Cellular networks have been successfully evolved over the decades. Especially, Long-Term Evolution (LTE) has been exceedingly successful and the security threats against LTE systems have increased rapidly. Particularly,trackingLTE user devices has been shown to be effective as thetemporaryuser identifiers (IDs) are easily extracted and used to locate targeted devices by passive eavesdroppers. We notice that naive approaches, such as frequent updates of temporary user IDs, areinsufficientto mitigate user-tracking attacks since the new and old temporary IDs for the same user device are easilylinkableby adversaries who can measure the wireless channel characteristics between the user device and herself. In this paper, we propose an opportunistic uplink power control scheme to minimize the probability of successful user tracking by an adversary whose location is unknown. We devise the notion of average inference error probability in order to measure the level of users’ location privacy. Moreover, we derive the closed-form expression of the approximated average inference error probability and formulate an optimization problem to maximize the average inference error probability under a constraint of an allowable power budget for each user. Against a passive adversary, our proposed power control scheme effectively degrades an adversary’s inference ability by 50% when 10 users are scheduled in each transmission time slot, which will lead to almost 100% inference error at the adversary over multiple time slots.

Cellular networks have been successfully evolved over the decades. Especially, Long-Term Evolution (LTE) has been exceedingly successful and the security threats against LTE systems have increased rapidly. Particularly, tracking LTE user devices has been shown to be effective as the temporary user identifiers (IDs), which are used in LTE systems to indicate LTE user devices in the system, are easily extracted and used to locate targeted devices by passive eavesdroppers. In this paper, we investigate the impact of uplink power control on the probability of successful user tracking by an adversary whose location is unknown. We devise the notion of average inference error probability in order to measure the level of users’ location privacy. Moreover, we derive the closed-form expression of the approximated average inference error probability and formulate an optimization problem for maximizing the average inference error probability under a constraint of an allowable power budget for each user. For defense, we propose a power control scheme able to effectively degrade an adversary’s inference ability by 50% when 10 users are scheduled in each transmission time slot, which will result in almost 100% inference error at the adversary over multiple time slots.

A massive number of Internet-of-Things (IoT) and machine-to-machine (M2M) communication devices generate various types of data traffic in cellular IoT networks: periodic or nonperiodic, bursty or sporadic, etc. In particular, bursty and nonperiodic traffic may cause an unexpected network congestion and temporary lack of radio resources. In order to effectively accommodate such bursty and nonperiodic traffic, we propose a novel recursive access class barring (R-ACB) technique to optimally utilize the available resources associated with the random access procedure (RAP) that consists of multiple steps in cellular IoT networks, while existing ACB schemes only considered the resource of the first step of RAP, i.e., the number of available preambles. The proposed R-ACB technique consists of two main parts: 1) online estimation of the number of active IoT/M2M devices who have data to transmit to an eNodeB and 2) adjustment of the ACB factor that indicates the probability that an active device sends a preamble to eNodeB. It is notable that the estimation and the adjustment recursively affect each other when R-ACB operates. In addition, we also propose mathematical models to analyze the performance of R-ACB in terms of total service time, average access delay, resource efficiency, and energy efficiency (EE). Through extensive computer simulations, we show that the proposed R-ACB technique outperforms the conventional ACB schemes.

Grant-free multiple access (GFMA) is an emerging technology to accommodate a massive number of devices for 6G-enabled Internet of Things (IoT) networks. The main advantages of GFMA are to efficiently reduce control signaling overhead for resource scheduling while improving resource efficiency. In this article, we propose anovelresource-hopping-based GFMA (RH-GFMA) framework with resource hopping schemes for providing massive connectivity in 6G cellular IoT networks, where each IoT device is allowed to access physical radio resources by using a preassigned resource hopping pattern without not only resource request but also grant procedure, which is the so-called “one-shot” noninteractive multiple access. We exploit three types of resource hopping schemes in the proposed RH-GFMA framework: 1) random hopping; 2) resource group hopping; and 3) Latin-square group hopping. We mathematically analyze the RH-GFMA system performance in terms of the hopping pattern collision probability, maximum allowable packet delay, and interference-over-thermal. Finally, we derive an accommodation capacity of the proposed RH-GFMA framework, which is defined as the expected number of IoT devices accommodated in a cell under a maximum allowable packet-delay requirement and an interference-over-thermal constraint. With the proposed GFMA, massive IoT devices are expected to be efficiently accommodated in 6G wireless networks, while satisfying strict latency and reliability requirements.

