Chirp spread spectrum (CSS) modulation-based long-range (LoRa) communication has attracted widespread attention owing to its long-range coverage and low-power consumption. However, the inherent trade-off between communication range and data rate in CSS modulation creates a throughput bottleneck, significantly limiting LoRa’s applicability in practical scenarios. To enhance the uplink data rate and reliability in LoRa communications, this paper proposes novel schemes that integrate reconfigurable intelligent surfaces (RIS) and index modulation (IM). Specifically, we develop comprehensive transmission and detection mechanisms for both single-node and multi-node scenarios. To improve the performance of single-node transmission, we introduce an IM scheme based on receiving antenna selection combined with RIS-assisted beamforming, and design a low-complexity sequential detector that exploits unique signal characteristics. To address the multi-node scenario with the co-spreading-factor (co-SF) interference, we propose a power control scheme at transmitters inspired by non-orthogonal access principles, and develop an asymptotically optimal RIS beamforming strategy. We further design an efficient demodulation scheme that leverages statistical features of the received signals across selected antennas. Extensive simulation results demonstrate that our proposed schemes effectively improve both bit error rate and data rate performance compared to conventional LoRa systems and related prior art.

Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel federated learning framework with a dual-level game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation. At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles. At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server. Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility.

The advent of ubiquitous computing devices in the Internet of Things (IoT) has resulted in an explosion of data. Traditional centralized machine learning models face challenges including limited bandwidth in wireless environments and privacy concerns due to their data aggregation approach. Federated learning addresses these challenges via decentralizing model training across numerous devices, leveraging model updates to enhance privacy and reduce communication overhead. To improve its cost efficiency, current research focuses on minimizing either time or energy costs but rarely both, and does not jointly optimize the parameters of device and training scheduling in the presence of system and data heterogeneity inherent in IoT networks. In our paper, we first introduce a multi-group transmission scheme and propose a comprehensive device scheduling framework, Group Scheduling on Orthogonal Frequency-Division Multiple Access (GS-OFDMA), to address time bottlenecks. Then we formulate a joint optimization problem for device and training scheduling that minimizes the total cost of training while ensuring model convergence. To tackle the resulting mixed integer nonlinear programming problem, we develop an iterative algorithm. Experimental results show that our approach significantly reduces the total cost by at least 35% across various real-world datasets and data distributions in comparison with random participant selection. The proposed GS-OFDMA protocol also exhibits higher time efficiency over other device scheduling schemes.

Federated learning enables decentralized model training across numerous devices without data centralization, leveraging model updates to enhance privacy and reduce communication overhead. Despite its advantages, federated learning systems must be optimized for cost efficiency, considering the limited computational capabilities and battery life of edge devices. Current research often focuses on minimizing either time or energy costs but rarely both, and does not jointly optimize the parameters of device and training scheduling in the presence of system and data heterogeneity. In our paper, we formulate a novel joint optimization problem for device and training scheduling that minimizes the total cost of federated learning while ensuring model convergence. We propose a new device scheduling scheme, Group Scheduling on Orthogonal Frequency-Division Multiple Access (GS-OFDMA), to improve time efficiency and develop an iterative algorithm to tackle the resulting mixed integer nonlinear programming problem. Our experimental results show that our approach significantly reduces the total cost by at least 35 % across different real-world datasets and data distributions in comparison with random participant selection.

Federated learning is a distributed machine learning paradigm that allows multiple edge devices to collaboratively train a shared model without exchanging raw data. However, the training efficiency of federated learning is highly dependent on client selection. Moreover, due to the varying wireless communication environments and various computation latencies among the clients, selecting clients randomly or uniformly may not be optimal for balancing the data diversity and training efficiency. In this article, we formulate a new latency-minimization problem that simultaneously optimizes client selection and training procedures in federated learning, which takes into account the data and latency heterogeneity among the clients. Given the nonconvexity of the problem, we derive a new convergence upper bound for federated learning with probabilistic client selection. To solve the mixed integer nonlinear programming problem, we introduce a hybrid solution that integrates grid search techniques with the polyhedral active set algorithm. Numerical analyses and experiments on real-world data demonstrate that our scheme outperforms the existing ones in terms of overall training latency and achieves up to three times acceleration over random client selection, especially in scenarios with highly heterogeneous data and latencies among the clients.

The unmanned aerial vehicle (UAV) with intelligent reflecting surface (IRS) mounted, namely, aerial IRS, has the potential in improving the information freshness (IF) and transmission data rate for wireless networks, where age of information (AoI) is generally utilized to characterize the IF. In this article, we optimize both the AoI and transmission data rate in aerial IRS-assisted Internet of Things (IoT) networks through the joint transmission scheduling, UAV location, and IRS phase shift matrix design. We formulate a multiobjective optimization problem, which simultaneously minimizes the system average AoI and maximizes the overall transmission data rate. The optimal solutions for the two objectives in the formulated problem are not always consistent with each other. Besides, the optimization problem with either objective is nonconvex and difficult to tackle directly. An effective three-step scheme is developed in this article to solve the formulated problem. To be more specific, firstly the UAV locations are optimized through a$Q$-learning-based scheme to maximize the overall data rate while guaranteeing the signal-to-noise ratio (SNR) constraint of each IoT device; then the IRS phase shift matrices are determined through a low-complexity Tabu-search-based scheme to further improve the overall data rate given the SNR constraint of each device; finally, the transmission scheduling is performed to optimize the system AoI based on a deep$Q$-network algorithm. Simulation evaluation demonstrates that the proposed scheme outperforms existing ones.

