Channel state information (CSI) is known to be crucial for both enhancing the transmission performance and ensuring physical-layer security (PLS) in wireless communication systems. To estimate a channel’s CSI, the transmitter (Tx) typically broadcasts a predetermined pilot signal, then the receiver (Rx) computes the channel coefficients based on the received pilot signal and returns the estimated CSI to the Tx. Most, if not all, of existing communication algorithms simply assume that the fed-back CSI is reliable/secure. However, in practice, a malicious terminal may send falsified CSI to the infrastructure, thus compromising the throughput and/or security of the communication over the channel. Although some researchers have already identified this vulnerability, demonstrated the feasibility of the CSI-forgery attacks, and designed countermeasures thereof, their methods either (i) are tailored to specific types of attacks, thus lacking generality, or (ii) require modifications to the pilot sequence and hence the protocol. To counter the CSI-forgery attacks and remove/mitigate the deficiencies of existing counter-measures, we first develop a comprehensive CSI-forgery model that can subsume the existing CSI-forgery attacks as special instances to facilitate the design of general countermeasures. Then, we propose a novel approach, called SecCSI, to detect potential CSI-forgery activities and identify their initiators using reconfigurable intelligent surface (RIS). SecCSI leverages the RIS to secretly and dynamically modify the wireless environment transparently to the receiver (Rx) in which the pilot signal is transmitted. The infrastructure can, therefore, detect any attempted manipulation of CSI by appropriately configuring the reflection coefficient matrix of the RIS, transmitting the pilot signal, and analyzing all CSI feedback. SecCSI can serve as a guard module for existing communication systems that simply accept the fed-back CSI without checking its trustworthiness. Our theoretical analysis, experimental and numerical evaluations have shown SecCSI to effectively detect the CSI-forgery attacks and identify the attacker.

With the rapid development of wireless communication technologies, Internet of Things (IoT) has emerged as one of the most important application scenarios. Due to the high density of IoT devices and the limited spectrum resources, along with the miniaturization and sustainability requirements of these devices, the development of low-cost interference management (IM) methods has become crucial for widespread use of IoT. Interference has long been known to harm network performance. Since a desired signal can be distorted by interference, and thus be incorrectly decoded at the destination, we argue that interference can also be transformed intentionally to extract the desired data from interfering signal(s). Based on this observation, we proposeInterference ReCycling(IRC) for the IoT. Under IRC, a recycling signal is generated using the interference a victim IoT device is subjected to, and then sent by the device’s associated gateway. Under the influence of the recycling signal, the desired data of the interfered/victim IoT transmission-pair can be recovered from the interference at the IoT device. We also show that the interfered user’s spectral efficiency (SE) with IRC can be optimized further by properly distributing the transmit power used for the desired signal’s transmission and the recycling signal. We validate the feasibility of IRC by implementing the method on the Universal Software Radio Peripheral (USRP) platform. Our theoretical analysis, experimental and numerical evaluation have shown that the proposed IRC can fully exploit interference, and hence can significantly improve the SE of the victim IoT device compared to other existing IM methods.

Due to the broadcast nature of wireless communications, users' data transmitted wirelessly is susceptible to security/privacy threats. The conventional modulation scheme “loads” all of the user's transmitted information onto a physical signal. Then, as long as an adversary overhears and processes the signal, s/he may access the user's information, hence breaching communication privacy. To counter this threat, we proposeIRS-DMSC, aDistributed Modulation based Secure Communication(DMSC) scheme by exploitingIntelligent Reflecting Surface(IRS). Under IRS-DMSC, two sub-signals are employed to realize legitimate data transmission. Of these two signals, one is directly generated by the legitimate transmitter (Tx), while the other is obtained by modulating the phase of the direct signal and then reflecting it at the IRS in an indirect way. Both the direct and indirect signal components superimpose on each other at the legitimate receiver (Rx) to produce a waveform identical to that obtained under traditional centralized modulation (CM), so that the legitimate Rx can employ the conventional demodulation method to recover the desired data from the received signal. IRS-DMSC incorporates the characteristics of wireless channels into the modulation process, and hence can fully exploit the randomness of wireless channels to enhance transmission secrecy. However, due to the distribution and randomization of legitimate transmission, it becomes difficult or even impossible for an eavesdropper to wiretap the legitimate user's information. Furthermore, in order to address the problem of decoding error incurred by the difference of two physical channels' fading, we developRelative Phase Calibration(RPC) andConstellation Point Calibration(CPC), to improve decoding correctness at the legitimate Rx. Our method design, experiment, and simulation have shown the proposed IRS-DMSC to cripple the eavesdropper while maintaining good performance of the legitimate transmission.

