Dynamic access class barring and relay assisted radio resource allocation methods for cellular M2M networks

In this paper we present a simple method to exploit the diversity of interference in heterogenous wireless communication systems with large number of machine-type-devices (MTD). We consider a system with a machine-type-aggregator (MTA) as underlay to cellular network with a multi antenna base station (BS). Cellular users share uplink radio resources with MTDs. Handling the interference from MTDs on the BS is the focus of this article. Our method takes advantage of received interference diversity on BS at each time on each resource block and allocates the radio resources to the MTD with the minimum interference on the BS. In this method, BS does not need to take the interference from MTD into account in the design of the receive beamformer for uplink cellular user, hence, the degrees of freedom is not used for interference management. Our simulation results show that each resource block can be shared between a cellular user and an MTD, with almost no harmful interference on the cellular user.

Owing to the nature of battery-operated sensors, the residual energy of their battery should be considered when a sink node collects sensory data from them. If a sink attempts to collect data from a sensor that does not have sufficient residual energy to transmit data, the utilization of wireless communication resources may be degraded. However, requiring a periodic residual energy report of the sensors considerably reduces the energy efficiency of them. Consequently, the sink node must be able to estimate the residual energy of the sensors without additional information exchange before attempting to collect data. To tackle this problem, we propose a data collection scheme for mobile sinks in cellular Internet-of-things (IoT) networks. The scheme consists of two phases. In the first phase, the mobile sink estimates the residual energy of the surrounding sensors. To reduce the complexity, we design a state diagram composed of three states based on the Markov chain of the sensor. The sink node calculates the communicable likelihood of each sensor based on the state diagram and collects data from the sensor with the highest likelihood. In the second phase, a cellular base station (BS) selects the appropriate mobile sink to improve the signal-to-interference-plus-noise ratio (SINR) of the signal from a cellular user. Each sink node delivers the bidding price to the BS based on the likelihoods of the sensors. The BS determines an appropriate sink node to allocate resources based on the received bidding prices. Moreover, we show the performance of the proposed method in terms of SINR and IoT network utilization through simulation.

In this paper, a spectrum sharing protocol is proposed for device-to-device (D2D) communication overlaying cellular networks. Specifically, the protocol allows the D2D users to communicate bi-directionally with each other while assisting the two-way communication between the cellular base station (BS) and the cellular user (CU) over the same time and frequency resources. The achievable rate region of the sum-rate of the D2D transmissions versus that of the cellular transmissions is evaluated. The Pareto boundary of the region is found by optimizing the transmit power at BS and CU as well as the power splitting factor at the relay D2D node. We find through numerical results that the proposed two-way protocol with power control at the BS and CU is effective to improve the sum rate for both the D2D and cellular communication.

For device-to-device (D2D) communication under laid in a cellular network with uplink resource sharing, both cellular and D2D pairs may cause significant inter-cell interference (ICI) at a neighboring base station (BS). In this paper, under optimal BS receive beamforming, we jointly optimize the power of a cellular user (CU) and a D2D pair for their sum rate maximization, while satisfying minimum SINR requirements and worst-case ICI limit in multiple neighboring cells. We solve this non-convex joint optimization problem in two steps. First, the necessary and sufficient condition for the D2D admissibility under given constraints is obtained. Finally, we consider joint power control of the CU and D2D transmitters. We propose a power control algorithm to maximize the sum rate. Depending on the severity of ICI that D2D and CU may cause, we categorize the feasible solution region into five cases, each of which may further include several scenarios based on minimum SINR requirements. The proposed algorithm is optimal when ICI to a single neighboring cell is considered. For multiple neighboring cells, we provide an upper bound on the performance loss by the proposed algorithm and conditions for its optimality. We further extend our consideration to the scenario of multiple CUs and D2D pairs, and formulate the joint power control and CU-D2D matching problem. We show how our proposed solution for one CU and one D2D pair can be utilized to solve this general joint optimization problem. Simulation demonstrates the effectiveness of our power control algorithm and the nearly optimal performance of the proposed approach in the setting of multiple CUs and D2D pairs.