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.

Heterogeneous networks(HetNets) have been widely used in the development of 5G because a large number ofsmall base stations(SBSs) can be deployed in hot spots to alleviate uneven traffic distribution. However, the performance of HetNet with wireless backhaul is limited by its backhaul capacity and the severe wireless interference. To solve this problem, we introducereconfigurable intelligent surface(RIS) into the wireless backhaul HetNets and employ reversedtime division duplex(TDD) in the time domain and dynamicsoft frequency reuse(SFR) in the frequency domain. In the proposed RIS-aided dual-layer HetNets system, we formulate a joint optimization problem of bandwidth allocation, RIS passive beamforming, and power allocation to maximize the overall data rate. Two schemes are considered to adapt to different situations, i.e.,unified dynamic SFR(U-SFR) andindividual dynamic SFR(I-SFR). We solve the different sub-problems in U-SFR and I-SFR modes with the alternate optimization method, and obtain closed-form expressions for bandwidth allocation and power allocation. The convergence, feasibility, and complexity of the proposed schemes are also analyzed. In numerical results, the overall data rate is shown to be significantly increased with the help of RIS.

The covert communication technology has emerged as a novel method for network authentication, copyright protection, and providing the evidence of cybercrimes. However, how to design the covert communication scheme in the physical layer of wireless networks and how to optimize the data rate for the covert communication channels are very challenging. In this article, we propose a wireless covert communication system (CCS), where the transmit antennas are selected and coded to generate a covert codebook. According to the covert codebook, the antennas can be dynamically combined to transmit different covert messages. In addition, we adopt a modulation scheme, named covert quadrature amplitude modulation (QAM), to modulate the covert messages, where the precoding method is designed to deviate the constellations for covert information bits from those for the public information bits. Furthermore, we derive the closed-form expressions of capacity and bit error ratio (BER) for the proposed CCS. To maximize the covert data rate of the CCS, we formulate an optimization problem of the covert data rate and solve the problem to find the optimal precoding matrix. To reduce the covert information leakage, artificial noise is introduced to the system to jam the communication between the transmitting and watching nodes. We design a beamforming scheme to maximize the secure rate for the CCS, where the leakage of covert information can be minimized while the covert communication is not influenced. Simulation results show that the proposed CCS can significantly improve the covert data rate and reduce the covert BER in comparison with the traditional CCSs.

In covert communication systems, covert messages can be transmitted without being noticed by the monitors or adversaries. Therefore, the covert communication technology has emerged as a novel method for network authentication, copyright protection, and the evidence of cybercrimes. However, how to design the covert communication in the physical layer of wireless networks and how to improve the channel capacity for the covert communication systems are very challenging. In this paper, we propose a wireless covert communication system, where data streams from the antennas of the transmitter are coded according to a code book to transmit covert and public messages. We adopt a modulation scheme, named covert quadrature amplitude modulation (QAM), to modulate the messages, where the constellation of covert information bits deviates from its normal coordinates. Moreover, the covert receiver can detect the covert information bits according to the constellation departure. Simulation results show that proposed covert communication system can significantly improve the covert data rate and reduce the covert bit error rate, in comparison with the traditional covert communication systems.

In spectrum sharing systems based on spectrum trading, user locations are vital for the efficiency of dynamic channel reuse. However, both primary users (PUs) and secondary users (SUs) undertake the risk of location information leakage: a malicious PU may illegally collect SUs' location information to manipulate market decisions; a malicious SU would threat a PU's operational privacy by inferring the PU's location through seemingly inoffensive queries. To protect both PUs' and SUs' location information in spectrum trading, a bilateral privacy preservation framework is introduced in this paper. A game-theoretic approach based on the Stackelberg model is proposed to achieve the tradeoff between the privacy-preserving level and user utility. With the proposed approach, both PUs and SUs can maximize their utilities while maintaining their location privacy to desired levels. Simulation results demonstrate that the proposed approach can effectively enhance user utility gain and strengthen user privacy guarantee by flexibly adjusting their privacy levels in practice.

In this paper, we consider a novel Internet of Things (IoT) system in smart city called unmanned aerial vehicle‐ (UAV‐) assisted cognitive backscatter network, where a UAV is employed as both a relay and a radio frequency source to help the data transmission between ground IoT backscatter devices (BDs) and a remote data center (DC). However, since the IoT applications are usually not assigned dedicated spectrum resource in smart cities, these data transmissions from BDs to the DC should share the licensed spectrum of cellular users (CUs). Therefore, we aim to maximize the minimum uplink throughput among all BDs while avoiding severe interference to CUs via joint spectrum management and UAV trajectory design. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into two blocks. For the spectrum management problem, we first prove its convexity with the transmit power and time scheduling and then propose a two‐step method to solve the two variables sequentially. For the UAV trajectory design problem, we resort to the fractional programming method to handle it. Simulation results demonstrate that the proposed algorithm can significantly increase the average max‐min rate of the BDs while guaranteeing the acceptable interference to CUs with a fast convergence speed.

In this paper, the problem of privacy-utility tradeoff in a database-driven spectrum sharing system is considered, where both primary users (PUs) and secondary users (SUs) may suffer from location privacy leakage. To protect the location information of both parties, a bilateral privacy preservation mechanism is introduced, in which the privacy-preserving levels are quantified. To tackle the dilemma that a higher privacy level leads to less available spectrum to share and thus reduces the profits of both parties, a game-theoretic approach based on the Stackelberg model is proposed to achieve the tradeoff between the privacy-preserving level and user utility. With the proposed approach, both PUs and SUs can maximize their utilities by adjusting their location privacy to desired levels. Simulation results demonstrate that the proposed approach can effectively increase the utilities for both PUs and SUs in comparison with the privacy preservation mechanism with a fixed privacy level.