With the rapid advancement of wireless communication technologies, interference has become a key impediment to the improvement of wireless data transmission performance. Traditional interference management (IM) suppresses or adjusts interference at the cost of additional communication resourceswithoutexploiting interference effectively. Especially when the interference is strong, simply eliminating it may be inefficient and costly. Moreover, wirelessly transmitted data is susceptible to threats such as eavesdropping, which also necessitates a thorough investigation. To address these issues cooperatively, we proposeOpportunistic Parasitic Communication with Asymmetric Demodulation(OPC-AD). In particular, we consider the interference experienced by the intended/target communication (i.e., parasitic) receiver (Rx) as the host signal. The target communication constructs a selection signal carrying parasitic indication information based on the data it intends to send and the data decoded by its Rx using asymmetric demodulation from the host signal, and then sends it to its Rx. This signal is used to instruct the parasitic Rx to extract the desired information from the host signal. OPC-AD allows for the exploitation of the interference (i.e., host signal) for data transmission to an interfered Rx. Using AD can also ensure the privacy of the host communication. Since the parasitic communication is concealed within the host signal, eavesdroppers cannot compromise the confidentiality of the parasitic transmission without precisely decoding the selection signal. Furthermore, considering more practical situations, such as non-zero delay/phase difference between the host signal and the selection signal, the implementation of the proposed method under various modulation schemes, and the enhancement of the probability of successful parasitism, we extend the OPC-AD design to cover a broader range of realistic scenarios. Our experimental results validate the applicability of OPC-AD, while our in-depth simulations demonstrate that parasitic communication can effectively thwart eavesdropping and achieve higher spectral efficiency (SE) than other existing IM methods, particularly in strong interference environments.

With the increasing number of wireless devices connecting to networks and sharing the same spectrum resources, interference has become a significant obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and mainly focus on suppressing or eliminating its impact on desired transmissions. However, this often comes at the cost of consuming communication resources. Therefore, design of low-cost IM method that can exploit interference is of research importance. To achieve this goal, we leverage the cost-effectiveness and adaptable deployment capabilities of Intelligent Reflecting Surface (IRS) to propose an IRS Empowered Interference Utilization (IRS-IU) method to realize efficient desired data transmission. By appropriately designing the reflection coefficient of the IRS, a phase shift is introduced to the incident interference, allowing the reflecting interference to interact with its direct counterpart at the interfered receiver (Rx). As a result, the interfered Rx can retrieve its desired data from the mixed interference. In this way, IRS-IU can make full use of the interference to enhance the desired data transmission. Our theoretical analysis and simulation results show that the proposed method can significantly improve the spectral efficiency (SE) of the interfered communication-pair.

With the rapid development of wireless technologies, future communication networks should not only enhance data transmission but also provide accurate and reliable location services. In complex indoor environment, traditional positioning methods encounter challenges related to accuracy and cost due to unpredictable attenuation, multipath interference, and other factors. This paper designs a Machine Learning Enhanced Indoor Positioning method that utilizes RIS-aided Channel Configuration and Analysis, called RCCA-MLEIP, for large-scale warehouse applications. This method consists of two stages: the RIS-aided Channel Configuration and Analysis (RCCA) stage, and the Machine Learning Enhanced Indoor Positioning (MLEIP) stage. In the RCCA stage, multiple RISs are deployed to create reflection paths associated with product tags. The phase coefficients of the RISs are adjusted multiple times, and the mixed signals observed by the reader after each adjustment are recorded. Based on these mixed signals, a system of equations is established to resolve both the phase fading and amplitude fading of each reflection path. In the MLEIP stage, a feature dataset is created using simulation methods, consisting of the phase fading and amplitude fading of multiple reference tags analyzed in the RCCA stage. We use the coordinates of the reference tags as the label dataset to train a Back Propagation (BP) neural network. The trained model can then output the coordinates of a target product tag based on the channel fading features associated with that tag, which are solved/obtained in the RCCA stage. The proposed RCCA-MLEIP utilizes RIS to create a multipath environment for positioning, effectively reducing the hardware costs of the positioning system. Moreover, employing machine learning techniques to estimate the target position can enhance both accuracy and response speed.

