The mobile network traffic patterns in urban areas significantly diverge depending on commercial and residential establishments. These regional traffic patterns provide crucial clues for predicting traffic patterns precisely. Previous studies have employed a combination of time-series and convolutional Deep Learning (DL) models to effectively capture the correlation of the regional features and traffic patterns. Despite promising results, these approaches are limited in identifying pattern similarities among sparsely located regions and can be further improved. To this end, this study proposes a GEospatial clustering and residual COnvolutional temporal long Short-term memory (GECOS) framework consisting of clustering and DL components. The proposed Urbanflow Peak Clustering (UPC) component exploits the peak traffic times of daily mobile data to obtain the groups of cells with similar traffic patterns apart from their geographical diversity. The UPC improves the scalability of existing algorithms and enables DL components to improve their accuracy by recognizing unique regional patterns and localizing the training targets. The proposed Residual Convolutional TCN-LSTM (RCTL) serves as the DL component of GECOS that improves TCN-LSTM structure through layer-wise feature transfer and enhances long-term dependency learnability. The RCTL ensures more accurate capturing of extensive spatiotemporal features through structural enhancements. The experiments conducted on real-world mobile traffic data showcase 43% improvement by GECOS compared to state-of-the-art models, enabling precise traffic engineering policies by operators.

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach.

This paper studies the performance of an intelligent reflecting surface (IRS)-assisted radio frequency (RF)-powered wireless network. In the considered network, a multi-antenna base station (BS) communicates with its users (UEs) through downlink transmission and energy-constrained internet-of-things (IoT) devices, which are randomly deployed around a cluster head (CH), utilize the resources of BS for energy harvesting and information transmission. IRS, deployed randomly around CH, reflects the RF signals transmitted by BS to the IoT devices, providing additional energy to harvest. For the considered network, average distances between BS, CH, IRS, and an IoT device have been computed, statistically. Furthermore, the average harvested energy at an IoT device is computed. Instead of considering a fixed transmit power (FTP) of an IoT device, we consider that its transmit power varies randomly depending on the stored energy. Using the Markov chain model, we obtain transmission probability and state-dependent transmit power (SDTP). Analytical expressions for outage probability and throughput of an IoT device are obtained. To manifest the effectiveness of derived expressions, numerical results are compared with Monte-Carlo simulation results. Our results reveal that the proposed SDTP scheme has a performance gain over the FTP scheme, especially in scenarios where harvested energy is in abundance.

Mobile edge computing (MEC) has recently become an enabling technology for mobile operators that are offering a diverse set of services. These services require extensive storage, energy, and computation resources. However, user devices (UDs) have resource constraint to meet the requirements of such services. To tackle the contradiction between resource-constrained UDs and computationally intensive services, MEC have been proposed. MEC servers provide task execution services for UDs. On receiving a service request from the UDs, a MEC server within the network may dynamically allocate computation and memory resources for the task execution. As the MEC servers have limited capacity, efficient utilization of MEC resources is necessitated. Also, it is challenging to find an optimal solution for efficient resource allocations due to different task requirements for a diverse set of services offered to users and dynamicity in wireless networks. To address these problems, we propose a partial task offloading and resource allocation scheme to maximize user task completion within a tolerable time period while minimizing energy consumption. In this paper, we convert the formulated optimization problem to a markov decision process (MDP) and then propose a solution based on the deep deterministic policy gradient (DDPG) algorithm. The performance results show that the proposed method completes a greater number of tasks within a tolerable delay and reduces the energy consumption in the network, compared to those of other conventional schemes.

Unlike conventional cyclic redundancy check concatenated polar codes, selectively applying the CRC protection only to the crucial information bits (CIBs), namely ‘partial protection’, has an effect of lowering the error floor by reducing the number of low Hamming-weight codewords. In the conventional partial protection scheme, CRC is partially concatenated to the information bits whose corresponding row weight of the generator matrix is minimal. However, we observed that the performance even degrades when the protection range (i.e. related to the number of protected information bits) is low compared to the conventional CRC-concatenated polar codes. Therefore, in this paper, we propose a modified partial protection scheme based on a new CIB selection rule. We not only consider the row weight of the generator matrix, but also utilize the polarization weight which is a tool for assessing the quality of the split-channels of polar codes. Simulation results show that the proposed scheme can recover the weak point of the conventional partial protection scheme, thereby obtaining similar or better error-rate performance over all code parameters compared to the standard concatenation schemes.