To enable heterogeneous channel valuations in spatial spectrum reuse, user characteristics involving the supply and demand relationship need to be considered. In this paper, we design a channel transaction mechanism for non-symmetric networks and maximize the social welfare in consideration of multi-level channel valuations of the secondary users (SUs). Specifically, we group the SUs into allowable user crowds (AUCs) through a modified Bron-Kerbosch algorithm. We introduce a Vickrey-Clarke-Groves (VCG) auction, in which the participants are limited to the AUCs. To facilitate the bid formation, we transform the constrained VCG auction to a step-by-step decision process. In each step, the truthful bidding of an AUC is to reveal the accumulated channel valuation of the coalition. Meanwhile, the SUs in a coalition play a coalitional game with transferable utilities. We use the Shapley value to realize fair payoff distribution among the SUs in a coalition. Furthermore, we approach the optimal channel allocation via a greedy algorithm and batch allocation. In our simulation, we compare the low-complexity algorithms and demonstrate the efficiency of the channel transaction mechanism.

Network slicing is treated as a key technology of the rapidly developing 5G system. Nevertheless, the environment of the users is extremely complex, leading to a great challenge for allocating the slices in an optimal manner. In this paper, we propose a dynamic slice allocation scheme with two- tier paradigm in consideration of the quality of experience (QoE). In the first tier, called local tier, we employ linear programming aided by a penalty function to allocate the radio resources in the slices to services for user equipments aiming at the best QoE. In the second tier, called edge tier, we design a deep reinforcement learning algorithm to dynamically allocate the computing resources to the edge networks, to achieve the best QoE and highest resource utilization rate. Simulation results demonstrate that the proposed paradigm can achieve better throughput and QoE in comparison with the traditional network slicing paradigms.

The hybrid wireless and power line communication (HWPLC) networks address the problem that mobile wireless sensors and power line communication (PLC) sensors cannot communicate with each other within an Internet of Things (IoT) network. In this paper, we design a relay equipped with a dual wireless and PLC interface, which connects both the PLC and wireless sensors into an IoT network. Furthermore, the dual-interface relay forwards messages by adaptively selecting a interface according to the channel state. A general mathematical probability model of the dual-interface relaying system is presented. The probability density function of the output signal-to-noise ratio (SNR) is developed, which is based on explicit closed-form expressions derived from the statistics character of the PLC and wireless channel. Furthermore, the average capacity, bit-error rate (BER) expressions, and the outage probability formulas are derived. Numerical results show that the HPLWC relaying system with the dual-interface can significantly improve the performance of capacity, BER, and outage probability by adaptively selecting the interface with the optimal received SNR.

The transportation network company (TNC) services efficiently pair the passengers with the vehicles/drivers through mobile applications, such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings by using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning-based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment, such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network-based route scheduling algorithm for vacant TNC vehicles based on distributed framework, which makes the server closer to the terminal users and accelerates the training speed. Furthermore, we apply the geo-indistinguishability scheme based on differential privacy to preserve the sensitive location information uploaded by the passengers. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

In heterogeneous vehicular networks, non-orthogonal multiple access (NOMA) with effective resource allocation improves spectrum efficiency by allowing multiple users to share the same channel. However, channel estimation errors caused by high mobility in vehicular networks affect system robustness and link reliability. As a result, resource allocation in high-mobility scenarios is a challenging issue. In this paper, robust resource allocation is studied to improve both the throughput performance and reliability of NOMA-based heterogeneous vehicular networks. A cascaded Hungarian channel assignment algorithm is proposed to simplify the formulated resource allocation problem with reliability requirements into a robust power allocation problem with chance constraints. With the approximation of non-central Chi-square distribution, the chance constraints are transformed into deterministic constraints. Furthermore, the optimal power allocation for the transformed problem is obtained in consideration of the requirements in NOMA. Simulation results illustrate the effectiv- eness of the proposed robust resource allocation scheme and its improvement over existing schemes.

Spatial spectrum reuse enables better utilization of limited spectral resources to achieve higher system throughput. However, improving the system throughput or spectrum efficiency does not necessarily translate to the satisfaction of more secondary users (SUs) according to their demands. To improve user satisfaction, user characteristics involving the supply and demand relationship need to be considered and thus enable heterogeneous channel valuations in spatial spectrum reuse. In this paper, we design a channel transaction mechanism for non-symmetric networks and maximize user satisfaction in consideration of multi-level flexible channel valuations of the SUs. Specifically, we introduce a Vickrey-Clarke-Groves (VCG) auction, in which the participants are limited to the allowable user crowds. To facilitate the bid formation, we transform the constrained VCG auction to a step-by-step decision process. Meanwhile, the SUs in a coalition play a coalitional game with transferable utilities. We use the Shapley value to realize fair payoff distribution among the SUs in a coalition. Furthermore, we approach the optimal channel allocation via finding the longest path in a directed acyclic graph, a greedy algorithm, and batch allocation. In our simulation, we compare the low-complexity algorithms and demonstrate the efficiency of the channel transaction mechanism.

