Rate-splitting multiple access (RSMA) is a promising non-orthogonal transmission scheme capable of achieving higher data rates with massive connectivity compared to conventional orthogonal multiple access. However, the effectiveness of RSMA can be significantly hindered by imperfect successive interference cancellation (SIC), leading to severe interference and rate degradation. Existing research has either focused on hybrid RSMA systems, where users are grouped and assigned orthogonal resources, or incorporated improper Gaussian signaling (IGS) to enhance interference management. However, no prior work has explored the combination of these approaches to flexibly manage interference under imperfect SIC. To address this gap, we propose a novel downlink hybrid RSMA system with IGS under imperfect SIC, where users are grouped into pairs to form RSMA groups. Specifically, we introduce the use of IGS for the common message within each group to effectively mitigate interference caused by imperfect SIC. The problem of optimizing the user grouping, subcarrier allocation, rate allocation, power allocation and IGS circularity coefficient, aimed at maximizing the sum rate under the minimum rate requirement, is investigated. We first develop a block coordinate descent method combined with successive convex approximation to optimize all variables except user grouping and subcarrier allocation. Subsequently, a swapping-based algorithm is proposed to refine user grouping and subcarrier allocation iteratively. Extensive simulation results validate the effectiveness of the proposed hybrid RSMA with IGS scheme, demonstrating its superior performance compared to various benchmark schemes in the literature.

The ultra-low latency requirements of emerging Internet of Things (IoT) applications necessitate data transmission in the finite blocklength (FBL) regime, while non-orthogonal multiple access (NOMA) enables massive connectivity. However, the practical imperfection of successive interference cancellation (SIC), a key component of NOMA, can significantly degrade system performance. This paper proposes the use of improper Gaussian signaling (IGS) to mitigate interference caused by imperfect SIC in an uplink NOMA system operating in the FBL regime. Specifically, users are grouped into pairs to form NOMA groups, where IGS is applied at the strong user in each pair to suppress residual interference. A minimum effective-throughput maximization problem is formulated by jointly optimizing user grouping, blocklength allocation, power allocation, IGS circularity coefficient, and decoding error probability. To address the resulting non-convex and mixed-integer nonlinear programming problem, we develop an efficient algorithm that achieves a high-quality suboptimal solution. Simulation results demonstrate that the proposed scheme with IGS-FBL outperforms various benchmark schemes, especially under severe imperfect SIC and stringent decoding error probability requirement conditions. Particularly, at a maximum decoding error probability of 10−4, the proposed scheme with IGS-FBL achieves the minimum effective-throughput that is 14.1×, 38.0×, and 98.8× greater than the benchmark schemes employing IGS-infinite blocklength (IGS-IFBL), proper Gaussian signaling-FBL (PGS-FBL), and PGS-IFBL, respectively.

Content caching at base stations (BS) can reduce backhaul traffic delays to deliver the requested files to users, but its effectiveness is limited by BS storage capacity. We propose a novel approach that integrates artificial intelligence-generated content (AIGC) into the BS operations. Instead of caching entire files, our AIGC-enhanced BS can cache smaller prompts, allowing files to be reconstructed on demand. We explore the challenge of jointly optimizing hybrid caching, AIGC computation, and communication resource allocation with the goal of minimizing average system latency. Given the non-convex nature and the complexity of mixed integer non-linear programming involved, we propose a divide-and-conquer algorithm that breaks down the problem into two timescale levels. Theoretical analysis and simulations confirms that our AIGC-enhanced hybrid content caching outperforms the conventional content caching.

Edge-based machine learning inference typically relies on models trained on static or historical datasets, making them susceptible to performance degradation when data distribution shifts. While model retraining (continuous learning) can alleviate this issue, the integration of real-time data acquisition and model updates remains challenging due to the inherently distributed nature of data across end devices. Unmanned aerial vehicles (UAVs) offer an efficient means of gathering end-device data in edge computing networks (ECNs), thereby enabling realtime retraining. However, existing approaches design UAV-based data collection and model training in isolation, hindering realtime data-augmented training. To address this gap while account for UAVs' limited computational capacity, we propose an integrated optimization framework that coordinates data collection and model retraining through joint UAV swarm deployment and bandwidth allocation, enabling real-time model updates at the edge server. To this end, our objective is to maximize both the collected data volume and the number of training iterations, determined by data volume, transfer time, and update time, within a constrained training window. We formulate this NP-hard problem with tightly coupled variables and develop RL-EDABA, a Reinforcement Learning algorithm Embedded with Device Association and Bandwidth Allocation, enhanced by greedy strategies and convex optimization. Experiments show that RLEDABA effectively mitigates model accuracy loss with lower computational complexity compared with baseline methods.

