Angle-Based Interference-Aware Routing Algorithm for Multicast over Wireless D2D Networks

Wireless devicetodevice （D2D） communications sharing the spectrum of cellular networks is important for improving spec trum efficiency. Furthermore, introducing multicast and multi hop communications to D2D networks can expand D2D ser vice functions. In this paper, we propose an anglebased inter ferenceaware routing algorithm for D2D multicast communica tions. This algorithm reuses the uplink cellular spectrum. Our proposed algorithm aims to reduce the outage probability and minimize the average hop count over all multicast destina tions （i.e., multicast receivers）, while limiting interference to cellular users to a tolerable level. In particular, our algorithm integrates two design principles for hopbyhop route selec tion. First, we minimize the distance ratio of the candidateto destination link to the candidatetobasestation link, such that the selected route advances closer to a subset of multi cast receivers. Second, we design the anglethreshold based merging strategy to divide multicast receivers into subsets with geographically close destinations. By applying the two principles for selection of each hop and further deriving an adaptive powerallocation strategy, the message can be more effieiently delivered to destinations with fewer branches when constructing the multicast tree. This means fewer duplicated data transmissions. Analyses and simulations are presented to show the impact of system parameters on the routing perfor mances. Simulation results also demonstrate the superiority of our algorithm over baseline schemes in terms of outage proba bility and average hop count.

Radio Frequency Fingerprinting Identification Using Semi-Supervised Learning with Meta Labels

Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.

Semi-Supervised Learning Based Big Data-Driven Anomaly Detection in Mobile Wireless Networks

With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network's performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete(or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4 G LTE-A to detect network's anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.

Reliability-Optimal Pilot-Assisted Transmission for URLLC over Non-Reciprocal MISO Channels: TDD or FDD?

In this paper, we focus on the pilot-assisted transmission design for downlink URLLC over nonreciprocal channels, in which the multi-antenna controller sends mission-critical data signals to a singleantenna actuator. In this system, the prior knowledge of downlink channel state information(CSI) is a prerequisite for reliable data transmission. Generally, the acquisition of downlink CSI is completed either via the uplink pilot measurement exploiting channel reciprocity and time-division duplex(TDD) operation, or via the downlink pilot measurement with quantized feedback and frequency division duplex(FDD) operation. Inspired by this, we aim to investigate how the degree of channel non-reciprocity impacts the transmission reliability of our URLLC system, and the superiority between the TDD mode and FDD mode in terms of transmission reliability maximization. To describe the degree of reliability loss, we derive the closed-form approximations on the transmission error probability of URLLC in TDD and FDD modes, via leveraging the Gauss-Hermite and Gauss-Chebyshev quadrature rules. Following by the theoretical approximations, we demonstrate how to determine the optimal training pilot length and quantized feedback duration that maximize the transmission reliability under given latency constraint. Through numerical results,we validate the accuracy of theoretical approximations derived in this paper, and obtain some meaningful conclusions.

Collaborative D2D Communication in Relay Networks with Energy Harvesting

Although the collaborative transmission of cellular network and device-to-device(D2D)pairs can improve spectrum utilization,it also results in the matual interference,which may be fatal for low-energy D2D pairs.Based on this,we propose in this paper a collaborative D2D transmission scheme with erergy harvesting(CDTEH)in a relay network,where D2D pairs are allowed to access the spectrum of relay network to accomplish their own transmission.In particular,the relay with energy harvesting is arranged to not only expand cellular transmission range but also assist D2D and cellular users to eliminate the mutual interference.To evaluate the performance,rate-energy(R-E)region is introduced.Based on the model,a data rate maximization problem of D2D pair is formulated,subject to a transmission demand of the cellular user and the optimal solution is acquired.Finally,numerical results are provided to validate the proposed scheme improves the data rate of D2D pair ensuring the cellular transmission requirement.

Network-Load Aware Adaptive Channel Access Control for WLAN

Wireless local area network (WLAN) brings us a low cost and high bandwidth experience and thus plays a critically important role in current/future networks to support high-rate transmissions. To better provide quality-of-service (QoS) for WLAN users, we in this paper propose an improved scheme called “A-EDCA”(adaptive EDCA), based on enhanced distributed channel access (EDCA) of IEEE 802.11e under the infrastructure mode. Our proposed scheme aims at efficiently adapting the transmission over WLAN to the time-varying network conditions and mitigating the competition ability unfairness between access point (AP) and non-AP stations (STAs). Specifically, all non-AP STAs adaptive-ly modify the contention window based on the network condition. Moreover, AP skips the backoff phase by setting its backoff counter as zero when non-AP STA completes transmission successfully to relieve the unfairness. At last, simulation results demonstrate the effectiveness of the proposed approach.