With the rapid development of wireless communication technologies, interference management (IM) and security/privacy in data transmission have become critically important. On one hand, due to the broadcast nature of wireless medium, the interference superimposed on the desired signal can destroy the integrity of data transmission. On the other hand, malicious receivers (Rxs) may eavesdrop a legitimate user’s transmission and thus breach the confidentiality of communication. To counter these threats, we propose a novel encoding method, called immunizing coding (iCoding), which handles both IM and physical-layer security simultaneously. By exploiting both channel state information (CSI) and data carried in the interference, an iCoded signal is generated and sent by the legitimate transmitter (Tx). The iCoded signal interacts with the interference at the desired/legitimate Rx, so that the intended data can be recovered without the influence of disturbance, i.e., immunity to interference. In addition, since the data carried in the iCoded signal which is obtained via encoding the desired data and interference cooperatively, is different from the original desired data, the eavesdropper cannot access unauthorized information by wiretapping the desired signal, thus achieving immunity to eavesdropping. Our theoretical analysis, experimental and numerical evaluation have shown iCoding to effectively manage interference while preventing potential eavesdropping, hence enhancing the legitimate user’s transmission and secrecy thereof.

With the rapid deployment and wide use of mobile services and applications, more and more sensitive user information is being transmitted wirelessly. Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). We show that a high-order modulation can be decomposed into multiple quadrature phase shift keying (QPSK) modulations, each of which can be further represented by two mutually orthogonal binary phase shift keying (BPSK) modulations. Therefore, traditional modulation can be realized by two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed two received BPSK signals while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to secure data transmission, enrich the spatial signatures of the legitimate user's transmission by employing two cooperative Txs, and then distribute the user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Moreover, due to random deployment of the two Txs and Rx, delay difference of the two transmissions is introduced. This can be further utilized to make eavesdropping difficult. Our theoretical analysis and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

With the increasing number of wireless devices connecting to networks and sharing spectrum resources, interference has become a major obstacle to improving network performance. Existing interference management (IM) methods treat interference as a negative factor and focus on suppressing or eliminating its impact on the transmission of intended signals. However, this often comes at the cost of consuming communication resources and degrading desired transmission performance. Therefore, the design of a cost-effective IM method that “exploits” interference is important. To achieve this goal, we proposeIntelligent Reflecting Surface Assisted Interference Exploitation(IRS-IE) to realize efficient desired data transmission. IRS-IE leverages the low-cost and adaptive deployment capabilities of IRS to gather and reflect interference towards the interfered receiver (Rx). By appropriately designing the reflection coefficient of IRS, a phase shift is introduced to the incident interference, allowing the reflected interference to interact with its direct counterpart at the interfered Rx. As a result, the interfered Rx can retrieve its desired data from the mixed interference. This way, IRS-IE can make use of the interference to facilitate the desired data transmission. Our theoretical analysis and simulation results show that IRS-IE significantly improves the spectral efficiency (SE) of the interfered communication pair over the other IM methods.

Due to the broadcast nature of wireless transmissions, they are exposed to all surrounding entities and thus vulnerable to eavesdropping. To counter this vulnerability, we propose a new physical-layer secure transmission scheme, called DDM-Sec, based on decomposed and distributed modulation (DDM). DDM-Sec realizes traditional QPSK modulation by using two cooperative transmitters (Txs), each generating a BPSK signal, in a distributed manner. The legitimate receiver (Rx) can decode the desired/intended information from the mixed received signal while preventing the eavesdropper from accessing the legitimate user's information. DDM-Sec can effectively exploit the randomness of wireless channels to encrypt data transmission, enrich the spatial signatures of the legitimate transmission by employing two cooperative Txs. Moreover, DDM-Sec distributes user's information to two transmissions so that none of the decomposed signals alone carry the legitimate user's full information. Our theoretical analysis, hardware experiment, and simulation have shown that DDM-Sec can effectively prevent the eavesdropping, and hence guarantee the secrecy of the legitimate user's data transmission.