Cooperative relaying is highly affected by the way that the source and relays are incentivized. However, the existing studies have not paid enough attention to the impacts of negotiation power and information availability of the players on the network performance. In this paper, two incentive mechanisms for cooperative relaying are first proposed to explore the influence of negotiation power, wherein the source holds either strong or weak negotiation power. In these two cases, the source posts take-it-or-leave-it offers for the relays and has to negotiate possible deals with the relays, respectively. The relay selection rules and the optimal amounts of relaying service and rewards are derived for each mechanism, respectively. Another two incentive mechanisms are also proposed to explore the influence of information availability, wherein the source has either weakly or strongly incomplete information about the relays. In these two cases, the source acquires the number of relays belonging to each type and the probability of each relay belonging to a certain type, respectively. The relay selection rules and the optimal contract offers are derived for the two mechanisms, respectively. The numerical results are provided to verify the theoretical analyses and demonstrate the effectiveness of the proposed mechanisms.

In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)- enabled wireless caching network, where each user randomly caches popular contents to its memory and shares the contents with nearby users through D2D links. In order to enhance the throughput of spectrum-sharing D2D links, which may be severely limited by the interference among D2D links, we enable the cooperation among some of the D2D links to eliminate the interference among them. We formulate a joint link scheduling and power allocation problem to maximize the overall throughput of cooperative D2D links (CDLs) and non- cooperative D2D links (NDLs), which is NP-hard. To solve the problem, we decompose it into two sub- problems, which maximize the sum rates of the CDLs and the NDLs, respectively. For CDL optimization, we propose a semi-orthogonal-based algorithm for joint user scheduling and power allocation. For NDL optimization, we propose a novel low-complexity algorithm to perform link scheduling and develop a Difference of Convex functions (D.C.) programming method to solve the non-convex power allocation problem. Simulation results show that cooperative transmission can significantly improve both the number of served users and the overall system throughput.

The advance in mobile communications has escalated the use of transportation network company (TNC) services by residents. The TNC services efficiently pair the passengers with the vehicles/drivers through mobile applications such as Uber, Lyft, Didi, etc. TNC services definitely facilitate the traveling of passengers, while it is equally important to effectively and intelligently schedule the routes of cruising TNC vehicles to improve TNC drivers' revenues. From the TNC drivers' side, the most critical question to address is how to reduce the cruising time, and improve the efficiency/earnings of using their own vehicles to provide TNC services. In this paper, we propose a deep reinforcement learning based TNC route scheduling approach, which allows the TNC service center to learn about the dynamic TNC service environment and schedule the routes for the vacant TNC vehicles. In particular, we jointly consider multiple factors in the complex TNC environment such as locations of the TNC vehicles, different time periods during the day, the competition among TNC vehicles, etc., and develop a deep Q-network (DQN) based route scheduling algorithm for vacant TNC vehicles. We evaluate the proposed algorithm's performance via simulations using open data sets from Didi Chuxing. Through extensive simulations, we show that the proposed scheme is effective in reducing the cruising time of vacant TNC vehicles and improving the earnings of TNC drivers.

Cooperative relaying in wireless networks is strongly affected by the way that the source and relays are incentivized. However, existing studies have not paid enough attention to the influence of negotiation power of the involved parties. In this paper, two incentive mechanisms for cooperative relaying are proposed, wherein the source possesses different degrees of negotiation power. One mechanism is for the source with strong negotiation power posting a series of take-it-or- leave-it contract offers for the relays while the relays are not entitled to negotiate the counteroffers, and the other mechanism is for the source with weak negotiation power while the relays confer substantial negotiation power, i.e., are capable of doing business with the source by crafting more profitable deals. The relay selection rules, the optimal amounts of relaying service, and the optimal rewards for the relays, are derived for the proposed mechanisms, respectively. A distributed algorithm is further proposed to iteratively obtain the optimal solution for the second mechanism. A case study is also presented to show the influence of negotiation power on the behaviors of the participants and the efficiency and distribution of profits. Numerical results are provided to verify the theoretical analyses of the proposed mechanisms.

In this paper, we investigate mobile association and power allocation in device-to-device (D2D)- enabled heterogeneous networks with non-orthogonal multiple access (NOMA) protocol. We formulate two optimization problems to maximize the minimum rate and the sum rate of the network, respectively. Each problem includes power allocation, access point selection and transmission mode switching. To solve the problems, we develop a two-step method, which can always reach the closed-form solution. Simulation results show that the proposed method can significantly improve the overall throughput of the system, compared with the traditional solutions without D2D communications. In addition, we also investigate the tradeoff between overall throughput and fairness.

In this paper, a network consisting of a source- destination pair and multiple relays in the presence of a smart jammer who can launch multi- channel jamming attacks is considered, where the direct link between the source and destination does not exist. A game-theoretic framework is proposed to analyze the conflict between the jammer and the legitimate nodes, i.e., the source and the relays, and the cooperation among the legitimate nodes. Specifically, a two-level Stackelberg game is formulated, where the jammer as a leader combats against the legitimate nodes by allocating the jamming powers at the upper level, and the relays as leaders cooperate with the source by selling forwarding powers at the lower level after observing the strategy of the jammer. The Stackelberg equilibrium of the proposed game is derived and analyzed. An algorithm is further designed to obtain the optimal jamming power allocation. Numerical results are provided to verify the theoretical analysis and show the effectiveness of the proposed algorithm.

In this paper, the energy efficiency in a cellular network with device-to-device communications is studied. A mixed-integer max-min optimization problem is formulated with both mode selection and resource allocation. Since the optimal solution requires an exhaustive search, a low-complexity decomposition (LCD) method is derived. A fairness-aware mode selection scheme, a subcarrier assignment scheme, and a mode switching scheme are introduced and analyzed. Moreover, a novel power allocation scheme is proposed, which exploits the property of the fractional structure of the energy efficiency optimization problem over multiple subcarriers. The proposed LCD method is scalable and suitable for a large number of users and subcarriers. Simulation results demonstrate that our proposed LCD method achieves satisfactory energy efficiency performance and promotes the fairness among individual users.

