On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers

Bashar, Manijeh; Ngo, Hien Quoc; Burr, Alister G.; Maryopi, Dick; Cumanan, Kanapathippillai; Larsson, Erik G.

doi:10.1109/acssc.2018.8645433

Cited by 41 publications

(43 citation statements)

References 10 publications

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“…Note that the terms |DS k | 2 , E |BU k | 2 , and E |IUI kk ′| 2 are derived in (50), (51) and (56), respectively. Finally substituting (18), (50), (51) and (56) into (14) results in (15), which completes the proof of Theorem 1. Case 2.…”

Section: Theorem 1 Having the Quantized Csi And The Quantized Signalsupporting

confidence: 56%

“…. (15) tight achievable rate which enables us to further design the systems. The tightness of this bound for cell-free Massive MIMO is presented in [2].…”

Section: Performance Analysismentioning

confidence: 99%

“…However, note that the work in [14] does not present any iterative algorithm to jointly solve power minimization problem and LSFD postcoding vector design. In [15], the authors use a bisection search approach to solve the power allocation problem. Next, MMSE receiver is exploited to determine the LSFD postcoding vectors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Bashar

Cumanan

Burr

et al. 2019

IEEE Trans. Commun.

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119

146

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Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.

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Section: Theorem 1 Having the Quantized Csi And The Quantized Signalsupporting

confidence: 56%

“…. (15) tight achievable rate which enables us to further design the systems. The tightness of this bound for cell-free Massive MIMO is presented in [2].…”

Section: Performance Analysismentioning

confidence: 99%

See 1 more Smart Citation

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Bashar

Cumanan

Burr

et al. 2019

IEEE Trans. Commun.

Self Cite

119

146

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show abstract

“…where the instantaneous effective signal-to-interference-and-noise ratio (SINR) is given by The SE expression in (17) holds for any receive combining vector v k , spatial correlation matrices, and selection of the DCCs, and is thus a generalization of [7], [30], [48], [49]. The expression can be easily computed for any v k by using Monte Carlo methods, as done in…”

Section: B Uplink Data Transmissionmentioning

confidence: 99%

“…To show this, we notice that the K UL SINR conditions can be expressed in a similar form where Σ is replaced by Σ T such that p = (Γ − Σ T ) −1 1 K σ 2 ul . Since the eigenvalues of Γ − Σ and Γ − Σ T are the same and the UL SINR conditions are satisfied by assumption, we can always select the DL powers according to (49). Substituting 1 K = 1…”

Section: Appendixmentioning

confidence: 99%

Scalable Cell-Free Massive MIMO Systems

Björnson

Sanguinetti

2020

IEEE Trans. Commun.

623

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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A Novel Combining Method for NOMA and OMA in Cell-Free Massive MIMO System

Sui

Zhang

2022

Wireless and Satellite Systems

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On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers

Cited by 41 publications

References 10 publications

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Scalable Cell-Free Massive MIMO Systems

A Novel Combining Method for NOMA and OMA in Cell-Free Massive MIMO System

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