Communications In Long Term Evolution Networks Research Articles

Massive Random Access of Machine-to-Machine Communications in LTE Networks: Throughput Optimization With a Finite Data Transmission Rate

This is a sequel of our previous work [20] on access throughput optimization of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. By incorporating a finite data transmission rate, this paper aims to characterize the effect of data transmission on the optimal access performance of Machine-Type Devices (MTDs). Specifically, both the maximum access throughput and the corresponding optimal Access Class Barring (ACB) factor are obtained as explicit functions of the data transmission rate, which show that even with the ACB factor optimally tuned, the access throughput may deteriorate as the number of MTDs increases, and even drop to zero if the data transmission rate is too small. To boost the data transmission rate, more resources should be allocated to data transmission, which, however, leads to fewer chances for access. In light of the tradeoff between the data transmission rate and the access frequency, the time slot length is further optimized for maximizing the normalized maximum access throughput. Simulation results corroborate that by properly choosing the time slot length, substantial gains can be achieved over the default setting in various scenarios.

Access Delay Optimization of M2M Communications in LTE Networks

This letter focuses on the optimization of access delay performance of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. Specifically, by deriving the probability generating function of access delay, both the first and second moments of access delay are obtained as functions of system parameters. The mean access delay is further minimized by optimally tuning the Access Class Barring (ACB) factor, and significant gains are demonstrated over the standard setting where the ACB factor is fixed.

Optimal Group Paging Frequency for Machine-to-Machine Communications in LTE Networks With Contention Resolution

Group paging is a baseline solution proposed by the long-term evolution (LTE) standardization body for supporting machine-to-machine (M2M) communications in the current-generation and the next-generation cellular networks. Yet, in conventional group paging scheme, upon the reception of paging message, all machine-type devices (MTDs) in a group will simultaneously access the base station, leading to severe network congestion and intolerably low access efficiency. To handle this issue, in this article, we propose a dynamic group paging mechanism, where only the MTDs with packets to send will join the contention process, and the collisions in the random access channel are addressed by the contention resolution scheme. Explicit expressions of key performance measures including the mean access delay of each MTD are derived as functions of the length of waiting period (interval between two consecutive paging periods) $T_{W}$ , where a smaller $T_{W}$ indicates a higher frequency of group paging. It is shown that $T_{W}$ is a key system parameter that determines the crucial tradeoff between the signaling overheads of the system during the paging period and the access delay performance of each MTD. To study how to properly tune the waiting period length, a utility-based analytical framework is established by taking the aforementioned tradeoff into consideration. The optimal waiting period length for maximizing the network utility is derived and verified by simulation results. The analysis in this article reveals that the network should increase the group paging frequency as the traffic becomes heavier or the number of preambles decreases. Providing more preambles can indeed improve the delay performance, while the gain becomes marginal if the number of preambles is large.

QoS hybrid uplink scheduler based on service type for M2M communications in LTE networks

<span lang="EN-US">The introduction of Machine-to-Machine (M2M) communications in cellular networks creates a new set of challenges because of the unique service requirements and features of M2M devices. One of these challenges is the management of radio resources, especially on the uplink because of the unfairness and poor performance that occurs when allocating resources to users. Long Term Evolution (LTE) and LTE-Advanced (LTE-A) are excellent candidates for supporting M2M communications because of their native IP connectivity and scalability for a variety of devices. Therefore, LTE schedulers should be able to meet the needs of M2M devices such as time constraints and specific Quality of Service (QoS) requirements. In this paper, these constraints are studied and analyzed, focusing on three schedulers; they are Round Robin (RR), First Maximum Expansion (FME) and Maximum Throughput (MT). These methods do not provide QoS to users who use different types of traffic flows. The solution proposed in this work is a hybrid model between two schedulers each of them is the best scheduling solution for the real-time service and the other for the non-real-time service, in order to meet QoS criteria maximizing throughput and minimizing packet loss. Video and voip were selected as real-time traffic and best effort for non-real time. The simulations results show that the proposed scheduler reaches the lowest Packet Loss Rate (PLR), delivering highest throughput and goodput among the other strategies.</span>

Massive Random Access of Machine-to-Machine Communications in LTE Networks: Modeling and Throughput Optimization

A key challenge for enabling machine-to-machine (M2M) communications in long-term evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of the M2M communications in LTE networks. Specifically, a novel double-queue model is established, which can both incorporate the queueing behavior of each machine-type device (MTD) and be scalable in the massive access scenarios. To evaluate the access efficiency, the network throughput is further characterized, and optimized by properly choosing the backoff parameters including the access class barring (ACB) factor and the uniform backoff (UB) window size. The analysis reveals that the maximum network throughput is solely determined by the number of preambles, and can be achieved by either tuning the ACB factor or the UB window size based on statistical information such as the traffic input rate of each MTD. Simulation results corroborate that with the optimal tuning of backoff parameters, the network throughput can remain at the highest level regardless of how many MTDs in the network, and is robust against feedback errors of the traffic input rate and burstiness of data arrivals.

A Survey of Random Access Control Techniques for Machine-to-Machine Communications in LTE/LTE-A Networks

Machine-to-machine (M2M) communications refer to the autonomous interaction between connected devices without the human intervention. Recently, the Third-Generation Partnership Project (3GPP) introduced the Long-Term Evolution (LTE) and Long-Term Evolution-Advanced (LTE-A) as the improved version of the fourth-generation (4G) cellular networks. LTE/LTE-A networks have been considered an appropriate infrastructure for implementing M2M communications. However, the LTE/LTE-A networks were originally introduced for human-to-human (H2H) communications which have different characteristics from M2M communications. Thus, the LTE/LTE-A networks must be adapted to support the special characteristics of M2M communications. This work provides a comprehensive review of M2M communications over LTE networks, including M2M architectures, LTE structure, deployment challenges, and access control requirements. Moreover, this work introduces a novel classification for the current Random Access (RA) techniques that have been proposed for M2M communications in LTE networks. According to the main targeted objective, the current RA techniques are classified into three categories: massive access control techniques, energy efficiency techniques, and performance improvement techniques. Each category is further divided into two subcategories, and the relevant RA techniques are presented for each category. Furthermore, an analytical comparison has been provided among the different techniques according to the parameters for each approach. This work can be considered a good starting point for those who are interested in RA techniques for M2M communications over LTE/LTE-A networks.

Proactive Interference Avoidance for Mobile-to-Mobile Communication in LTE Networks

We present in this paper a novel proactive interference avoidance scheme for mobile-to-mobile (M2M) communication as an underlay in Long-Term Evolution (LTE) downlink networks. The proposed scheme introduces an intelligent user-selective resource allocation algorithm that aims to proactively avoid interference between M2M pairs and LTE regular users while achieving a satisfactory link budget for both M2M and LTE regular links. The proposed resource allocation algorithm is based on a novel low-complexity interference estimation approach that requires only channel quality indicator (CQI) information (CSI). Since the CQI reporting mechanism has been already implemented in current LTE systems, the proposed algorithm is indeed practical and standard compatible, compared with the existing works that require perfect knowledge of the channel state information (CSI). In this research, achievable data throughput with LTE-compliant adaptive modulation and coding has been adopted as the optimization measurement, rather than the information-theoretic capacity. Therefore, the performance of the proposed scheme is practically realizable in LTE communication systems. Since the performance of the proposed scheme highly relies on the accuracy of CQI feedback, we also carry out performance analysis to evaluate the impact of CQI estimation error and delay. The simulation results show that the total achievable data throughput for LTE networks is dramatically enhanced by M2M communication with this proposed interference avoidance resource allocation scheme.