In this paper, secure transmission in a heterogeneous network in the presence of multiple eavesdroppers is studied. A game-theoretic framework is proposed to enhance the security of the macro base station (MBS), while guaranteeing user satisfaction of the small base stations (SBSs) by exploiting the cooperation and competition among the entire network. Specifically, a two-level Stackelberg game is formulated, where the MBS employs a set of competing jamming SBSs to jam the eavesdroppers at the top level, and each employed jamming SBS may require offloading service from multiple competing helping SBSs in its cluster at the bottom level if needed. Two levels of user satisfaction are investigated at the bottom level, respectively. One is fixed with the priority given to user satisfaction over the profit and the other is flexible with a balance between user satisfaction and the profit. The Stackelberg equilibrium of the proposed game is derived and analyzed from the economics viewpoint. An iterative algorithm is also proposed to obtain the optimal solutions. Numerical results are provided to verify the theoretical analysis and show the effectiveness of the proposed algorithm.

In this paper, secure transmission in a two-tier heterogeneous network, consisting of a macrocell and a set of small cells, is considered, where an eavesdropper attempts to wiretap legitimate macrocell users. A game-theoretic framework is proposed to enhance the security of the macrocell while guaranteeing user satisfaction of the small cells. Specifically, a two-level Stackelberg game is formulated, where the macro base station as a follower employs multiple small base stations (SBSs) to jam the eavesdropper at the top level, and each employed SBS as a leader requires offloading service from multiple helping SBSs in its cluster at the bottom level if needed. Two types of objectives are investigated at the bottom level, one with the priority given to user satisfaction over the leader's profit, and the other to balance between user satisfaction and the leader's profit, respectively. The Stackelberg equilibrium of the proposed game is also derived. Numerical results verify the analysis and show that employing the existing SBSs is a promising approach to enhancing security while satisfying the user demands of the small cells.

Spatial spectrum reuse significantly enhances spectrum utilization but requires delicate design to avoid co-channel interference. Instead of focusing solely on spectrum efficiency, we consider maximizing social welfare via on-demand channel allocation in this paper. We design a spectrum reuse mechanism for non-symmetric networks, in which the optimal channel allocation that maximizes social welfare is the result of an appropriate bidding method of secondary users (SUs) in the constrained Vickrey-Clarke-Groves (VCG) auction. To simplify the constrained VCG auction, we group the SUs into interference-free maximal independent groups (MIGs) using a modified Bron-Kerbosch algorithm. We introduce the VCG auction for MIGs, in which truthful bidding is the optimal strategy for the MIGs. We build a decision process such that the MIGs as representatives of the SUs can update their channel evaluations in each step and submit truthful bids. Furthermore, we approximate and simplify the optimal channel allocation with a greedy algorithm and Dijkstra's algorithm. In our simulation, we compare the proposed methods and demonstrate that our on- demand channel allocation increases social welfare.

With device-to-device (D2D) communications, a user terminal (UT) can be used as a relay node to support multi-hop transmission so that cell-edge or deeply faded users can obtain a better connective experience. In this paper, we investigate energy-efficient transmission for D2D-enabled cooperative networks. We aim to maximize the energy efficiency (EE) of the uplink transmission while guaranteeing the minimum data rate requirement via joint D2D relay node (DRN) placement and power allocation. To solve the problem, we first decompose it into four sub-problems depending on the minimum data rate requirement and the power limits at the UT and DRN and then derive a closed-form solution for each problem. Numerical results demonstrate that the maximum EE can be always achieved with the proposed DRN placement and power allocation.

In heterogeneous vehicular networks, power control with non-orthogonal multiple access (NOMA) is effective in improving spectrum efficiency by allowing multiple users to share the same channel. However, the channel estimation error caused by high mobility in vehicular communications needs to be considered for efficient power allocation and link reliability. In this paper, a robust power allocation problem with chance constraints is studied to improve at the same time the throughput performance and reliability of NOMA heterogeneous vehicular communications. Through the approximation of non-central Chi-square distribution, the chance constraints are transformed into deterministic constraints. Moreover, the optimal power allocation is obtained with the consideration of NOMA requirements. Simulation results illustrate the effectiveness of the proposed power allocation and its improvement over existing schemes.

In this paper, we consider a multiple dimension resources allocation problem, including user grouping in the spatial domain and resource blocks (RBs) allocation in the time-frequency domain, in a frequency-division-duplexing (FDD) massive MIMO system. We formulate an optimization problem on joint user grouping and resource allocation to maximize the system capacity. Then, we propose two user grouping methods with load balance to fully use the resources in each user group and a corresponding greedy resource allocation algorithm to verify the effectiveness of the user grouping methods. The simulation results demonstrate that the proposed schemes can obtain better performance over the existing ones and the optimal number of scheduled users can be obtained.

In this paper, we study resource allocation for full-duplex communications in an orthogonal frequency division multiple access femtocell network. We aim to maximize the throughput of the femtocell while avoiding severe inter-tier interference to the macrocell via joint sub-channel assignment and power allocation. To be more practical, we take channel estimation error into account and use the robust optimization theory to model the uncertainty in interference channels. By using the Lagrangian dual method, we decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the non-convex primal problem into a tractable form through sequential convex approximations and then utilize the sub-gradient method to solve the dual problem. Simulation results show the effectiveness of the proposed algorithm and demonstrate the impact of channel uncertainty on the system performance.