With device-to-device (D2D) based relay-assisted communications, deep faded cellular users (CUs) get a better connectivity. The deep faded CU's achievable throughput of the link from base station (BS) to the CU is less than the rate demand from the CU (unsatisfied CUs). In this paper, we study the resource allocation problem for both CUs and D2D pairs to increase the achievable throughput. This is accomplished by reducing the difference between rate demand and rate achieved for the active CUs. The resource allocation optimization problem is solved in three steps. The initial set of resources that can be allocated to each active CUs is determined in the first step. In the second step, it finds the set of active CUs which needs D2D assistance and then it finds suitable D2D relay nodes (DRNs). Resource allocation to those DRNs and D2D pairs without degrading the link throughput of active CUs is performed in the third step. Numerical results shows significant amount of throughput gain for the active CUs using D2D communication. We also measure the performance of our proposed model in terms of churn rate and the number of excess resource blocks (RBs) in the system.

In the fifth generation (5G) wireless communication network, to better offer service for different vertical industry applications, network slicing is needed to meet the differential requirements. In this paper, we study the joint problem of radio resource allocation and power resource allocation in the radio access network (RAN) to improve the radio resource utilization with multi-user demands in smart grid scenario. Different with conventional single-agent algorithm, we propose a non-orthogonal multiple access (NOMA) based multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve this dynamic optimization problem. The scheme determines the remote radio unit (RRU) and physical resource block (PRB) allocation for each user, and updates the neural network to dynamically adjust the resource allocation strategy to maximize the long-term rate of network slices. The reliability and latency requirements of network slicing are guaranteed as well. Simulation results show the convergence and efficiency of the proposed algorithm.

In this paper, we propose a system model for cellular vehicle-to-everything (C-V2X) networks mode-3, with two user groups: cellular users and vehicular users. Cellular users communicate with each other through the base station (BS). Vehicular users communicate through direct vehicular links while sharing the spectrum of an orthogonal frequency division multiplexing (OFDM) system with cellular users. In particular, we focus on the underlay approach, in which vehicular users can transmit at the same time in a sub-carrier that is being used by a cellular user. Assuming a limited number of sub-carriers, the number of allowed vehicular links is limited by the quality of service (QoS) requirement for cellular users because of the interference from vehicular links. Our goal is to maximize the number of vehicular links while satisfying the QoS for cellular users. To achieve this goal, we state the optimization problem of resource allocation in C-V2X networks mode-3, assuming high mobility of vehicular users, which results in Doppler effect for them. We also assume that the global channel state information (CSI) of mobile links is not available for the BS. We propose a novel scheme for the BS to jointly allocate resources, such as power and sub-carriers, to cellular and vehicular users, while minimizing the number of non-allowed vehicular links, considering high data rate requirements for cellular users and high reliability requirements for vehicular users. Using numerical results, we obtain the maximum number of vehicular links that can be established for a given available bandwidth and cellular user distribution.

In a traditional cellular network, the base station (BS) plays a critical role in linking devices for communications, but the huge user demand, especially in cities, has become a cost burden to the BS. The technique of device-to-device (D2D) communications underlaying cellular networks solves this problem and adds benefits to resource utilization. Nevertheless, the issue of how to efficiently utilize both radio resource and power has become a challenge. In this paper, we study power-saving resource allocation for D2D communications underlaying a single-cell environment, where all devices are assumed to have ability to use the D2D linking mode. An innovative algorithm is presented to minimize total transmission power consumption while satisfying minimum data-rate requirements of all users. Simulation results demonstrate that the presented approach has better power-saving ability compared with that of conventional ones under all traffic loads.

This paper studies the optimal joint beamforming and power control strategy for device-to-device (D2D) communication underlaying multiuser multiple-input multiple-output cellular networks. We consider multiple antennas at the base station (BS) and a single antenna at each cellular user (CU), D2D transmitter (DT), and D2D receiver (DR). We aim to minimize the total transmission power of the system by jointly designing the transmit beamforming at the BS and the transmit powers for both BS and DTs, while satisfying the signal-to-interference-plus-noise ratio based quality-of-service constraints for both CUs and DRs. Due to the nonconvex nature of the problem, we apply the semidefinite relaxation technique to find the optimal solution, which always satisfies the rank-one constraint. We also investigate three suboptimal fixed beamforming schemes: zero-forcing (ZF), regularized ZF, and hybrid maximum ratio transmission-ZF, where the focus is to minimize the total transmission power while reducing complexity. When perfect channel information is not available, we propose a robust transmit power minimization strategy with ZF beamforming that only requires limited feedback based channel direction information at the BS. Finally, computer simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Device-to-Device (D2D) communications provide the means to users of cellular networks for reusing licensed spectrum by creating a direct link between two devices. However, enabling D2D communications in a cellular network presents a challenge in resource allocation because of the potential severe interference it may cause to the cellular network due to the reuse of the spectrum with the cellular users. Such interference can be prevented otherwise diminish if radio resource assigned intelligently with the coordination from the eNodeB (eNB). In this paper, we analyze the resource allocation problem in a multi cell environment and we propose a new scheme for Intra-Inter-Cell D2D communication when both Intra Cell D2D (Intra-cell-D2D) and Inter Cell D2D communication(Inter-cell-D2D) are present in the system.