Content caching is a promising solution to overcome the backhaul traffic delay issue by caching content at the base station (BS). However, the performance of content caching is restricted by the limited BS cache storage. In this paper, we are the first to employ artificial intelligence generated content (AIGC) to enhance the content caching performance by empowering the BS’s intelligence capability. Besides caching a popular file, our AIGC-enhanced BS can alternatively cache its prompt of smaller size to reconstruct the file whenever needed by mobile terminals. We reveal the fundamental tradeoff between caching storage saving and computation delay for AIGC to decide whether to cache a file or its prompt. In this regard, this paper investigates the new problem of joint hybrid caching, computation and communication resource allocation optimization to minimize the average system latency to serve mobile terminals. As the problem is a non-convex and involves mixed integer non-linear programming, we develop a divide-and-conquer algorithm to decompose the problem into two timescale levels. We theoretically prove that our AIGC-enhanced hybrid content caching outperforms the conventional content caching once the computation capacity is non-trivial. Extensive simulation results also validate the superiority of the proposed hybrid content caching.

Wireless federated learning (WFL) enables devices to collaboratively train a global model via local model training, uploading and aggregating. However, WFL faces the data scarcity/heterogeneity problem (i.e., data are limited and unevenly distributed among devices) that degrades the learning performance. In this regard, artificial intelligence generated content (AIGC) can synthesize various types of data to compensate for the insufficient local data. Nevertheless, downloading synthetic data or uploading local models iteratively takes a lot of time, especially for a large amount of devices. To address this issue, we propose to leverage non-orthogonal multiple access (NOMA) to achieve efficient synthetic data and local model transmission. This paper is the first to combine AIGC and NOMA with WFL to maximally enhance the learning performance. For the proposed NOMA+AIGC-enhanced WFL, the problem of jointly optimizing the synthetic data distribution, two-way communication and computation resource allocation to minimize the global learning error is investigated. The problem belongs to mixed integer nonlinear programming, whose optimal solution is intractable to find. We first employ the block coordinate descent method to decouple the complicated-coupled variables, and then resort to our analytical method to derive an efficient low-complexity local optimal solution with partial closed-form results. Extensive simulations validate the superiority of the proposed scheme compared to the existing and benchmark schemes such as the frequency/time division multiple access based AIGC-enhanced schemes.

Proactive eavesdropping improves the wireless information surveillance performance by cognitively sending jamming signals to degrade the suspicious link condition. Current studies all considered that the suspicious users do not apply jamming to defend against the proactive eavesdropping. Contrary to that, this paper considers that a jammer sends jamming signals to degrade the eavesdropping link condition to protect the suspicious communications. A Stackelberg game framework is formulated to track the interactions between the jammer and the monitor and make strategy designs, where the monitor is the leader aiming at maximizing the successful eavesdropping probability and the jammer is the follower aiming at minimizing the successful eavesdropping probability. For the follower’s problem, the optimal jammer’s jamming power allocation policy with respect to the fixed monitor’s jamming power is derived based on the sum-of-ratios optimization. Then for the leader’s problem, the optimal monitor’s jamming power allocation policy is obtained via the bisection search method and the sum-of-ratios optimization. Some design insights are discussed from the obtained policies. The effectiveness of the proposed scheme is verified by simulation results. It is shown that the jamming from the jammer deteriorates the eavesdropping performance and the proposed scheme outperforms various benchmark schemes in existing literature.