In this paper, we study resource allocation for secondary users (SUs) in underlay full-duplex cognitive networks, where the channel state information of the links between SUs and primary users (PUs) is uncertain. To protect the transmission of the PUs from interference generated by the SUs, we utilize robust optimization theory to characterize the channel uncertainty and formulate a resource allocation problem by jointly optimizing sub-channel assignment, user pairing, and power allocation. By using the dual method, we decompose the original resource allocation problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results demonstrate the effectiveness of our proposed algorithm.

In this paper, we propose a new estimation method to obtain the cross-channel gain, which avoids the severe interference to the primary receiver (PR) in existing relay-assisted estimation methods. In our method, we let the cognitive transmitter add a time delay when it conducts the full-duplex amplify-and-forward relaying. This forces the time-difference-of-arrival (TDOA) between the direct and relay signals to be large enough rather than randomly large or small. Then we develop our estimation method only in the large TDOA case and precisely control the interference to the PR. Simulation results indicate that the proposed method can significantly reduce the interference to the PR.

In this paper, we study resource allocation in full-duplex OFDMA networks. We explore the joint optimization of subcarrier assignment, uplink-downlink user pairing, and power allocation to maximize the overall throughput with consideration of self-interference and inter-node interference. By using the dual method, we can decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results show that the proposed algorithm can always achieve better throughput in comparison with the existing algorithms.

For a spectrum sharing system using economic approaches, conventional models without geographic considerations are oversimplified. In this paper, we develop a model where geographic information, including licensed areas of primary users (PUs) and locations of secondary users (SUs), plays an important role in the spectrum sharing system. We consider a multi-price policy and the pricing power of non-cooperative PUs in multiple geographic areas. Meanwhile, the value assessment of a channel is price-related and the demand from the SUs is price-elastic. To maximize the payoffs of the PUs, we propose a unique quota transaction process. By applying an evolutionary procedure defined as replicator dynamics, we prove the existence and uniqueness of the evolutionary stable strategy quota vector of each PU, which leads to the optimal payoff for each PU selling channels without reserve. In the scenario of selling channels with reserve, we predict the channel prices for the PUs leading to the optimal supplies of the PUs and hence the optimal payoffs. Furthermore, we introduce a grouping mechanism to simplify the process. In our simulation, the effectiveness of the learning processes designed for the two scenarios is verified and our spectrum sharing scheme is shown efficient in utilizing the frequency resources.

In cognitive radio networks, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is critical for spectrum sharing and obtaining the cross-channel gain is very difficult. Even though proactive estimation allows the CT to autonomously estimate the cross-channel gain, it may cause severe interference to the PR. This raises a new issue for spectrum sensing, called spectrum sensing interference. In this paper, we deal with the sensing interference and propose a relay-assisted method to conduct the proactive estimation, which obtains the cross-channel gain with much less interference to the PR. In our method, we let the CT act as a full-duplex amplify-and-forward relay to probe the close-loop power control between primary transceivers. By measuring the power adjustment of the primary signal, the CT estimates the cross-channel gain. Simulation results indicate that our method can reduce the sensing interference to an extremely low level.

With device-to-device (D2D) communications, a user terminal can be used as a relay node to support multi-hop transmission, so that cell-edge or deeply faded users can obtain a better connective experience. In this paper, we investigate energy-efficient mobile association in D2D-enabled heterogeneous networks. We consider joint access point selection, mode switching, D2D relay node (DRN) selection, and power control to maximize the energy efficiency (EE) of uplink transmission while guaranteeing the quality-of-service requirement of users. The optimization problem can be decomposed into three subproblems: access point selection, power control, and joint mode switching and DRN selection. The joint mode switching and DRN selection problem is a 0-1 integer optimization problem, whose optimal solution can be found by the brute-force searching method that is complexity-prohibitive when the number of DRNs is large. To reduce the complexity involved in computation, channel estimation, and feedback, we develop a distance-based mobile association (DMA) algorithm, which only operates based on the location information of users and DRNs. Simulation results demonstrate that the proposed DMA algorithm can achieve a good tradeoff between the EE and the complexity.

In this paper, the energy-efficient design of wireless relay networks with radio frequency (RF) energy harvesting capability is studied. A decode-and-forward relay is used to forward information and harvest energy simultaneously from the same signal with a power splitting architecture. Given the power and quality-of-service constraints, a non- convex energy- efficient optimization problem is formulated, in which the transmit power and power splitting ratio are coupled together. With our proposed algorithm, the problem is decoupled and solved. The question of when RF energy harvesting relay is preferred is also discussed. Simulation results demonstrate that the proposed algorithm improves the energy efficiency over the power allocation scheme for capacity maximization. The RF energy harvesting relay is shown to be preferred over the direct transmission when the environmental noise level is high.

The performance of remote estimation over wireless channels is strongly affected by sensor data losses due to interference. Although the impact of interference can be alleviated by applying cognitive radio technique which features in spectrum sensing and transmitting data only on clear channels, the introduction of spectrum sensing incurs extra energy expenditure. In this paper, we investigate the problem of energy-efficient spectrum sensing for remotely estimating the state of a general linear dynamic system, and formulate an optimization problem which minimizes the total sensor energy consumption while guaranteeing a desired level of estimation performance. We model the problem as a mixed integer nonlinear program and propose a simulated annealing based optimization algorithm which jointly addresses when to perform sensing, which channels to sense, in what order and how long to scan each channel. Simulation results demonstrate that the proposed algorithm well balances the sensing energy and transmission energy expenditure and can achieve the desired estimation performance.