SummaryIn this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters.

Background: Manmade disasters like explosion, toxic wastes, chemical spills etc. have become an imperative concern for our society. Manmade disasters are not everyday phenomenon. Thus, the allocation of separate resources is not realistic. The idea is to use existing cellular infrastructure to implement single hop cognitive radio sensor network for protecting human being from manmade disasters. Objective: The main objectives of this paper are as follows: (a) design of an efficient iterative power regulation algorithm based on the Firefly Algorithm for the proposed network, (b) computation of sensor nodes’ optimal power for different position of the cellular user from the base station assuming that cellular user compromises power for its own sensor node, (c) computation of the maximum number of sensor nodes coupled with single cellular user for different distances from the base station in worst channel condition, and (d) comparative performance analysis with state-of-the-art algorithms. Method: In the presence of explosive and toxic gases, cognitive radio in sensor node establishes a connection with the nearest base station to send pre-disaster alert signal utilizing cellular user’s resources. The power is distributed among sensor nodes maintaining the fundamental requirements of cellular users. Here, an iterative power regulation mechanism is employed for distributing the power between sensor node and cellular user to achieve reliable utility of the network. The fitness function is designed under the constraints of interference and the designed algorithm is implemented in MATLAB platform towards the search of optimal power of sensor nodes by maximizing the fitness function. Results: Comparative performance analysis demonstrates the effectiveness of the proposed algorithm in terms of speed of convergence, the position of mobile phone user from the base station, number of coupled sensor nodes with single cellular user, and Jain’s fairness factor. Conclusion: The proposed network controls the occurrence of manmade disasters and achieves reliable transmission of emergency information prior to disaster without disrupting cellular phone users. Simulation results validate that the proposed IPRFA algorithm outperforms with respect to state-ofthe- art methods in terms of sharing power and Jain’s fairness factor.

This work proposes a new video buffer framework (VBF) to acquire a favorable quality of experience (QoE) for video streaming in cellular networks. The proposed framework consists of three main parts: client selection algorithm, categorization method, and distribution mechanism. The client selection algorithm was named independent client selection algorithm (ICSA), which is proposed to select the best clients who have less interfering effects on video quality and recognize the clients’ urgency based on buffer occupancy level. In the categorization method, each frame in the video buffer is given a specific number for better estimation of the playout outage probability, so it can efficiently handle so many frames from different videos at different bitrates. Meanwhile, at the proposed distribution mechanism, a predetermined threshold value is selected for lower and upper levels of playout outage probability. Then, the control unit at the base station will distribute the radio resources and decide the minimum rate requirement based on clients’ urgency categories. Simulation results showed that the VBF grantees fairness of resources distribution among different clients within the same cellular network while minimizing the interruption duration and controlling the video buffer at an acceptable level. Also, the results showed that the system throughput of the proposed framework outperforms other existing algorithms such as playout buffer and discontinuous reception aware scheduling (PBDAS), maximum carrier‐to‐interface ratio (MAX‐CIR), and proportional fair (PF) due to enhancing the quality of experience for video streaming by increasing the radio resources in fairness manner.

This paper uses big data technologies to study base stations’ behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, first we exploit big data platform and technologies to analyze cellular network big data, i.e., Call Detail Records (CDRs). Our CDRs data set, which includes more than 1,000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Second, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations’ behaviors and dissect their predictability. Third, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations’ activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations’ behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.

On the effectiveness of single and multiple base station sleep modes in cellular networks