Cognitive M2M Communications Research Articles

Throughput and delay analysis of cognitive M2M communications

In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.

Joint Resource and Power Allocation for Clustered Cognitive M2M Communications Underlaying Cellular Networks

The massive number of Machine-Type Communication Devices (MTCDs) coexisting with Cellular User Equipment (CUE), in addition to the diverse Quality-of-Service (QoS) requirements of M2M communications and cellular communications, present significant implementation challenges due to interference, congestion, and spectrum scarcity. This makes resource allocation an important but challenging problem. In this article, clustered Cognitive Machine-to-Machine (CM2M) communications underlaying cellular networks is proposed to solve this problem. In this system, MTCDs are grouped in clusters based on their spatial locations and communicate with the Base Station (BS) via a Machine-Type Communication Gateway (MTCG). Underlay Cognitive Radio (CR) is employed so that MTCDs within a cluster can share the spectrum of neighbouring CUE. A joint resource-power allocation problem is formulated and solved using a two-phase resource and power allocation scheme. The goal is to maximize the uplink sum-rate of the neighbouring CUE and clustered MTCDs while satisfying interference, power, and minimum data rate constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints.

Power Control for Cognitive M2M Communications Underlaying Cellular With Fairness Concerns

The explosion in the number of machine-to-machine (M2M) devices, as envisioned in the Internet of Things (IoT), will create a significant challenge in terms of spectrum scarcity. One promising approach for addressing this problem is to accommodate the fast-growing M2M traffic with temporally unused or under-used licensed bands. In this paper, a cognitive M2M communications underlaying cellular network is studied where M2M devices reuse licensed spectrum of cellular users in an opportunistic and fair manner. In particular, we consider two fairness metrics: 1) proportional fairness; and 2) max-min fairness, and design two transmit power assignment strategies for M2M devices that achieve the global fairness objectives, while satisfying an interference temperature constraint at the base station (BS) side. Furthermore, we provide a heuristical floating-ceiling water-filling (FCWF) algorithm with little computational overhead to obtain the optimal solutions. The numerical results show that the proportional fair power assignment could maximize the joint system utility and improve average SINR, while denying data transmission to some M2M devices with high interfering channel gain to the BS; On the other hand, the max-min fair power assignment protects those with high interfering channel gain to the BS by offering them the largest possible power allocation, which is more applicable to scenarios where at least a minimum level of QoS should be guaranteed.

QoE-Oriented Rate Control and Resource Allocation for Cognitive M2M Communication in Spectrum-Sharing OFDM Networks

With the development of wireless communication systems, it is particularly essential to maximize the quality of experience (QoE) of machine-to-machine (M2M) communication. In this paper, we propose a new QoE-oriented uplink rate control and resource allocation scheme for the Internet of Things (IoT) network, by introducing an evaluation model based on mean opinion score (MOS) for different machine-type communication (MTC) devices. The existing works are only dedicated to solving the short-term resource allocation problems by considering the current transmission time slots, which cannot handle long-standing problems. To this end, based on the recently developed Lyapunov optimization, we convert the original long-term optimization problem into the admission rate control subproblem and the resource allocation subproblem in each time slot. To solve the joint power optimization and sub-channel selection subproblems, Gale-Shapley algorithm is utilized to formulate it as a two-dimensional matching problem, and the preference lists are established by the transmission rate and signal to interference plus noise ratio (SINR). In the proposed algorithms, a priority mechanism is employed to ensure fairness. The simulation results demonstrate that without prior knowledge of the data arrivals and sub-channel statistics, the proposed algorithms can significantly improve the overall perceived quality from the users' perspective.

