Dynamic Scheduling and Power Control in Uplink Massive MIMO with Random Data Arrivals

Chen, Zheng; Björnson, Emil; Larsson, Erik G.

doi:10.1109/icc.2019.8761465

Cited by 3 publications

(6 citation statements)

References 18 publications

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“…, λ K ]. 1 This work can be easily extended to the case with a finite-size reservoir. The only difference is that the transport layer reservoir are updated as L k (t…”

Section: A Problem Formulationmentioning

confidence: 99%

“…Compared to the conference version of this paper [1], which considers only co-located Massive MIMO, we have added substantial new content about dynamic control and optimization in CF Massive MIMO, where the maximum weighted sum rate problem is solved by using the weighted minimum mean-squared error (MMSE) method. To make the paper self-contained, we have also added brief explanations about the implementation of several baseline heuristic power control algorithms.…”

Section: B Contributionsmentioning

confidence: 99%

“…To make the paper self-contained, we have also added brief explanations about the implementation of several baseline heuristic power control algorithms. In addition to maximum ratio combining (MRC) considered in [1], we also show the performance of the algorithm when using zero-forcing (ZF), and with more fairness utility functions.…”

Section: B Contributionsmentioning

confidence: 99%

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Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO

Chen

Björnson

Larsson

2020

IEEE Trans. on Green Commun. Netw.

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In this paper, we study joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with randomly arriving data traffic. We consider both co-located and Cell-Free (CF) Massive MIMO, where the difference lies in whether the antennas are co-located at the base station or spread over a wide network area. The data is generated at each user according to an individual stochastic process. Using Lyapunov optimization techniques, we develop a dynamic scheduling algorithm (DSA), which decides at each time slot the amount of data to admit to the transmission queues and the transmission rates over the wireless channel. The proposed algorithm optimizes the long-term user throughput under various fairness policies while keeping the transmission queues stable. Simulation results show that the state-of-the-art power control schemes developed for Massive MIMO with infinite backlogs can fail to stabilize the system even when the data arrival rates are within the network capacity region. Our proposed DSA shows advantage in providing finite delay with performance optimization whenever the network can be stabilized.Index Terms-Massive MIMO, dynamic resource allocation, cross-layer control, Lyapunov optimization, drift-plus-penalty. has been studied in [21], where the optimal control strategy is decoupled into subproblems of flow control, routing and resource allocation. The proposed algorithms based on the driftplus-penalty (DPP) technique are shown to provide stability and achieve time-average throughput arbitrarily close to the optimal fairness operating point. Similar types of DPP-based algorithms can be found in [22], [23]. A general presentation of cross-layer control (CLC) and resource allocation strategies can be found in [24], with special focus on flow control algorithms that achieve optimal network fairness with stability guarantees. Recently, Lyapunov optimization has been used to study power control and scheduling in delay-aware device-todevice communication [25] and packet-based communication with deadlines [26]. Although the use of Lyapunov optimization in communication systems is not a new topic, very few works have

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“…, λ K ]. 1 This work can be easily extended to the case with a finite-size reservoir. The only difference is that the transport layer reservoir are updated as L k (t…”

Section: A Problem Formulationmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO

Chen

Björnson

Larsson

2020

IEEE Trans. on Green Commun. Netw.

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“…The channel between a

and

at specified locations can serve several UEs with respect to the used encoding and decoding scheme. According to Shannon’s channel capacity [ 20 , 21 , 22 ], the max. SE can be calculated by channel capacity that is demonstrated in Equation ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…Figure7shows the interference peaks when the desired and interfering both UEs signals arrive at the same angle θ 0 of 45 • and when angles of both are mirror reflections of each other such asθ 1 = 180 • − 45 • = 135 • .The SE expression Equation (17) for NLoS is complex as it consists of special functions and summations. The lower bound for N ≥ 1 is modeled Equation(20).…”

mentioning

confidence: 99%

Spectral Efficiency Augmentation in Uplink Massive MIMO Systems by Increasing Transmit Power and Uniform Linear Array Gain

Arshad

Rehman

et al. 2020

Sensors

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Improved Spectral Efficiency (SE) is a prominent feature of Massive Multiple-Input and Multiple-Output systems. These systems are prepared with antenna clusters at receiver (Rx) and transmitter (Tx). In this paper, we examined a massive MIMO system to increase SE in each cell that ultimately improves the area throughput of the system. We are aiming to find appropriate values of average cell-density (D), available bandwidth (B), and SE to maximize area throughput because it is the function of these parameters. Likewise, a SE augmentation model was developed to attain an increased transmit power and antenna array gain. The proposed model also considers the inter-user interference from neighboring cells along with incident angles of desired and interfering users. Moreover, simulation results validate the proposed model that is implementable in real-time scenarios by realizing maximum SE of 12.79 bits/s/Hz in Line of Sight (LoS) and 12.69 bits/s/Hz in Non-Line of Sight (NLoS) scenarios, respectively. The proposed results also substantiate the SE augmentation because it is a linear function of transmit power and array gain while using the Uniform Linear Array (ULA) configuration. The findings of this work ensure the efficient transmission of information in future networks.

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