorial auction is believed to be an effective way to distribute spectrum to buyers who have diversified valuations for different spectrum combinations. However, the allocation of spectrum with combinatorial auctions mainly aims at optimizing over certain utility functions, e.g., social welfare, but ignores individual preferences of buyers and sellers, who have incentives to deviate from globally optimal allocation results to improve their own utility. In this paper, we explore the possibility of designing a new stable matching algorithm for combinatorial spectrum allocations. Starkly different from existing efforts on spectrum matching mechanism design, our proposed combinatorial spectrum matching framework not only allows buyers to express preferences towards spectrum combinations (rather than individual channels), but also computes the payment that should be transferred from buyers to sellers. Payment determination, while essential in spectrum exchange, has never been addressed in existing spectrum matching frameworks. We design a novel algorithm to achieve a stable combinatorial spectrum matching and to compute the corresponding payment profiles. We conducted an extensive array of experiments to compare the performance of stable combinatorial spectrum matching with spectrum auctions. It is shown that the combinatorial spectrum matching sacrifices little allocation efficiency in terms of social welfare and spectrum utilization, but achieves a much higher individual buyer utility, which will incentivize buyers to participate and comply with the allocation results.

Efficient spectrum and resource allocation remains a fundamental challenge in fifth generation (5G) wireless networks due to heterogeneous traffic requirements, spectrum scarcity, and dynamic interference conditions. Conventional scheduling and optimization techniques struggle to meet the stringent quality of service (QoS) demands of enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). This paper presents an improved deep reinforcement learning (DRL)-based optimization framework for adaptive spectrum and resource allocation in 5G networks. The proposed model integrates Deep Q-Network (DQN), Double DQN (DDQN), and DDQN with Prioritized Experience Replay (PER) to enhance convergence speed, stability, and scalability. A realistic simulation environment combining ray-traced channel data and packet-level traffic modeling was developed to evaluate system performance. Results demonstrate that the proposed DRL framework achieves up to 7% throughput improvement over proportional fair scheduling, reduces average latency by 10–12%, and lowers URLLC violation rates by approximately 15% under high-interference and heterogeneous traffic scenarios. These findings confirm the feasibility of DRL as a practical and scalable solution for real-time 5G resource management.

Dynamic spectrum redistribution---under which spectrum owners lease out under-utilized spectrum to users for financial gain---is an effective way to improve spectrum utilization. Auction is a natural way to incentivize spectrum owners to share their idle resources. In recent years, a number of strategy-proof auction mechanisms have been proposed to stimulate bidders to truthfully reveal their valuations. However, it has been shown that truthfulness is not a necessary condition for revenue maximization. Furthermore, in most existing spectrum auction mechanisms, bidders may infer the valuations---which are private information---of the other bidders from the auction outcome. In this paper, we propose a Differentially privatE spectrum auction mechanism with Approximate Revenue maximization (DEAR). We theoretically prove that DEAR achieves approximate truthfulness, privacy preservation, and approximate revenue maximization. Our extensive evaluations show that DEAR achieves good performance in terms of both revenue and privacy preservation.

Auctions are believed to be effective methods to solve the problem of wireless spectrum allocation. Existing spectrum auction mechanisms are all centralized and suffer from several critical drawbacks of the centralized systems, which motivates the design of distributed spectrum auction mechanisms. However, extending a centralized spectrum auction to a distributed one broadens the strategy space of agents from one dimension (bid) to three dimensions (bid, communication, and computation), and thus cannot be solved by traditional approaches from mechanism design. In this paper, we propose two distributed spectrum auction mechanisms, namely distributed VCG and FAITH. Distributed VCG implements the celebrated Vickrey-Clarke-Groves mechanism in a distributed fashion to achieve optimal social welfare, at the cost of exponential communication overhead. In contrast, FAITH achieves sub-optimal social welfare with tractable computation and communication overhead. We prove that both of the two proposed mechanisms achieve faithfulness, i.e., the agents' individual utilities are maximized, if they follow the intended strategies. Besides, we extend FAITH to adapt to dynamic scenarios where agents can arrive or depart at any time, without violating the property of faithfulness. We implement distributed VCG and FAITH, and evaluate their performance in various setups. Evaluation results show that distributed VCG results in optimal allocation, while FAITH is more efficient in computation and communication.

