On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems

Bashar, Manijeh; Cumanan, Kanapathippillai; Burr, Alister G.; Debbah, Mérouane; Ngo, Hien Quoc

doi:10.1109/twc.2019.2892463

Cited by 168 publications

(178 citation statements)

References 36 publications

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“…For the multi-path case, P A = AA † = A(A H A) −1 A H represents the projection matrix onto the subspace spanned by A, and A is the steering matrix given in (3). As shown in [30] while including the large scale path-loss parameter β, the MSE (18) of the considered DFT estimator coincides with that of the ML estimator (20). Using Lemma 1 in [30] while including the large-scale fading parameter and p k p H k = 1, the MSE of υ is expressed as…”

Section: Performance Analysismentioning

confidence: 93%

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Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Abdallah

Mansour

2020

IEEE Trans. Commun.

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Cell-free massive MIMO communications is an emerging network technology for 5G wireless communications wherein distributed multi-antenna access points (APs) serve many users simultaneously. Most prior work on cell-free massive MIMO systems assume time-division duplexing mode, although frequency-division duplexing (FDD) systems dominate current wireless standards. The key challenges in FDD massive MIMO systems are channel-state information (CSI) acquisition and feedback overhead. To address these challenges, we exploit the socalled angle reciprocity of multipath components in the uplink and downlink, so that the required CSI acquisition overhead scales only with the number of served users, and not the number of AP antennas nor APs. We propose a low complexity multipath component estimation technique and present linear angle-ofarrival (AoA)-based beamforming/combining schemes for FDDbased cell-free massive MIMO systems. We analyze the performance of these schemes by deriving closed-form expressions for the mean-square-error of the estimated multipath components, as well as expressions for the uplink and downlink spectral efficiency. Using semi-definite programming, we solve a maxmin power allocation problem that maximizes the minimum user rate under per-user power constraints. Furthermore, we present a user-centric (UC) AP selection scheme in which each user chooses a subset of APs to improve the overall energy efficiency of the system. Simulation results demonstrate that the proposed multipath component estimation technique outperforms conventional subspace-based and gradient-descent based techniques. We also show that the proposed beamforming and combining techniques along with the proposed power control scheme substantially enhance the spectral and energy efficiencies with an adequate number of antennas at the APs.Index Terms-FDD mode, cell-free massive MIMO, multipath component estimation, array signal processing, angle-based beamforming/combining, power control.

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Section: Performance Analysismentioning

confidence: 93%

“…Moreover, in [19], the non-convex problem of power allocation for downlink global energy efficiency maximization is addressed. In [20], an uplink TDD-based cell-free massive MIMO system is considered. Geometric programming GP is used to sub-optimally solve a quasi-linear max-min signal-to-interference-and-noise ratio (SINR) problem.…”

Section: A Related Workmentioning

confidence: 99%

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Abdallah

Mansour

2020

IEEE Trans. Commun.

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“…Instead, we utilize the so-called use-and-then-forget bound that is widely used in Massive MIMO [2,Th. 4.4], and also in [7], [30], [31] for Cell-Free Massive MIMO with D i = I M ∀i and specific combining vectors.…”

Section: B Uplink Data Transmissionmentioning

confidence: 99%

Scalable Cell-Free Massive MIMO Systems

Björnson

Sanguinetti

2020

IEEE Trans. Commun.

574

583

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Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide algorithms for initial access, pilot assignment, cluster formation, precoding, and combining that are proved to be scalable. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio processing and also performs closely to the best unscalable alternatives. Index TermsCell-Free Massive MIMO, scalable implementation, centralized and distributed algorithms, dynamic cooperation clustering, user-centric networking. 2 impractical, assumptions that lead to immense fronthaul signaling for CSI and data sharing, respectively, as well as huge computational complexity. Fortunately, [9] proved that Network MIMO can operate without CSI sharing, by sacrificing the ability for the APs to jointly cancel interference. Moreover, to limit data sharing and computational complexity, each UE can be served only by an AP subset [10]. Initially, a network-centric approach was taken by dividing the APs into non-overlapping (disjoint) cooperation clusters in which the APs are sharing data (and potentially CSI) to serve only the UEs residing in the joint coverage area [11]-[13]. This approach was considered in 4G but provides small practical gains [14]. One key reason is that many UEs will be located at the edges of the clusters and, thus, will observe substantial intercluster interference from the neighboring clusters [15].The alternative is to take a user-centric approach where each UE is served by the AP subset providing the best channel conditions. Since these subsets are generally different for every UE, it is not possible to divide the network into non-overlapping cooperation clusters. Instead, each AP needs to cooperate with different APs when serving different UEs, over the same time and frequency resource [16]-[18]. 1 A general user-centric cooperation framework was proposed in [17] under the name dynamic cooperation clustering (DCC) and was further described and analyzed in the textbook [10]. The word dynamic refers to the adaptation to time-variant characteristics such as channel properties and UE locations (to name a few). The practical feasibility of DCCs was experimentally verified by the pCell technology [21], but the combination of Network MIMO and DCC didn't gain much interest at the time it was prop...

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“…Since this problem is non-convex, we use alternating optimization to develop an algorithm that solves two sub-problems and update the variables iteratively [11]. Let a (n−1) k , η (n−1) k denote the values of the optimization variables in the iteration n−1.…”

Section: Baseline Heuristic Schemesmentioning

confidence: 99%

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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On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems

Cited by 168 publications

References 36 publications

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Scalable Cell-Free Massive MIMO Systems

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

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