In the era of big data, the unprecedented growth of data has spawned the commercial application of data trading markets in the Internet of Vehicles (IoV), while also posing challenges to their economic feasibility. In this paper, we propose a data-energy trading architecture in IoV consisting of a market operator, electric vehicles (EVs), and roadside units (RSUs), where RSUs exchange energy for data collected by EVs, and the market operator solves the data/energy allocation problem to maximize social welfare. However, due to the information asymmetry and fragmentation in the market, it is difficult to determine the optimal data and energy trading amount. To this end, we design an iterative double-sided auction (IDA) mechanism to regulate the interactive behaviors among the trading entities, where the market operator gathers local information from RSUs and EVs, and gradually adjusts the submitted bids of two sides to reach the desired payment and reward rules. The proposed IDA-based data-energy trading algorithm is convergent and satisfies the economic properties of efficiency, incentive compatibility, individual rationality, and budget balance. Numerical results demonstrate the performance of the proposed IDA-based data-energy trading architecture in IoV.

In this paper, we focus on a multi-hop decentralized wireless network consisting of legitimate nodes, adversary wardens, and friendly but selfish jammers, and investigate the routing design for achieving covert communication. For a pair of source and destination nodes, we first provide theoretical analysis for a given route between them to reveal how the covertness performance is related to the jamming power of jammers in the network. Then, we design an incentive mechanism that stimulates selfish jammers to supply artificial jamming to protect communication covertness, by granting them rewards from the source. A two-stage Stackelberg game framework is developed to analyze the strategic interactions between the source and jammers, and so as to determine the optimal settings of rewards and jamming power. Based on these results, we formulate a shortest weighted path-finding problem to identify the optimal route for covert communication between the source and destination, which can be solved efficiently by employing Dijkstra's algorithm. Simulation results demonstrate the performance of the proposed incentive routing scheme.

This paper investigates secure communication in a two-hop relaying system based on physical layer security. The relay employs time-switching simultaneous wireless information and power transfer (SWIPT) to harvest energy and receive information from the source, and then transmits the source’s information and its own information to the destination. A passive eavesdropper exists and wiretaps information transmission over both hops. Under the general system configuration, we first provide performance modeling to reveal the secrecy rate of source and relay as well as identify their utilities. Then, we analyze the hierarchical competition behaviors between the source and relay from a game-theoretic perspective. In particular, we develop a Stackelberg game-based analytical framework to determine the optimal strategies for the source and relay by deriving the Stackelberg equilibrium. Furthermore, we summarize the feasible conditions of utilizing SWIPT-enabled relaying for secure communication and propose the end-to-end transmission scheme accordingly. Extensive numerical results are presented to demonstrate the performance of the proposed SWIPT-enabled relaying system.

With the rapid development of wireless communication technologies, interference management (IM) and security/privacy in data transmission have become critically important. On one hand, due to the broadcast nature of wireless medium, the interference superimposed on the desired signal can destroy the integrity of data transmission. On the other hand, malicious receivers (Rxs) may eavesdrop a legitimate user's transmission and thus breach the confidentiality of communication. To counter these threats, we propose a novel encoding method, called immunizing coding (iCoding), which handles both IM and physical-layer security simultaneously. By exploiting both channel state information (CSI) and data carried in the interference, an iCoded signal is generated and sent by the legitimate transmitter (Tx). The iCoded signal interacts with the interference at the desired/legitimate Rx, so that the intended data can be recovered without the influence of disturbance, i.e., immunity to interference is achieved. In addition, since the data carried in the iCoded signal which is obtained via encoding the desired data and interference cooperatively, is different from the original desired data, the eavesdropper cannot access legitimate information by wiretapping the desired signal. Therefore, immunity to eavesdropping is achieved. Our theoretical analysis and in-depth simulation have shown iCoding to effectively manage interference while preventing potential eavesdropping, hence enhancing the legitimate user's data transmission and secrecy thereof.