Rate splitting multiple access (RSMA) provides a flexible transmission framework that can be applied in mobile edge computing (MEC) systems. However, the research work on RSMA-assisted MEC systems is still at the infancy and many design issues remain unsolved, such as the MEC server and channel allocation problem in general multi-server and multi-channel scenarios as well as the user fairness issues. In this regard, we study an RSMA-assisted MEC system with multiple MEC servers, channels and devices, and consider the fairness among devices. A max-min fairness computation offloading problem to maximize the minimum computation offloading rate is investigated. Since the problem is difficult to solve optimally, we develop an efficient algorithm to obtain a suboptimal solution. Particularly, the time allocation and the computing frequency allocation are derived as closed-form functions of the transmit power allocation and the successive interference cancellation (SIC) decoding order, while the transmit power allocation and the SIC decoding order are jointly optimized via the alternating optimization method, the bisection search method and the successive convex approximation method. For the channel and MEC server allocation problem, we transform it into a hypergraph matching problem and solve it by matching theory. Simulation results demonstrate that the proposed RSMA-assisted MEC system outperforms current MEC systems under various system setups.

Proactive eavesdropping is an effective approach to improve the performance of wireless information surveillance, where suspicious users are legitimately eavesdropped by authorized monitors. For the situation when no monitor is available, we propose to authorize unsuspicious users to surveil suspicious users provided that the quality-of-service (QoS) of unsuspicious users is guaranteed. This paper considers an interference network consisting of a suspicious-transmitter (STx)/suspicious-receiver (SRx) pair and an unsuspicious-transmitter (UTx)/unsuspicious-receiver (URx) pair. We assume that URx acts as a monitor to eavesdrop the suspicious communication, and UTx adopts improper Gaussian signaling (IGS) for flexibly managing the interference to SRx. The QoS of the unsuspicious communication is guaranteed by enforcing its outage performance to be higher than a desirable level. The problem of optimizing the transmit power and the IGS circularity coefficient of UTx to maximize the average eavesdropping rate is optimally solved by the Lagrange duality method. Theoretical results show that IGS is effective when the eavesdropping is successful and the unsuspicious communication is in non-outage status. Simulation results confirm that the proposed design works well even when no additional outage of the unsuspicious communication is allowed, and show that IGS can greatly outperform proper Gaussian signaling in most cases.

Proactive eavesdropping is an effective approach to legitimately surveil the suspicious communications. Current studies all considered that physical layer security (PLS) techniques such as the wiretap coding are not applied by the suspicious users (SUs) to protect their communications. Contrary to that, we consider that the wiretap coding in PLS is adopted by the SUs to defend against the proactive eavesdropping from the legitimate monitor over fading channels. Under the fixed power allocation (FPA) and the water-filling power allocation (WFPA) at the SUs, the problem of jamming power allocation at the monitor to maximize the relative eavesdropping rate under the average transmit power constraint is investigated. The optimization problem is solved by two nested optimizations, where the bisection search method is used in the outer optimization, and the Lagrange duality method and the successive convex approximation method are used in the inner optimization. Simulation results confirm the effectiveness of the proposed algorithms compared to various baseline algorithms. It is shown that the proposed algorithm under the WFPA at the SUs outperforms the one under the FPA at the SUs, and the monitor achieves lower relative eavesdropping rate when the SUs adopt a stricter secure transmission scheme.

To legitimately eavesdrop communications of suspicious users such as criminals, proactive eavesdropping has been proposed as an effective approach. Current works considered that suspicious communication links (SCLs) carry homogeneous services, which is unrealistic due to randomness of user behaviors. Thus, this paper investigates proactive eavesdropping over multiple SCLs with heterogeneous services. The relative eavesdropping non-outage probability and the relative eavesdropping rate are adopted as eavesdropping utilities for delay-sensitive (DS) and delay-tolerant (DT) suspicious services, respectively. The jamming power allocation problem to maximize the weighted sum eavesdropping utility under the average transmit power constraint at the monitor is investigated under the non-adaptive fixed power allocation and the adaptive power allocation schemes adopted by the SCLs. The problem belongs to sum-of-ratios optimization and is transformed to an equivalent parameterized subtractive-form with some extra auxiliary variables. Two nested optimizations are proposed to solve the equivalent problem based on the Lagrange duality method and the damped Newton method. Simulation results demonstrate the superiority of the proposed algorithms compared to various benchmark algorithms in existing literature. It is shown that the impacts of the power allocation schemes of the SCLs on the eavesdropping performance may be different for the DS and DT suspicious services under different system setups.