The cellular Internet of Things envisions the connection of billions of devices with the Internet through cellular networks. These Internet-of-Things Devices (IoDs) are normally energy constrained. In order to provide network services to these energy-constrained IoDs, this article proposes a cellular architecture, whereby IoDs utilize resources of the cellular network for energy harvesting and information transmission. The radio resources of the cellular network are divided by time and frequency, and each time-frequency block is called a cellular resource block (CRB). At the beginning of each time slot, IoDs select CRBs randomly. IoDs receive the control information from the base station (BS) and extract the resource allocation information about the selected CRB. When selected CRBs are busy, IoDs harvest energy from the radio-frequency signals of the BS, and then they use that stored energy to transmit their information to the cluster head (CH) when selected CRBs are idle. In order to obtain the average harvested energy, the random distance between cellular BS and randomly deployed IoD has been statistically calculated. Using the finite-state Markov chain model, the transmission probability of each IoD is calculated. Analytical expressions for the collision probability and average throughput of IoDs are also derived. To verify the derived analytical expressions, system-level simulations are carried out, and it is shown that the analytical results match the simulation results. Moreover, the overall performance of the considered architecture is studied for different system parameters.

Spectrum sharing is one of the ways to overcome the inefficient use of wireless resources. For successful spectrum sharing, dynamic spectrum access based on reinforcement learning has been actively studied. The environment in which mobile network operators (MNOs) share spectrum has not been considered. This paper considers a cellular spectrum sharing scenario where MNOs share the single band in distributive manner. For successful spectrum sharing in the scenario, we propose a learning-based distributed multi-channel dynamic access scheme. The proposed scheme enables multi-channel access dynamically while minimizing interference among MNOs. To evaluate our proposed scheme, we conduct system level simulations in simple network environments. The simulation results indicate that the proposed scheme not only improve the throughput of the network, but also guarantee the fairness among operators.

Futuristic internet of things (IoT) aims to deploy billions of devices. Limited battery power is one of the major concern in deploying these devices. Moreover, it is infeasible to replace the batteries of these devices in most cases. Considering the massive deployment of IoT devices, low power sensors with wireless energy harvesting capabilities are highly desirable. In this regard, low power backscatter communications is an effective candidate. To accommodate the data traffic of these low power backscatter devices, an efficient spectrum access mechanism is required. Cognitive radio provides an effective solution to efficiently utilize the spectrum and improves the network performance in terms of throughput. This paper proposes a spectrum efficient scheduling scheme for low power backscatter sensors where spectrum is shared with the cellular network to achieve spectrum efficiency. The proposed network is evaluated via simulations, and simulation results demonstrate the improved network performance in terms of throughput and energy harvesting capabilities.

In this paper, we apply the Network Mobility protocol (NEMO), which is capable of managing group - based network mobility, in a partially Distributed Mobility Management (DMM) environment. This paper proposed an efficient scheme to support network mobility. When the PR initially accesses the AR, it requests the AR for two prefixes. The first prefix is the Home Network Prefix (HNP), required for the configuration of PR's IP address and the other is Mobile Network Prefix (MNP) which is required for IP configuration of MN. Therefore, even if the MN is added, it is expected that the packet transmission cost is better as compare to the existing schemes, as there is no need for prefix request and information update related signaling between PR and Centralized Mobility Database (CMD). Through analytical results we have shown that our proposed scheme outperforms the existing schemes in terms of packet delivery cost.