Mobile communication plays an important role in the future of communication systems. To meet personalized and intelligent requirements, mobile communication has evolved from single wireless cellular network to heterogeneous mobile communication network, including wireless cellular network, wireless local network, and wireless personal network. In the heterogeneous communication system, to entirely fulfill the spectrum resource complementary advantages of such a heterogeneous wireless network, the spectrum resource trade algorithm has attracted tremendous research efforts. In this paper, the advantages and disadvantages of dynamic spectrum allocation and load balancing are discussed and spectrum auction takes the place of spectrum allocation to maximize the operation revenue. The authors design a joint spectrum auction and load balancing algorithm (SALB) based on heterogeneous network environment to develop a more flexible and efficient spectrum management. The simulation result shows that SALB increases the operation profit significantly.

The Ultra-Reliable Low-Latency Communication (URLLC) is expected to be an important feature of 5G and beyond networks. Supporting URLLC in a resource-efficient manner demands optimal Modulation and Coding Scheme (MCS) selection and spectrum allocation. This paper presents a study on MCS selection and spectrum allocation to support URLLC. The essential idea is to establish an analytical connection between the delay and reliability requirements of URLLC data transmission and the underlying MCS selection and spectrum allocation. In particular, the connection factors in fundamental aspects of wireless data communication include channel quality, coding and modulation, spectrum allocation and data traffic characteristics. With this connection, MCS selection and spectrum allocation can be efficiently performed based on the delay and reliability requirements of URLLC. Theoretical results in the scenario of a 5G New Radio system are presented, where the Signal-to-Noise Ratio (SNR) thresholds for adaptive MCS selection, data-transmission rate and delay, as well as spectrum allocation under different configurations, including data duplication, are discussed. Simulation results are also obtained and compared with the theoretical results, which validate the analysis and its efficiency.

The fifth-generation (5G) wireless communication system promises to revolutionize global connectivity by offering ultra-high-speed data transfer, massive device connectivity, and low latency. However, achieving these objectives demands efficient spectrum allocation and error-correcting mechanisms. The scarcity of radio spectrum and interference management are key challenges, necessitating optimized solutions for spectrum allocation. Additionally, reliable communication over noisy channels requires robust error-correcting codes. Optimized turbo codes, known for their iterative decoding capabilities, have emerged as a viable solution to enhance error resilience and throughput in 5G networks. This research proposes an integrated approach to optimize spectrum allocation and turbo code performance. The spectrum allocation employs a dynamic multi-objective optimization model based on machine learning algorithms, prioritizing fairness, quality of service (QoS), and interference minimization. Simultaneously, an improved turbo coding algorithm utilizing adaptive puncturing and interleaving strategies enhances data integrity. Simulations conducted in a heterogeneous 5G environment demonstrate significant performance improvements. The optimized turbo codes achieve a Bit Error Rate (BER) of 10-5 at a Signal-to-Noise Ratio (SNR) of 2.5 dB, outperforming conventional turbo codes by 40%. The proposed spectrum allocation strategy enhances spectral efficiency by 25%, ensuring equitable resource distribution and improved QoS. This integrated framework highlights the potential for scalable and efficient 5G systems, addressing the dual challenges of spectrum scarcity and error correction.

In recent years, a large number of spectrum mechanisms have been proposed, but these mechanisms ignore the security issues that arise during the design of the mechanism. In this paper, two secure models for sealed-bid spectrum auction are given based on Wang’s generic spectrum auction mechanism. One is the basic model and another improved model based on the basic model is proposed, which maximizes Social welfare while it is a Privacy-preserving Spectrum auction mechanism with public Verification namely SPSV. The SPSV scheme achieves the properties of maximizing the social welfare but also, by using the double paillier cryptosystem, it is privacy-preserving for bidders’ bids without revealing any sensitive information to auctioneer or agent during the entire spectrum auction. Oblivious transfer is applied to ensure the anonymity of bidders. Furthermore, the use of inequality comparison proof also provides the public verification of winner group to verify the comparison relationship between winner groups and losing groups. At last, the performance analysis are given.