Spectrum sharing between primary users (PUs) and secondary users (SUs) can be realized using economic approaches. In this paper, we propose a method for multiple PUs to share with multiple SUs different idle channels in overlapped licensed areas. Due to the fluctuations of supply and demand in different areas, the SUs are grouped according to their suppliers and the PUs set channel transaction quotas for these SU groups. By applying evolutionary games, the PUs can obtain quotas of evolutionary stable strategy (ESS) and achieve their maximum payoffs theoretically. Furthermore, we design a learning process for the PUs to attain best integer quotas that are realizable. In our simulation, we obtain under two pricing schemes both ESS and best integer quotas that render payoffs close to each other.

Orthogonal frequency-division multiplexing (OFDM) is an ideal transmission technique for cognitive radio (CR) systems because of its flexible nature to support dynamic spectrum access. However, the out-of-band (OOB) radiation of OFDM signals from different CR users must be strictly controlled to protect licensed users operating in the adjacent frequency bands. In this paper, we propose a spectral precoding approach for multiple OFDM-based CR users to reduce OOB leakage and enhance spectrum compactness. By constructing individual precoders to render selected spectrum nulls, our approach suppresses the overall OOB radiation without sacrificing bit-error rate performance of CR users. The proposed approach also ensures user independence thus with low encoding and decoding complexities. Furthermore, our approach can improve bandwidth efficiency by carefully selecting notched frequencies. As a comprehensive application of the proposed approach, two simplified multiuser spectral precoding schemes are provided to reduce the computational complexity. Simulation results demonstrate that our spectral precoding schemes effectively limit OOB radiation and enable efficient spectrum sharing.

We develop cognitive resource allocation scheme to mitigate co-tier and cross-tier interference in overlay femtocell networks. By exploiting statistical models for characterizing multitier interference, our scheme avoids prohibitive exchange of realtime interference statistics in the network. The proposed scheme is independently implemented at each femtocell and allocates resources distributedly in response to the probabilistic interference conditions in the network. This “self-organizing” framework can be useful to address interference management in dense, ad-hoc, and consumer-deployed femtocell networks. Simulation results show that the proposed scheme can improve the throughput of femtocell links while simultaneously reducing cross-tier interference.

Cognitive radio (CR) can successfully deal with the growing demand and the scarcity of the wireless spectrum. To increase the spectrum usage, CR technology allows secondary users to access licensed spectrum bands. Since licensed users have priorities to use the bands, the secondary users need to continuously monitor the activities of the licensed users to avoid interference and collisions. In this paper, we design sequential detection to maximize the throughput of secondary users for a given detection probability of the licensed users. We further investigate the impact of different system parameters on the performance. Numerical results are given to verify the theoretical analysis and demonstrate the performance.

Orthogonal frequency-division multiplexing (OFDM) is an ideal transmission technique for dynamic spectrum access in cognitive radio (CR) systems. In this letter, we propose low-complexity spectrum shaping to enable fast decaying of power spectral sidelobes and enhance spectral compactness for OFDM-based CR systems. Based on a basic scheme mapping antipodal symbol pairs onto adjacent subcarriers, we present two modified schemes to further balance sidelobe suppression and system throughput. Compared with existing spectrum shaping schemes, ours exhibit their advantage of both simplicity and flexibility.

Orthogonal frequency-division multiplexing (OFDM) is a candidate transmission technique for cognitive radio (CR) because of its flexible nature to support spectrum sharing. However, the out-of-band (OOB) radiation of OFDM signal from CR users must be strictly controlled to protect licensed users in adjacent bands. In this paper, we propose a spectral precoding scheme for multiple OFDM-based CR users to reduce OOB emission and enhance spectrum compactness. By constructing individual precoders to render selected spectrum nulls, our scheme suppresses the overall OOB radiation without sacrificing the bit-error rate performance of CR users. The proposed scheme ensures user independence with low encoding and decoding complexity. We also study the selection of notched frequencies to further increase the bandwidth efficiency and implementation flexibility. Simulation results demonstrate that our spectral precoding scheme effectively limits OOB radiation and enables efficient spectrum sharing.

Cognitive radio (CR) technology provides great flexibility in spectrum utilization with orthogonal frequency-division multiplexing (OFDM) as its candidate transmission technique. In this paper, we propose a simple spectrum shaping scheme for OFDM-based CRs to enhance spectral compactness and ensure bandwidth efficiency. By mapping antipodal symbol pairs onto adjacent subcarriers at the edges of the utilized spectrum band, our scheme enables fast power spectral sidelobe decaying without bringing much extra complexity to the transmitter or receiver. Sidelobe suppression and system throughput can be well balanced by adjusting the coding rate while power control on different sets of subcarriers will further deepen the sidelobes. The proposed scheme is also validated by our simulation.

In this paper, we investigate joint relay selection and power allocation to maximize system throughput with limited interference to licensed (primary) users in cognitive radio (CR) systems. As these two problems are coupled together, we first develop an optimal approach based on the dual method and then propose a suboptimal approach to reduce complexity while maintaining reasonable performance. From our simulation results, the proposed approaches can increase the system throughput by over 50%.

Cognitive radio (CR) networks are designed to utilize the licensed spectrum when it is not used by the primary (licensed) users. In this paper, we investigate how a CR user senses multiple channels and determine the optimal transmission duration and power allocation. When performing optimization, we take energy efficiency, throughput, and interference with the primary users into consideration and find a closed-form solution for transmission duration for chosen channels. It is shown that the proposed optimization approach significantly improves energy efficiency and throughput of CR networks.