In this paper, we propose an ambient backscatter (AB)-enabled decode-and-forward (DF) cognitive relay network with wireless energy-harvesting capabilities. In our proposed scheme, a source node communicates with its destination node via a radio frequency-powered DF relay. It is assumed that the relay node is equipped with two different interfaces and can concurrently harvest/decode and backscatter the received source signals. A power-splitting-based approach is adopted for the information processing and the energy harvesting at the relay. The analytical expressions for the outage probability at all of the receiving nodes are derived. It has been shown that the analytical results match the simulation results. It has also been shown that using the AB for secondary communications can significantly improve the overall network performance in terms of the achievable throughput and the energy efficiency.

Wireless energy harvesting has emerged as an efficient solution to prolong the lifetime of wireless networks composed of energy‐constrained nodes. In this paper, we consider a multipoint‐to‐multipoint relay network, where multiple source nodes communicate with their respective destination nodes via intermediate energy‐constrained decode‐and‐forward (DF) relay. The performance of two different transmission modes, namely, delay tolerant and delay nontolerant, is studied. Based on power‐splitting relaying protocol (PSR), optimal energy harvesting and distribution schemes for both transmission modes are provided. In addition, for more realistic and practical analysis, we consider a nonlinear energy conversion model for energy harvesting at the relay node. Our numerical results provide useful insights into different system parameters of a nonlinear energy harvesting‐based multipair DF relay network.

Wireless energy harvesting is an efficient way to prolong the lifetime of energy constrained networks. In this paper, a two-way amplify-and-forward (AF) based relay network is considered, where two communicating nodes concurrently transmit there information signals to a relay node using time switching based relaying protocol (TSR). The relay node is energy constrained and therefore it first harvests the energy from the received radio frequency (RF) signals. After successful energy harvesting the relay utilizes the harvested energy to amplify and forward the received information signal to its destination. We derive the analytical expression for outage probability and achievable throughput at the receiving nodes. Our numerical results verify our analytical derivation and shows the effect of different system parameters on achievable throughput at receiving nodes.

The effective reuse of the radio resources in D2D communication underlaying cellular system is pivotal for enhancing the total system capacity. Developing practical and effective scheduling schemes is critical to the success of D2D-based cellular systems. An optimal proportionally fair (PF) scheduling scheme that maximizes the logarithmic sum of the user data rates is proposed in this paper. Considering the fact that the computational complexity of the optimal PF scheduling is significantly high, low-complexity heuristic algorithm has also been proposed. It has been shown that the heuristic algorithm achieves high logarithmic sum of the average user data rates while maintaining low-computational complexity. Simulation results are used to verify the performance of the proposed algorithm, and to compare it with the optimal PF scheduling algorithms.

Heavy loads caused by the emerging local data traffic affect the performances of existing wireless communication systems. In order to offload the local data traffic from the congested wireless communication systems, device-to-device communication, which enables direct communications between local devices, is considered as an attractive solution. Qualcomm has introduced a new technology for D2D communication, called FlashLinQ (FLQ), which can improve utilization of wireless medium by making a D2D link to consider SIRs of its own and neighboring D2D links in the decision of medium access. However, since a control of transmission power for a D2D link affects the link scheduling results for both the link and its neighboring links, it is difficult to adequately adjust transmit power in FLQ. In this paper, we propose a new transmission power control scheme for FLQ, which controls each D2D link's transmission power considering its effects to the FLQ's medium access mechanism. From simulation results, we verify the proposed scheme can improve the capacity of FLQ network.

Wireless local area network (WLAN) is the one of the technologies which enable mobile users to connect to local area network through a wireless radio connection in industrial, scientific and medical (ISM) band. Since WLAN can support high data rate communications with low-cost, the use of WLAN is rapidly increasing. However, in congested region, such as shopping mall, airport, and so on, where a lot of WLAN access points (AP) becomes collocated, indiscriminate installation of APs causes high signal interference and heavy contention in overlapping region among APs. In order to solve these problems, we propose self-configurable cooperative RRM (CRRM) scheme for WLAN. The proposed CRRM scheme effectively reduces the overlapping regions by cooperatively coordinating channel assignments and adjusting transmission power of neighboring APs. From simulations for various scenarios, we verify that the proposed scheme can help to mitigate interference and reduce the contention between neighboring APs.

