Resource Allocation for Cell-free Massive MIMO enabled URLLC Downlink Systems

Abstract

Resource allocation is conceived for cell-free (CF) massive multi-input multi-output (MIMO)-aided ultra-reliable and low latency communication (URLLC) systems. Specifically, to support multiple devices with limited pilot overhead, pilot reuse among the users is considered, where we formulate a joint pilot length and pilot allocation strategy for maximizing the number of devices admitted. Then, the pilot power and transmit power are jointly optimized while simultaneously satisfying the devices' decoding error probability, latency, and data rate requirements. Firstly, we derive the lower bounds (LBs) of ergodic data rate under finite channel blocklength (FCBL). Then, we propose a novel pilot assignment algorithm for maximizing the number of devices admitted. Based on the pilot allocation pattern advocated, the weighted sum rate (WSR) is maximized by jointly optimizing the pilot power and payload power. To tackle the resultant NPhard problem, the original optimization problem is first simplified by sophisticated mathematical transformations, and then approximations are found for transforming the original problems into a series of subproblems in geometric programming (GP) forms that can be readily solved. Simulation results demonstrate that the proposed pilot allocation strategy is capable of significantly increasing the number of admitted devices and the proposed power allocation achieves substantial WSR performance gain.

Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

“…Furthermore, this approach has the potential to enhance SINRs by limiting the interference term to only a fraction of UEs. Numerous studies have investigated the integration of APS schemes into signal transmission and detection techniques for CF mMIMO networks [69,78,87,[93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111].…”

“…One scheme relies on power control coefficients, involving high computational complexity, while the other is based on LSF coefficients. Similarly, in [94], an APS approach based on ZF precoding and LSF is employed to improve MSR. The authors of [95] develop a comprehensive strategy that combines DL joint power allocation and APS to minimize the overall energy consumption of a CF mMIMO system, all while adhering to QoS constraints.…”

“…Power control aided [78] UL: Game theory-based [102] DL: ZF/LSF-based [94] Power control aided with MMSE [96] Sequential/LSF-based [97] Linear assignment-based [103] Joint beamforming for mmWave [110] mmWave-based [111] DL: Power control aided with LSF-based [69] QoS-constrained [95] Deep Learning (DeL) SO optimization UL: Graph neural network-based [104] DL: Cluster-based [100] UL: DRL/SE QoS-constrained [109] DL: QoS/DRL-based [106] BO optimization…”

Next Generation Multiple Access with Cell-Free Massive MIMO

“…Furthermore, this study developed improper Gaussian signaling to improve both the Shannon function rate, URLLC rate, and EE. By using the UC approach, Peng et al [234] derived lower bounds on the achievable downlink data rate with imperfect CSI for the MRT, FZF, and local ZF precoding schemes under finite channel blocklength. Then, they maximized the weighted sum rate by jointly optimizing the pilot power and the transmission power, while considering the minimal requirements of decoding error probability and data rate.…”