Throughput Optimization of M2M Communications in LTE Networks

　

In [Zhan-Dai'18], a new analytical framework was proposed for optimizing the access efficiency of M2M communications in LTE networks. Specifically, to capture the key feature of connection-based random access process, a novel double-queue model is established, where each MTD has one request queue and one data queue, and only the request queue is involved in the contention. By characterizing the state transition of each access request, the network steady-state points are obtained as the non-zero roots of the single fixed-point equation of the limiting probability of successful transmission of access requests. The complexity is independent of the number of MTDs even with the queueing behavior of each MTD taken into consideration, which is highly attractive in the massive access scenario.

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 key assumption in [Zhan-Dai'18] and existing studies is that for each MTD, once its access request is successful, it can always clear its data queue within one time slot, i.e., one period of PRACH subframes. Though a good approximation for light-traffic scenarios that are common for M2M communications, this assumption may not hold true when the traffic load becomes heavy or the resource for data transmission is insufficient. In that case, it may take more than one time slot for MTDs to clear their data queues. Intuitively, with prolonged data transmission time, the access efficiency would decrease because newly generated access requests may not be accommodated until the ongoing data transmission completes. It may even drop to zero when the data transmission rate is too small to clear the data queues. To improve the access efficiency, more resources may be allocated to data transmissions to boost the data transmission rate, with which, however, the time slot length, i.e., the period of PRACH subframes, would be enlarged, indicating that the MTDs can access the channel less frequently. Apparently, the time slot length determines a crucial tradeoff between the data transmission rate and the access frequency, which should be properly set to optimize the access performance.

In the sequel [Zhan-Dai'19], the analytical framework proposed in [Zhan-Dai'18] is extended to analyze the effect of the data transmission rate \beta, which is defined as the total number of data packets that can be transmitted per time slot, on the optimal access performance of MTDs in LTE networks. Specifically, based on the double-queue model, a discrete-time Markov renewal process is established to characterize the behavior of each access request, where a data transmission state is introduced to describe the case of a data transmission lasting for more than one time slot. To evaluate the access efficiency, the access throughput is characterized and maximized by optimally tuning the ACB factor. Both the maximum access throughput and the optimal ACB factor are obtained as explicit functions of the data transmission rate \beta, the number of preambles, the number of MTDs and the traffic input rate of each MTD. The analysis shows that the maximum access throughput is a monotonic increasing function of the data transmission rate , which becomes zero when is smaller than the aggregate traffic input rate. In that case, the data throughput, which is defined as the average number of transmitted data packets per time slot, reaches the maximum value, but the network becomes unstable as the data queues can never be cleared.

For improving the data transmission rate, a larger time slot length should be chosen, which, nevertheless, reduces the access frequency of MTDs. The analysis further demonstrates that to optimize the access performance, the time slot length should be carefully selected based on the number of MTDs, the traffic input rate and data transmission rate per subframe. The optimal time slot length for maximizing the normalized access throughput, i.e., the average number of successful access requests per millisecond, is characterized, and shown to lead to significant gains over the default setting in various scenarios.