In this paper, we analyze the effects of imperfect channel state information (CSI) on the outage performance of linear zero-forcing (ZF) receiver in time-varying multiple-input multiple-output (MIMO) fading channels. Accurate CSI is an essential condition to improve the overall performance of the rate-adaptive MIMO system. However, acquiring perfect CSI at transceivers is impossible due to feedback delay and channel estimation error. To investigate imperfect CSI environments, therefore, the outage probability is approximated by deriving the probability density function of the output signal-to-noise ratio (SNR) of the ZF receiver, based on temporal MIMO channel correlation. Since the exact distribution of the ZF-output SNR is yet unknown, we apply a Gaussian approximation to obtain mathematical expressions for the outage probability with the assumption of a square MIMO channel. Simulation results shows that our approximation is close to the actual outage probability.

ABSTRACT Secure distributed spectrum sensing (DSS) schemes in cognitive radio networks (CRNs) have been proposed to reliably detect the signals of the primary users (PUs) even when compromised nodes generate forged sensing reports. However, they have limited sensing accuracy for PU signals because of the absence of exact signal patterns of PUs. This is caused by Federal Communications Commission restriction on (no modification of) PUs, and thus, the CRNs cannot communicate with the PUs to obtain such patterns. We propose a verification framework utilizing primary user emulation signals that can be applied to existing DSS schemes to address this challenge. This will reinforce the robustness against forged sensing values. We then develop a concrete verification scheme based on this framework and an existing secure DSS scheme. We evaluate our approach via in‐depth simulation and analysis compared with the existing scheme. Results show that our approach improves sensing accuracy and fusion speed in the cases of attack. Copyright © 2011 John Wiley & Sons, Ltd.

In wireless mobile environments, large bandwidth and high quality of service (QoS) are recently required to support the increased demand for multimedia services. Long term evolution (LTE) is one of the promising solutions for the next generation broadband wireless access systems. In order to support high data rate with low latency, LTE simplifies network architecture and uses orthogonal frequency division multiple access (OFDMA) for downlink transmission technology. Packet scheduling algorithm is one of the important features to support QoS in LTE system. In this paper, we propose delay-aware packet scheduling (DAPS) algorithm considering QoS requirements of delays for various traffic classes, channel conditions, and fairness. To reflect delay experience at scheduling instance, DAPS algorithm observes how queue waiting time of head-of-line (HOL) packet is closed to allowed maximum packet delay. As a result, DAPS increases the number of transmitted packets with satisfying the required delay. Comprehensive simulation results show that DAPS algorithm yields better delay performance compared with two existing PF and M-LWDF algorithms.

Device-to-Device (D2D) communication is the technology enabling user equipments (UEs) to directly communicate with each other without help of evolved nodeB (eNB). Due to this characteristic, D2D communication can reduce end-to-end delay and traffic load offered to eNB. However, by applying D2D communication into cellular systems, interference between D2D and eNB relaying UEs can occur if D2D UEs reuse frequency band for eNB relaying UEs. In cellular systems, fractional frequency reuse (FFR) is used to reduce inter-cell interference of cell outer UEs. In this paper, we propose a radio resource allocation scheme for D2D communication underlaying cellular networks using FFR. In the proposed scheme, D2D and cellular UEs use the different frequency bands chosen as users' locations. The proposed radio resource allocation scheme can alleviate interference between D2D and cellular UEs if D2D device is located in cell inner region. If D2D UEs is located in cell outer region, D2D and cellular UEs experience tolerable interference. By simulations, we show that the proposed scheme improves the performance of D2D and cellular UEs by reducing interference between them.

As demands for multimedia services are increased, shortage of radio resources becomes serious problem in cellular network. In order to overcome the resource shortage problem, device-to-device (D2D) communication has been considered. D2D communication is a technology enabling cellular devices to directly communicate with each other. D2D communication operating as an underlay of cellular networks can efficiently utilize radio resource by reusing resources of conventional cellular networks. However, as D2D communication reuses resource of cellular networks, it can reduce system performance by occurring interference if D2D session is established without considering its effects on cellular networks. Thus, we propose a session setup mechanism in order to improve performance of cellular networks. In the proposed mechanism, D2D communication session is only established when D2D communication is determined to be efficient. By performing simulations, we show that the proposed mechanism can improve total data rate of cellular networks than existing schemes.

