On the Throughput of Large-but-Finite MIMO Networks Using Schedulers

Abstract

This paper studies the sum throughput of the multiuser multiple-input-single-output (MISO) networks in the cases with large but finite number of transmit antennas and users. Considering continuous and bursty communication scenarios with different users' data request probabilities, we derive quasi-closedform expressions for the maximum achievable throughput of the networks using optimal schedulers. The results are obtained in various cases with different levels of interference cancellation. Also, we develop an efficient scheduling scheme using genetic algorithms (GAs), and evaluate the effect of different parameters, such as channel/precoding models, number of antennas/users, scheduling costs and power amplifiers' efficiency, on the system performance. Finally, we use the recent results on the achievable rates of finite block-length codes to analyze the system performance in the cases with short packets. As demonstrated, the proposed GA-based scheduler reaches (almost) the same throughput as in the exhaustive search-based optimal scheduler, with substantially less implementation complexity. Moreover, the power amplifiers' inefficiency and the scheduling delay affect the performance of the scheduling-based systems significantly.

Coded-Caching Using Adaptive Transmission

“…3-4, the results are obtained by optimizing the rate and power allocation. Here, both exhaustive search and the genetic-algorithm based scheme of [15] have been used which have ended up in the same results, indicating the accuracy of the optimization process. In Fig.…”

“…which can be effectively solved by, e.g., exhaustive search or the machine-learning based scheme of [15].…”

“…with η i , i = 1, 2, given in (7), and ( 12) is adapted correspondingly. In (15), γ11 is the probability that C 1 decodes X 1 during the LT period. Also, γ12 gives the probability that, after decoding and removing X1 , the cache node C 1 correctly decodes X 2 in the HT period.…”

“…( e R1 −1 ) (1−α 2 )P −α 2 P ( e R1 −1 ) otherwise (16) with γ11 and γ22 given in (15). Also, in (16) we use Rayleigh channel PDFs and some manipulations to derive the probabilities.…”

Zero Forcing Detection for Short Packet Transmission Under Channel Estimation Errors

“…2)] 1 F 1 (a; 2; x) ≥ 1 + ax/2, which is quite tight (as illustrated in the next section) when R is relatively low (e.g., R ≤ 0.1), as in the ultra-reliable region. 3 Most importantly, the sum-series term within (14) can be conveniently expanded after some straightforward calculus as…”