Blockchain technology is a promising approach for solving the security and personal privacy problems in Internet applications. The successful commercial deployment of Blockchain markets relies on a comprehensive understanding of the economic and strategic interactions among different entities involved. In this paper, we focus on a blockchain market consisting of a blockchain platform (BP), multiple miners, and blockchain users (BUs), and formulate their interactions as a three-stage Stackelberg game. In Stage I, the BP strategizes the rewards granted to the miners, so as to attract the miners to contribute more computing power used for improving the security and privacy of the blockchain. In Stage II, each miner strategizes its computing power individually for winning the mining compe-tition, which is modeled as a non-cooperative game. In Stage III, the BUs strategize the transaction fee to acquire a corresponding service experience. With the objective of utility maximization, we develop a theoretical framework to analyze the hierarchical interactive behaviors among the entities in a backward inductive way. By solving the Stackelberg equilibrium, we determine the optimal strategies of entities in closed-form. Numerical results are provided to demonstrate the performance of the strategic interactions in the blockchain market.

Wireless networks are usually deployed to cover more overlapping areas, experiencing severe interferences and hence making interference management essential for their viability. Interference alignment (IA) is an effective way of interference management and has thus received significant attention. With IA, at least one degree-of-freedom (DoF) should be used to place the aligned interferences. However, when multiple interferences are from one identical transmitter and to a common destination, IA is not applicable under a one-DoF cost constraint. If these interfering signals are aligned in the same direction at the interfered receiver, they will also overlap with each other at their intended receiver, thus becoming indistinguishable. Moreover, IA is realized by adjusting the spatial feature of disturbance, hence incurring co-channel interference to the interfering transmission-pair’s own communication. To solve this problem, we proposecooperative IA. Instead of adjusting the spatial characteristics of all interferences, we aim to achieve IA by adjusting interferences’ strength along with one or none interference’s signature, based on the interactions among multiple wireless disturbances. We propose two cooperative IA schemes: cooperative IA with space-power adjustment and cooperative IA with power adjustment. With the first method, the overall effect of all interferences is aligned in the orthogonal direction with respect to the desired signal at the interfered receiver. This method modifies the direction of one interference with a properly designed precoding vector and the strength of all interferences via power allocation. Under the second scheme, the strength of all interfering signals is modified so that the overall effect of multiple interferences is orthogonal to the desired transmission while guaranteeing the orthogonality among the interfering signals at their intended receiver. Our theoretical analysis and in-depth simulation have shown that the proposed schemes can effectively manage multiple interferences from an identical source while achieving good performance of the interfering transmission-pair.

With the rapid development of wireless communication technologies, the demand for higher data rate and more concurrent transmissions has been continually increasing. Due to the widespread deployment of various wireless technologies, interference has become a key roadblock to the improvement of network performance. Interference has long been known to be harmful, leading to development of numerous interference management (IM) mechanisms based on resource segmentation or signal processing to mitigate or suppress interference. Since a desired signal can be distorted by interference, and thus be incorrectly decoded at the destination, we argue that interference can also be transformed intentionally to extract the desired data from interfering signal(s). Based on this observation, we propose Interference ReCycling (IRC). Under IRC, a recycling signal is generated with the interference a victim receiver (Rx) is subjected to, and then sent by the Rx's associated transmitter (Tx). Under the influence of the recycling signal, the desired data of the victim Tx-Rx pair can be recovered from the interference at the victim Rx. That is, by exploiting the interactions among multiple signals, i.e., recycling signal and interference, useful data information can be extracted from interference, or the unintended data carried in interference can be artificially converted to the desired one. Our theoretical analysis and numerical evaluation have shown that the proposed IRC can fully exploit interference, and hence can significantly improve the spectral efficiency (SE) of the victim Rx compared to the other existing IM methods.