In this paper, we investigate bandwidth efficient combination of spectrum sensing information in cooperative cognitive radio (CR) networks. We propose a general approach in which CR users are allowed to simultaneously send local sensing data to a combining node through a common control channel, based on which we discuss bandwidth efficient combination schemes under two different cases. In the proposed schemes, the bandwidth required for reporting is fixed regardless of the number of cooperative users. With proper preprocessing at individual users, the proposed schemes maintain reasonable performance with the superposition of sensing data at the combining node. Simulation results also demonstrate the effectiveness of the proposed approach.

Spectrum sensing in cognitive radio (CR) typically assumes that the primary user appears only at the beginning of the sensing block. In this paper, we first establish a probability model regarding the appearance of the primary user at any sample of a CR user frame by utilizing the statistical characteristics of the licensed channel occupancy. While conventional spectrum sensing schemes allocate the same weight to each sample, we vary the weight for each sample based on the probability of the presence of the primary user at the corresponding sample and show that such a probability-based spectrum sensing scheme has nearly optimal performance. Based on the assumption that the idle duration of the licensed channel is exponentially distributed, we further investigate how the probability model on the primary user appearance varies from frame to frame in periodic spectrum sensing and show that both the conventional fixed weight and the probability-based dynamic weight energy detection schemes converge to their respective stable detection probability.

This letter proposes a probability-based scheme for combination of spectrum sensing information collected from cooperative cognitive radio users. The proposed scheme enables combination of both synchronous and asynchronous sensing information by utilizing the statistics of licensed band occupancy and is superior to conventional schemes in terms of detection performance.

Opportunistic spectrum access (OSA) in cognitive radio (CR) networks significantly improves spectrum efficiency by allowing secondary usage of licensed spectrum. In this paper, we propose a probabilistic resource allocation approach to further exploit the flexibility of OSA. Based on the probabilities of channel availability obtained from spectrum sensing, the proposed approach optimizes channel and power allocation in a multi-channel environment. The given algorithm maximizes the overall utility of a CR network and ensures sufficient protection of licensed users from unacceptable interference, which also supports diverse quality-of-service requirements and enables a distributed implementation in multi-user networks. Both analytical and simulation results demonstrate the effectiveness of this approach as well as its advantage over conventional approaches that rely upon the hard decisions on channel availability.

In this paper, we propose probability-based resource allocation in cognitive radio (CR) networks to exploit the flexibility of opportunistic spectrum access (OSA). Assisted by the statistical information acquired from spectrum sensing, the proposed approach maximizes the overall utility of CR users and ensures sufficient protection of licensed users from unacceptable interference. It also supports diverse quality-of-service (QoS) requirements of multiple CR users and allows distributed implementation. Simulation results demonstrate the effectiveness of the approach as well as its advantage over any conventional approach that ignores the statistical information.

To take advantage of time-varying spectrum opportunities, acognitiveradio(CR) network monitors the dynamic usage of the licensed band through cooperative spectrum sensing. In this paper, we propose a probability-based scheme for combination of spectrum sensing information collected from several CR users. Different from conventional cooperative spectrum sensing schemes that assume synchronous local sensing information, our scheme enables combination of both synchronous and asynchronous sensing information by utilizing the statistics of licensed band occupancy. In our scheme, the amount of information from each CR user is flexible and a simplified implementation is also feasible under a symmetrical case. Simulation results demonstrate that our scheme is robust and superior in terms of detection performance.

Probability-based strategies are proposed in this letter to determine the optimal inter-sensing duration and power control for cognitive radio (CR). With utilization of the statistics of licensed band occupancy, appropriate inter-sensing duration is determined to capture the recurrence of spectrum opportunity in time when the licensed signal is detected, or to achieve the maximum spectrum efficiency under a certain level of interference with licensed communication when the licensed signal is declared absent. Transmit power is varied dynamically according to the non-interfering probability at each sample so as to increase the transmission rate and decrease the interference power.

In this paper, detection timing and channel selection for periodic spectrum sensing are investigated to improve the performance of cognitive radio (CR) users. Designed to maximize the channel efficiency of CR users under a given level of interference with licensed users, the detection timing scheme utilizes the statistics of the licensed channel occupancy to determine the optimal starting point of each sensing action. A channel selection scheme in the multichannel multiuser environment is also proposed to specify which channel to detect for the upcoming sensing action based on detection timing. Numerical results demonstrate that our schemes considerably improve the overall channel efficiency while protecting the communication among licensed users.

In this paper, the problem of cell search for IEEE 802.16e OFDMA systems is addressed, which includes two steps: frame detection and cell ID identification. We propose two novel frame detection algorithms: one is based on the pseudo-periodicity property of the preamble signal in time-domain (TD algorithm), and the other one is based on the Euclidean distance in frequency-domain (FD algorithm). In comparison with the FD algorithm, the TD algorithm is more robust to fading and interference, at the cost of a higher implementation complexity. Therefore, we should choose the suitable one based on different system reliability and complexity requirements. As for the cell ID identification procedure, an adaptive correlation length algorithm (ACL algorithm) is proposed, in which the necessary correlation length can be chosen without prior channel knowledge, and thereby greatly reduces the cell search time. The system simulation results show that the proposed frame detection and cell ID identification algorithms are robust and efficient for OFDMA cellular systems, especially for IEEE 802.16e OFDMA systems.