Distributed Gateway Selection for M2M Communication in Cognitive 5G Networks

M2M communication is an important component for future wireless networks. M2M systems consist of a large number of devices that can operate with minimum or no human intervention. However, spectrum demand rises exponentially with the increase in the number of connected devices. Cognitive 5G networks are key to address the issue of spectrum scarcity. Further, use of multiple gateways in cognitive 5G networks for M2M communication can increase system throughput, coverage, and energy efficiency. Nevertheless, using multiple gateways for the secondary M2M devices may cause interference to the primary M2M devices. Existing gateway selection protocols for cognitive M2M communication mostly use single channel CSMA, and thus are not efficient in terms of reducing the interference. Thus, in this article, we propose a DGAP based on multi-channel CSMA for M2M communication in 5G networks. Further, we propose a Lo-DGAP, where each gateway transmits only the worst primary M2M device information rather than transmitting all neighboring primary M2M device information. The proposed Lo-DGAP increases the throughput of the system by reducing the message header payload and is also energy- efficient. Simulation results demonstrate the effectiveness of the proposed schemes in terms of network lifetime and energy consumption.

PRMA-Based Cognitive Machine-to-Machine Communications in Smart Grid Networks

It is expected that cognitive machine-to-machine (M2M) communications in smart grid networks will be indispensable in the near future. In this paper, after discussing the motivation of cognitive M2M communications in a smart grid network, we design a cognitive medium access control (MAC) protocol, keeping in view the unique features of M2M devices and smart grid communication requirements. We propose the use of packet reservation multiple access (PRMA), carry out its feasibility study, and adapt and significantly enhance it with modifications particularly tailored for M2M environments. The proposed protocol is centralized in nature and utilizes a specialized frame structure for supporting the coexistence of the cognitive M2M network with the primary network. This frame structure is further optimized, considering different tradeoffs pertaining to primary network protection and the utility of a secondary M2M network. Performance evaluation is carried out by analytical modeling and simulation studies. We also present a case study for application of the proposed protocol in advanced metering infrastructure (AMI) networks under the dynamics of power systems.

Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective

Machine-to-machine (M2M) communications enables networked devices to exchange information among each other as well as with business application servers and therefore creates what is known as the Internet-of-Things (IoT). The research community has a consensus for the need of a standardized protocol stack for M2M communications. On the other hand, cognitive radio technology is very promising for M2M communications due to a number of factors. It is expected that cognitive M2M communications will be indispensable in order to realize the vision of IoT. However cognitive M2M communications requires a cognitive radio-enabled protocol stack in addition to the fundamental requirements of energy efficiency, reliability, and Internet connectivity. The main objective of this paper is to provide the state of the art in cognitive M2M communications from a protocol stack perspective. This paper covers the emerging standardization efforts and the latest developments on protocols for cognitive M2M networks. In addition, this paper also presents the authors' recent work in this area, which includes a centralized cognitive medium access control (MAC) protocol, a distributed cognitive MAC protocol, and a specially designed routing protocol for cognitive M2M networks. These protocols explicitly account for the peculiarities of cognitive radio environments. Performance evaluation demonstrates that the proposed protocols not only ensure protection to the primary users (PUs) but also fulfil the utility requirements of the secondary M2M networks.

Feasibility of cognitive machine-to-machine communication using cellular bands

We evaluate the feasibility of cognitive machine-to-machine communication on cellular bands from engineering and business perspectives. We propose a hierarchical network structure for cognitive M2M communication, where cluster headers gather M2M traffic using cognitive radio and forward it to the cellular networks. This structure can resolve the congestion problem that arises in conventional M2M systems. We obtain the optimal network parameters that minimize congestion at the radio access network. In addition, we investigate the business value of cognitive M2M on cellular bands. Taking into account the network usage fee, service fee, and hardware production costs, we model the profit structure of M2M services and derive the condition under which the CR type of M2M communication is superior to conventional M2M communication. We find that the optimal network design parameters (i.e., the number of cluster headers and cluster size) for business value would be different from the parameters expected from an engineering point of view. We believe that cognitive M2M communication can be a good solution for dealing with excessive M2M traffic in cellular networks, in terms of technical feasibility and business opportunity.