In this paper, we investigate the problem of recall mechanism in a dynamic spectrum auction system. The licensed spectrum holders (SHs) can auction their idle channels to the secondary wireless service providers (SWSPs) for economic incentive. However, the primary user (PU) of an SH has higher priority than the SWSPs for the spectrum access. To deal with a sudden increase in PUs' spectrum demands after auction and guarantee their quality of services (QoS), we propose a Local recall-based dYnamic double spectRum auctIon meChanism (LYRIC) for SHs' spectrum auction operation. Different from the most existing approaches about spectrum recall-based spectrum auction, the proposed spectrum auction method for heterogeneous spectrum transaction takes account of the joint effect of heterogeneous demand, spectrum spatial reusability, and the economic efficiency. Simulation results show that the proposed LYRIC algorithm can improve the auction process of SHs, the channels utilization efficiency and the average buyers' satisfactory level with guarantees on the QoS of PUs.

AI-based resource allocation techniques in D2D communication: Open issues and future directions

Dynamic spectrum allocation has proven promising for mitigating the spectrum scarcity problem. In this model, primary users lease chunks of under-utilized spectrum to secondary users, on a short-term basis. Primary users may need financial motivations to share spectrum, since they assume costs in obtaining spectrum licenses. Auctions are a natural revenue generating mechanism to apply. Recent design on spectrum auctions make the strong assumption that the primary user knows the probability distribution of user valuations. We study revenue-maximizing spectrum auctions in the more realistic prior-free setting, when information on user valuations is unavailable. A two-phase auction framework is constructed. In phase one, we design a strategyproof mechanism that computes a subset of users with an interference-free spectrum allocation, such that the potential revenue in the second phase is maximized. A tailored payment scheme ensures truthful bidding at this stage. The selected users then participate in phase two, where we design a randomized competitive auction and prove its strategyproofness through the argument of bid independence. Employing probabilistic techniques, we prove that our auction generates a revenue that is at least 1/3 of the optimal revenue, improving the best known ratio of 1/4 proven for similar settings.

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.

In this paper, we present a blockchain-based mobile edge computing (B-MEC) framework for adaptive resource allocation and computation offloading in future wireless networks, where the blockchain works as an overlaid system to provide management and control functions. In this framework, how to reach a consensus between the nodes while simultaneously guaranteeing the performance of both MEC and blockchain systems is a major challenge. Meanwhile, resource allocation, block size, and the number of consecutive blocks produced by each producer are critical to the performance of B-MEC. Therefore, an adaptive resource allocation and block generation scheme is proposed. To improve the throughput of the overlaid blockchain system and the quality of services (QoS) of the users in the underlaid MEC system, spectrum allocation, size of the blocks, and number of producing blocks for each producer are formulated as a joint optimization problem, where the time-varying wireless links and computation capacity of the MEC servers are considered. Since this problem is intractable using traditional methods, we resort to the deep reinforcement learning approach. Simulation results show the effectiveness of the proposed approach by comparing with other baseline methods.

Overestimating Wireless Demand: Policy and Investment Implications of Upward Bias in Mobile Data Forecasts

Spectrum has now become a scarce resource due to the continuous development of wireless communication technology. Cognitive radio technology is considered to be a new method to solve the shortage of spectrum resources. The spectrum allocation model of cognitive radio can effectively avoid the waste of spectrum resources. A novel binary version of slime mould algorithm is proposed for the spectrum allocation model to solve the spectrum allocation scheme. In addition, adding unselected factors strategy can make the approach find a better solution. Compared with other algorithms, the novel binary slime mould algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation. The resulting spectrum allocation scheme can achieve efficient use of network resources.