In this paper, the legitimate surveillance of a suspicious mobile edge computing (MEC) network consisting of a suspicious edge server (SES) and multiple suspicious users (SUs), in the presence of a full-duplex monitor is studied. Each SU has a computation task to complete within a time deadline and can completely or partially offload the task to SES, while the monitor can either jam or assist the suspicious communications during task uploading and result downloading. With the heterogeneous offloading model adopted by the SUs, the problem of optimizing the monitor mode and transmit power to maximize the average ratio of successfully eavesdropped tasks, subject to the monitor transmit power constraint and the task completion time deadline constraint is investigated. The problem is solved via exploring the particular problem structure and adopting the sum-of-ratios optimization. Simulation results show that the proposed algorithm significantly outperforms the benchmark algorithms, especially for the SUs with partial offloading. It is also shown that the proposed algorithm is of low-complexity and achieves almost the same performance as the high-complexity optimal algorithm. Besides, compared to the SUs with binary offloading, the eavesdropping performance for the SUs with partial offloading is shown to be much better.

Short packet communications play key roles in the Internet-of-Things. Conventional Shannon’s coding theorem is not applicable for short packet communications, and the achievable rate in the finite blocklength regime is related to the blocklength and the decoding error probability. Contrary to conventional physical layer security, wireless information surveillance assumes that the communication users are suspicious users and the eavesdroppers are legitimate monitors, and proactive eavesdropping via jamming has been proposed to improve the eavesdropping performance. Existing works on proactive eavesdropping ignored the scenario when the suspicious users adopt the short packet communications. Thus, this paper tries to develop effective proactive eavesdropping schemes suitable for the short packet suspicious communications. Under the truncated channel inversion power allocation and the modified constant power allocation policies at the suspicious source, the problems of jamming power allocation for maximizing the monitoring success probability subject to the average transmit power constraint at the monitor are investigated. Based on the Dinkelbach-type algorithm, the Lagrange duality method and a novel tractable approximate expression for the decoding error probability, we derive suboptimal solutions to the problems. Simulation results demonstrate the effectiveness of the proposed solutions compared to various benchmark algorithms in existing literature.

This paper investigates a proactive eavesdropping scenario where multiple suspicious communication (SC) links are eavesdropped by a full-duplex monitor who can send jamming or constructive signals on each SC link. In order to avoid being discovered by the suspicious users, the monitor restricts the SC quality-of-service (QoS) degradation caused by itself. Under this SC QoS constraint and the average transmit power constraint, the problems of optimizing the mode and transmit power of the monitor to maximize the average successful eavesdropping probability and the average eavesdropping rate are investigated for the delay-sensitive suspicious service and delay-tolerant suspicious service, respectively. By proving that the time-sharing condition is satisfied, the optimal solutions are derived based on the Lagrange duality method. Simulation results validate the effectiveness of the proposed schemes. It is shown that the proposed schemes can provide satisfactory eavesdropping performance improvement even with zero SC QoS degradation requirement.

Recently, wireless powered cooperative cognitive radio networks (CRNs), which combine the technologies of radio frequency (RF) energy harvesting and CR, have drawn great attention. In such networks, energy cooperation between the cognitive users (CUs) and the wireless powered primary users (PUs) can be performed, where the CUs can charge the PUs wirelessly in exchange for the spectrum access. Specifically, energy cooperation and information transmission is executed in two phases, where the CUs transmit their data signals and the PUs harvest energy from these signals in the first phase, and the PUs transmit their data using the harvested energy in the second phase. In particular, we consider two multiple access schemes for the CUs, namely non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA). For both NOMA and TDMA, the PU sum-rate maximization problems under the minimum CU sum-rate constraint are first simplified by exploring particular problem structure, then are transformed to convex problems, and finally are solved optimally. The PU sum-rates of the two schemes are compared theoretically as well as numerically. It is revealed that the circuit power consumption at the CUs, the required minimum CU sum-rate, and the PU energy harvesting sensitivity and saturation thresholds play key roles in the PU performance comparison of the two schemes.

This article investigates physical layer security of a downlink multiuser orthogonal frequency division multiplexing (OFDM) Internet of Things (IoT) system with an access point communicating with multiple legitimate IoT devices in the presence of multiple eavesdroppers. For coordinating multiuser communication, the orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) are considered. The IoT devices are assumed to support simultaneous wireless information and power transfer (SWIPT) and can use the harvested energy to jam the eavesdroppers based on full-duplex. The resource allocation problems of maximizing the sum secrecy rate for the OFDMA and TDMA systems are investigated. We first consider the scenario that perfect channel state information (CSI) is available and derive suboptimal algorithms based on alternating optimization. Then we consider the scenario that CSI is imperfect and propose heuristic algorithms. The secrecy performances of the OFDMA and TDMA systems with jamming from the SWIPT IoT devices are compared using extensive simulations. It is shown that the secrecy performance of the OFDMA system is superior over the TDMA system. It is also shown that the algorithm with imperfect CSI is inferior over the algorithm with perfect CSI, but it can outperform the benchmark algorithm with perfect CSI and without jamming.