Random access (RA) or preamble collision is one of the crucial problems in massive internet-of-things (IoT) at the network entry stage. Since a massive number of IoT nodes simultaneously attempt RAs on the same physical random access channel (PRACH), preambles may be selected by multiple nodes, incurring preamble collisions at the first step of the RA procedure. However, conventional RA models are limited to binary preamble detections which poses severe RA performance loss in the massive IoT environment. In this paper, we propose a deep learning (DL)-based end-to-end RA framework which has detection and resolution abilities for the collided preambles. In particular, advanced preamble classification and timing advance (TA) classifications are performed using deep neural networks (DNNs) for improving the probability of RA success while reducing the delay of the entire RA procedure. The effectiveness of the proposed DNN-based preamble and TA classifiers are demonstrated through extensive simulations. We further evaluate the system-level performance of the proposed DL-based RA model. It shows a significantly higher probability of instant RA success, which makes every node succeed in RA with very limited reattempts, and also maintains a significantly lower RA delay in massive IoT environment.

In this paper, we propose a novel access control (AC) mechanism for cellular internet of things (IoT) networks with massive devices, which effectively satisfies various performance metrics such as access throughput, access delay, and energy efficiency. Basic idea of the proposed AC mechanism is to adjust access class barring (ACB) factor according to performance targets. For a given performance target, we derive the optimal ACB factors by considering not only the conventional preamble collision detection technique but also the early preamble collision detection technique, respectively. In addition, the proposed AC mechanism considers overall radio resources to optimize the ACB factor, which includes the number of preambles, random access response (RAR) messages, and physical-layer uplink shared channels (PUSCHs), while most conventional ACB schemes consider only the number of preambles. In particular, the proposed AC mechanism is illustrated with two representative performance metrics: latency and energy efficiency. Through extensive computer simulations, it is shown that the proposed versatile AC mechanism outperforms the conventional ACB schemes in terms of various performance metrics under diverse resource constraints.

In massive cellular internet-of-things (IoT) networks, periodic and sporadic traffic may be well processed, while bursty traffic may cause an unexpected network congestion or overload problem. Thus, in this paper, we propose a generalized random access (RA) control mechanism considering whole steps of RA procedure and available resources at each step for handling bursty traffic in cellular IoT networks. The proposed RA control mechanism mainly consists of the estimation method for the number of backlogged nodes and the computation method for access class barring (ACB) factors. Through extensive computer simulations, the proposed RA control mechanism shows the enhanced performance in terms of the total service time, the average access delay, and the energy efficiency, compared to the conventional RA control mechanism, which only focuses on controlling preamble transmissions at the first step of the RA procedure.

In this paper, we propose a novel opportunistic downlink interference alignment with a beam selection (ODIA-BS) technique for fog-radio access networks (F- RANs). With joint transmit and receive beamforming strategies, inter-cell interference is effectively suppressed and intra-cell interference is perfectly eliminated. The performance in terms of sum-rate is improved by selecting a proper transmit beam through F- RAN with low signaling overheads. We also develop a spectrally-efficient ODIA-BS (SE-ODIA-BS) technique to further improve the sum-rate performance, where the scheduler at remote radio head selects users with the most orthogonal effective channel vector to each other. Through extensive computer simulations, it is shown that the sum-rate of the proposed ODIA-BS and SE-OIDA- BS techniques outperform other conventional schemes in the F-RAN environment.

In cellular Internet-of-Things (IoT) systems, system overload may occur during a random access (RA) procedure under a limited number of preamble resources and physical uplink shared channel (PUSCH) resources especially when there exist massive IoT devices in a cell. In order to resolve the system overload, the commercial system like 3GPP LTE adopted a group paging (GP)-based uplink access technique, but it has been known that the performance of the GP-based technique drastically degrades as the number of devices increases. In this paper, we first propose a dynamic access control (DAC) mechanism for the GP-based cellular massive IoT system, which dynamically adjusts RA-attempting probability by considering not only the number of available preambles but also the number of available PUSCH resources. We also intelligently combine the proposed DAC mechanism with an early preamble collision detection technique to further improve the RA performance of the cellular IoT system. Through extensive computer simulations, we show that the proposed DAC mechanism outperforms the conventional access control mechanisms, which consider only the number of available preamble resources, in terms of GP completion time, PUSCH resource efficiency, transmission efficiency, and energy efficiency.