Schedule-Aware Power Management for Energy-Efficiency Improvement in 802.11u WLAN

Mobile stations supporting the 802.11u standard can access WLAN automatically when they are within the coverage of the network service provided by this WLAN. To achieve this goal, the stations need to keep “on” states includingidleandactiveall the time. However, studies have noted that the idleness of stations often lead to considerable power consumption. Although the conventional power saving mode (PSM) can provide energy saving effect to some extent, its own disadvantage leads to lower energy efficiency when the number of stations accessing the target WLAN. In this paper, we propose a Schedule-Aware PSM (S-PSM), which can improve the energy efficiency in 802.11u WLAN. Particularly, we use the Generic advertisement service (GAS) defined in 802.11u standard to broadcast the transmission schedule information and all stations switch off their radios based on this information accordingly. We introduce the Respond Contention Window to reduce the collision probability of competition channel. When there is no packet in the access point (AP), AP broadcasts the GAS frame and actives the Idle Timer. All stations will turn into sleep and AP will not send GAS frame until Idle Timer expires. Simulations have shown that our proposed scheme can significantly reduce power consumption compared with the conventional PSM.

Two-Step Resource Block Allocation Algorithm for Data Rate Maximization in LTE Downlink Systems

This study focuses on resource block allocation issue in the downlink transmission systems of the Long Term Evolution (LTE). In existing LTE standards, all Allocation Units (AUs) allocated to any user must adopt the same Modulation and Coding Scheme (MCS), which is determined by the AU with the worst channel condition. Despite its simplicity, this strategy incurs significant performance degradation since the achievable system throughput is limited by the AUs having the worst channel quality. To address this issue, a two-step resource block allocation algorithm is proposed in this paper. The algorithm first allocates AUs to each user according to the users' priorities and the number of their required AUs. Then, a re-allocation mechanism is introduced. Specifically, for any given user, the AUs with the worst channel condition are removed. In this manner, the users may adopt a higher MCS level, and the achievable data rate can be increased. Finally, all the unallocated AUs are assigned among users without changing the chosen MCSs, and the total throughput of the system is further enhanced. Simulation results show that thanks to the proposed algorithm, the system gains higher throughput without adding too many?complexities.

A Novel Cooperative Multicast Scheme Based on Fountain Code

Multicast is an efficient way to support emerging multimedia services over wireless network. Fountain codes are used in multicast systems to enable a robust transmission without CSI feedback and ARQ. We propose a cooperative multicast scheme based on fountain code to improve the performance of multicast. The users are coordinated with each other to decode the message at different time slots within the data transmission of a multicast session. Speci?cally, we take the local channel state information (CSI) and the local residual energy information (REI) into consideration, and apply a relay-selection and power-allocation strategy in our cooperative multicast scheme to prolong the network lifetime, while keeping the transmission delay as low as possible. The simulation results show that the proposed scheme can achieve a good tradeoff between transmission delay and network lifetime.

Adaptive Rate Control for Multi-Antenna Multicast in OFDM Systems

We propose two rate control schemes for multi-antenna multicast in OFDM systems, which aim to maximize the minimum average rate over all users in a multicast group. In our system, we do not require all multicast users to successfully recover the signals received on each subcarrier. In contrast, we allow certain loss for multicast users, such that the multicast transmission rate can be increased. We assume that the loss-repairing can be completed at upper protocol layers via advanced fountain codes. Following this principle, we formulate the rate control problem via beamforming in multi-antenna multicast to optimize the minimum achievable rate for all multicast users. While the computation complexity to solve for the optimal beamformer is prohibitively high, we propose a suboptimal iterative rate control scheme. Moreover, we modify the above optimization problem by selecting a ?xed proportion of users on each subcarrier. The beamformer searching process will then be performed only based on the selected users on each subcarrier, such that the complexity can be further reduced. We also solve this new problem with a low complexity approach. Theoretical analyses and simulation results show that our proposed two rate control schemes can have higher minimum average rate than the baseline scheme without rate control, while achieving low complexity.

Enhancing physical-layer security via big-data-aided hybrid relay selection

The explosive growth in data trac presents new challenges to the new generation of wireless communication systems,such as computing capabilities,spectrum eciency and security.In this paper,we use the network structure,which is adaptable for the big data trac,to improve the security of wireless networks.Speci cally,a big-data aided hybrid relay selection scheme is designed and analyzed to enhance physical layer security.First,considering the ideal situation that an eavesdropper's CSI(Channel State Information)is known to the legal nodes,we propose an optimal hybrid relay selection scheme consisting of the optimal mode selection scheme and the optimal relay selection scheme.In this case,we analyze the upper bound of an eavesdropper's capacity in FD(Full-Duplex)mode and the secrecy outage probabilities of the optimal HD(Half-Duplex),FD,and hybrid relay selection schemes.Through the analysis of data,it is clear that the mode selection is decided by the self-interference of the FD technique.However,the instantaneous CSI of an eavesdropper is dicult to obtain due to the passive characteristic of eavesdroppers in practice.Therefore,a more practical hybrid relay selection scheme with only the channel distribution information of an eavesdropper is further studied,where a weighting factor is employed to guarantee that the hybrid mode is no worse than either the FD mode or HD mode when the self-interference grows.Finally,the simulation results show the improved security of our proposed scheme.