Network virtualization has emerged as a significant trend to solve the issues caused by ossification of traditional network. Under the circumstance of network virtualization, substrate network and virtual network are inextricably interdepending each other. The substrate network serves many virtual networks. Substrate network faults may lead to different virtual network faults. A service''s failure may introduce additional influence on other services. Therefore, it has become a big challenge to predict when and where a fault happens in the network. In this paper, we propose a fault diagnosis method by deep learning to predict the failure of virtual network. Our deep learning model enables the earlier failure prediction by the Long Short-Term Memory (LSTM) network, which discovers the long-term features of network history data. Simulation results show that the proposed method performs well on faults prediction.

In this study, the authors investigate the physical‐layer security in a cognitive radio network where both the secondary user (SU) and the primary user (PU) are facing security threats from the malicious eavesdroppers. To protect the PU, the SU acts as a friendly jammer to interfere with the eavesdroppers by splitting a certain portion of the transmit power for sending the jamming noise. The problem of optimising SU scheduling, power allocation and power splitting ratio to maximise the SU ergodic secrecy rate subject to the PU secrecy outage constraint with imperfect channel state information available at the SU is investigated based on the dual optimisation method. In addition, a greedy algorithm is also proposed for minimising the PU secrecy outage probability. Simulation results indicate that the proposed algorithms are effective in improving the PU secrecy performance in terms of secrecy outage probability as well as providing secure communications for the SU.

This paper considers a cognitive radio (CR) network in the presence of a malicious eavesdropper who attempts to receive confidential messages from a pair of primary users (PUs). Under the PU secrecy rate loss constraint and the SU maximum transmit power constraint, a pair of secondary users (SUs) is proposed to interfere with the eavesdropper to improve the PU secrecy level and thus gain its own transmission opportunities. Then, the closed-form optimal power allocation strategy for the SU to maximize its transmission rate subject to the aforementioned constraints is derived. Extensions of the results to the scenarios with multiple eavesdroppers and multiple SUs are also presented, respectively. Numerous simulation results are illustrated to investigate the impacts of various system parameters on the SU transmission rate and the PU secrecy rate. Our results indicate that the PU secrecy rate improves significantly with the help of the SU transmission.

This paper investigates the problem of energy efficient resource allocation in a chunk-based multi-carrier cognitive radio (CR) network. Chunk-based resource allocation is adopted where subcarriers are grouped into chunks to be allocated to the secondary users (SUs). The objective is to maximize the energy efficiency of the CR network while also satisfying the transmit power constraint as well as the interference power constraint for protecting the primary user (PU). For this, based on Dinkelbach method and dual optimization method, an efficient iterative algorithm is proposed. The impacts of the interference power constraint, the transmit power constraint, number of subcarriers within the chunk and the channel coherence bandwidth on the performance of the proposed algorithm are examined by simulations. It is shown that the proposed algorithm not only converges fast but also achieves almost the same performance as the exhaustive search algorithm does. It is also shown that the performance of the proposed algorithm significantly improves compared to the max-sum-rate algorithm and the equal power allocation algorithm especially for large transmit power and interference power limits. In addition, it is shown that the proposed algorithm achieves higher energy efficiency with less number of subcarriers within the chunk.

In this paper, we consider a cognitive relay network which contains a source node, a destination node and a group of network clusters each consisting of several cognitive relay nodes. We choose one relay node with the optimal power value in each cluster to aid the data transmission from source to destination, with the goals of minimizing the total transmitting power and maximizing the network capacity under outage and interference constraints. The research is based on a dual-hop scenario and a multi-hop scenario. The proposed schemes achieve the optimal power allocation and efficient relay selection in both scenarios. The closed-form optimal power allocation and relay selection for a conventional relay network are also listed in this paper.