Internet of Things (IoT) is a new paradigm that involves the interconnection of thousands of devices and home appliances. Due to the scarcity of the spectrum suitable for wireless electromagnetic transmission, many communication systems and devices are close to each other, or even overlapping in spectrum, thus incurring complicated interference situations. Therefore, interference in IoT is worthy of thorough investigation and should be well addressed. There have been numerous interference management (IM) proposals at the interfering transmitter or the interfered transmitter/receiver separately or cooperatively. Moreover, the existing IM schemes rely mainly on the use of channel state information (CSI). However, in some communication scenarios, the option to adjust the interferer is not available, and, in the case of downlink transmission, it is always difficult or even impossible for the interfered receiver to acquire necessary information for IM. Based on the above observations, we first propose a novel IM technique, called interference steering (IS). By making use of both CSI with respect to and data carried in the interfering signal, IS generates a signal to modify the spatial feature of the original interference, so that the steered interference at the interfered receiver is orthogonal to its intended signal. We then apply IS to a wireless local area network (WLAN)-based IoT in which the same frequency band is reused by adjacent basic service sets (BSSs) with overlapping areas. With IS, multiple nearby access points (APs) could simultaneously transmit data on the same channel to their mobile stations (STAs), thus enhancing spectrum reuse. Our in-depth simulation results show that IS significantly improves network SE over existing IM schemes.

Both interference alignment (IA) and interference neutralization (IN) are exploited for coordinated multi-point (CoMP) communications. With the cooperation of the base station (BS), a transmit precoder and a receive filter are jointly designed, and concurrent transmissions of multiple data streams are then enabled. In the design of a precoder, the IN is applied to the interferences carrying the same data so as to align the interfering signals in the opposite direction in a subspace. On the other hand, for interferences carrying different information, IA is employed to align them in the same direction in a subspace, thus reducing the interference signal observed at the receiver side. Based on different precoding schemes at the transmitter's side, receivers adopt zero forcing (ZF) so as to recover the desired data. The proposed IA- and IN-based CoMP (IAN-CoMP) mechanism can achieve effective interference cancellation and suppression by exploiting limited and flexible collaboration at the BS side. It can also make a flexible tradeoff between the cooperation overhead and the system's achievable degrees of freedom (DoFs). We extend the mechanism to general cases where the antenna configurations at both the transmitter and receiver sides, the number of transmitters participating in CoMP, and that of simultaneously served users are variable. Moreover, we discuss both single-location and multi-location-based realizations of the IAN-CoMP. Finally, by defining the average transmit and receive cooperation order, we analyze the upper bound for IAN-CoMP. Our in-depth simulation shows that the IAN-CoMP can significantly improve the spectral efficiency (SE) for cell-edge users.

With the rapid development in wireless communication technologies, interference has become a main impediment to network performance, making interference management (IM) a critical issue. Interference steering (IS) is emerging as a novel way of IM, which can steer the spatial feature of interference to a target subspace by exploiting the interactions of multiple signals over the air to avoid the disturbance to the interfered receiver. In reality, there always exists multiple interferences from single or multiple sources. We propose three ways to realize IS. The first two of them are categorized as individual interference steering (IIS), in which multiple interfering signals are separately adjusted to either an identical direction (a single-target IS, STIS) or different directions (multi-target IS, MTIS), incurring single or multiple degrees of freedom (DoFs) overheads, respectively. Furthermore, by recognizing that the goal of IM is to limit the effect of interference-i.e., the effective portion of interference imposed on the intended transmission- we propose aggregated interference steering (AIS) that exploits the constructive or destructive effects of multiple interfering signals. By considering the overall effect of multiple interferences, the DoF cost of AIS is reduced to one regardless of the number of interference to be managed. Finally, the proposed three IS realizations are adapted to minimize the power cost of steering interference. Our theoretical analysis and in-depth simulation results have shown that the proposed IS schemes can effectively manage multiple interference via better utilizing the DoF and transmit power. The AIS is shown to be advantageous in both power cost and DoF consumption.

This paper proposes a joint transmit-receive spectrum sharing method based on space division multiplexing (SDM) in cognitive radio networks (CRN). By exploiting spatial correlation features between cognitive transmission and interference from cognitive base station (CBS) to primary user (PU) as well as interference from primary base station (PBS) to cognitive user (CU), appropriate primary system and its channel are selected. Consequently spectrum sharing is carried out employing the proposed SDM based strategy. The method is a good complement for conventional cognitive radio (CR) which adopts opportunistic spectrum access. Analysis and simulation results show that compared with the method implemented only at CBS and exhaustive searching, the proposed scheme could achieve near-optimal throughput performance with reduced complexity.