As demands for data communications among home/personal devices in home environments increase, various types of home-networking technologies have appeared. Among them, power line communication is one of the most promising wired home-networking technologies, because the existing power line facilities can be utilized for data transmission without deploying any new physical links. HomePlug 1.0 is the most popular power line communication technology, which has been standardized by the HomePlug power line alliance, and attempts to mitigate the effect of time- and frequency-varying channels by enhanced modulation and channel coding. Although HomePlug 1.0 has undergone field trials and simulations, its analytic model and performance was only conducted for throughput under saturation conditions. We propose a new analytic model to evaluate MAC throughput and delay of HomePlug 1.0 both under saturation and under normal traffic conditions. We verify our proposed model via simulations and evaluate the performance of HomePlug 1.0.

Two mobile terminal (MT) power on/off state management schemes are introduced based on the level of location registers (LRs) for managing the state information of MTs. The cost of the two schemes is obtained based on modeling of the state transition behavior of an MT. Based on the observation that there is a tradeoff between the two schemes, we propose an adaptive scheme that incorporates the two schemes. The results show that the adaptive scheme selects an appropriate scheme in real time.

When a universal personal telecommunications (UPT) user visits a mobile network, both terminal mobility and personal mobility are involved and it may take a long time for UPT call delivery to the UPT user because it is required to retrieve both the terminal mobility related data in mobile network and the UPT user related data in the UPT database. If the UPT database also manages location and status information of the MT on which the UPT user has registered, incoming call delivery to the UPT user can be improved. Mobility management schemes for UPT users roaming in a mobile network are analyzed. The performance is evaluated in terms of signaling cost and the number of node accesses during an InCall registration.

An intelligent network architecture with fixed local anchor (FLA) is proposed to support Universal Personal Telecommunication (UPT) services. The performance of the proposed IN architecture is compared with the architecture based on IS-41 considering UPT user's personal mobility in terms of call delivery cost and location update cost.

We propose an approximation method for sojourn time distributions in telecommunication networks consisting of general queuing networks. The queuing network is of the open network type, and has n single server queuing systems with arbitrary interconnections. Customers may enter the network at any queuing system. The interarrival times of the external arrival processes and the service times at the queuing systems are generally distributed. The analysis is based on the methods of decomposition, two-moment approximation, waiting time approximation and semi-Markov chain construction. All related processes are approximated by the first two moments only. Numerical results are compared with simulation ones in order to validate the proposed method.

Since mobile user mobility behaviors can vary according to time periods, e.g. office-going/closing and office hours, the location management traffic load for tracking user positions is also a function of time. The transient characteristics of location management traffic vary according to users' initial starting positions. In this paper, we propose a mobile user mobility model considering office-going/closing and office hours for evaluating the effect of user mobility on the performance of mobile networks. We analyze the transient characteristics of a mobile network in terms of the mean location update rate.

Advanced satellite on-board baseband switching processors have time-space-time (T-S-T) structures which are similar to the terrestrial switching networks (Sw-Nw). Generally, the satellite systems require higher reliability than ground equipment because of more severe environment and lack of repair. This paper proposes fault-tolerant satellite on-board T-S-T Sw-Nw with multiple separated space switches instead of a single spare switch. The authors analyze the mean time to unreliable operation (MTUO), as a performance and reliability index for the T-S-T systems with multiple separated space switches as well as conventional T-S-T systems. The MTUO varies depending on the threshold level of blocking-probability and the offered traffic. In general, T-S-T Sw-Nw with multiple space switches have better performance and reliability than those with the single space switch, and their performabilities are appreciably improved, especially for one additional spare space switch. This study can be extended to analyze the performance and reliability of entire satellite communication systems including the on-board baseband Sw-Nw, Rx interfaces, and Tx interfaces.