This paper studies a spectrum sharing cognitive radio (CR) network coexisting with a primary network. In particular, the channel state information (CSI) between the secondary transmitter (STx) and the primary receiver (PRx) is assumed to be outdated due to channel feedback latency. We assume that the secondary user (SU) shall satisfy a given delay quality-of-service (QoS) constraint as well as the average interference power constraint. Our aim is to obtain the maximum arrival rate of the SU under aforementioned constraints with the outdated CSI. In this respect, we derive the optimal power allocation scheme to achieve the maximum effective capacity, and further derive the effective capacity. The closed-form expressions for the lower and upper bounds on the effective capacity are also provided. Numerical and simulation results are presented to show the effects of the outdated CSI. It is shown that the effective capacity of the SU is insensitive to the channel correlation coefficient especially under low channel correlation coefficient.

Cognitive radio is considered to be a key technology for future heterogeneous networks. Cognitive radio network is an evolution of the cognitive radio by extending the radio link scope to network scope, and is defined as a network that can observe its environment, make decisions based on the observations, and then reconfigure according to the decisions, all while taking into account the end-to-end goals. This paper proposes a high level abstraction of the cognitive radio network architecture. The operation of the proposed architecture is guided by the end-to-end goals. The proposed architecture consists of four components: end-to-end goals management, cognition management, self-organization management, and reconfiguration management. The proposed architecture provides the functionality to manage these components, enable communication between them, and facilitate the interfaces between cognitive radio network and its surrounding environment. In order to demonstrate the functionality of the proposed architecture, we present a use case of ubiquitous wireless access services and show that the proposed architecture enables ubiquitous connectivity with harmonized networks and integrated services.

In the future B3G/4G communication systems, it is of broad prospect to implement the femtocell in the enterprise offices or other public places. However, the femtocell operations under such environment are significantly different from the macrocell as well as the usual residential femtocell, and the configuration and optimization are more complex. Although there have been a few works studying the optimization of enterprise femtocell networks, much more issues need to be further investigated. In this paper, we proposed an approach for improving the enterprise femtocell network's performance by automated optimizing the femtocell base station's (FBS's) pilot power as well as antenna pattern, and the recently proposed multi-element antenna which is appropriate for femtocell is also introduced. The aim of the optimization is to maximize femtocell network's coverage while minimize interference between femtocells, and thus improve network's performance parameters such as call drop ratio, average throughput, etc. To reduce the complexity of this algorithm, the optimizing procedure is divided into two steps: first a pilot power optimization approach based on Newton's method is used to maximize the coverage while reduce overlap area of femtocells; then a simulated annealing (SA) algorithm based FBSs' antenna patterns joint selection scheme is considered to further optimize the network. The numerical results showed that the proposed approach can significantly improve the network performance.

In this paper, we consider a cognitive radio (CR) multicast network sharing spectrum with a primary network. To protect the primary transmission, interference power constraint is applied to restrict the transmit power of the cognitive base station (CBS). The objective is to minimize the weighted aggregate outage probability for given target rates for the CR multicast network. Specifically, two types of outage probability are concerned, that is, group outage probability and individual outage probability. For each type of outage probability, the optimal power/rate control scheme is derived. The simulation results are illustrated to validate the proposed power/rate control schemes.

This paper considers a secondary user (SU) sharing the spectrum licensed to a primary user (PU) if limited interference caused to the latter can be guaranteed. In particular, besides the interference power constraint at the PU to protect the PU, the transmit power of the SU is also considered. Under such a setup, we consider the average bit error rate (BER) as the performance metric for the SU, and then derive the optimal power allocation strategies to achieve the minimum average BER of the SU. Simulation results are presented and discussed. It is shown that the optimal power allocation strategies can achieve substantial performance gain for the SU over the water-filling method.

Based on 5.25 GHz wideband channel measurements performed in indoor office environment, empirical path loss models in in-room line-of-sight (LoS), room-corridor, and room-room non-line-of-sight (NLoS) propagation conditions are developed for future wireless radio systems. One-slope and dual-slope log-distance models are adopted in in-room LoS and room-corridor conditions, respectively. In room-room NLoS condition, we propose the enhanced attenuation factor models: AF-extended and AF-linear models to further explore the effect of medium walls, heavy walls and doors. This work offers valuable propagation measurements in a frequency range that is being considered allocating to IMT-advanced systems.