Achieving large multiplexing gain in distributed antenna systems via cooperation with pCell technology

Forenza, A.; Perlman, Stephen M.; Saibi, Fadi; Dio, Mario Di; Laan, Roger van der; Caire, Giuseppe

doi:10.1109/acssc.2015.7421132

Cited by 44 publications

(30 citation statements)

References 29 publications

(45 reference statements)

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“…Massive MIMO has been very intensively studied in the past few years and it is still a very active research topic (e.g., see [1] and references therein). Furthermore, massive MIMO based on TDD reciprocity for the estimation of the downlink (DL) channel vectors from uplink pilot signals has been demonstrated in practice in several academic and industrial prototypes [2]- [4], thus confirming the possibility of obtaining accurate and timely DL channel estimates at the base station side from pilot symbols sent by the users in the uplink direction.…”

Section: Introductionmentioning

confidence: 99%

On the Ergodic Rate Lower Bounds With Applications to Massive MIMO

Caire

2018

IEEE Trans. Wireless Commun.

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111

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A well-known lower bound widely used in the massive MIMO literature hinges on channel hardening, i.e., the phenomenon for which, thanks to the large number of antennas, the effective channel coefficients resulting from beamforming tend to deterministic quantities. If the channel hardening effect does not hold sufficiently well, this bound may be quite far from the actual achievable rate. In recent developments of massive MIMO, several scenarios where channel hardening is not sufficiently pronounced have emerged. These settings include, for example, the case of small scattering angular spread, yielding highly correlated channel vectors, and the case of cell-free massive MIMO. In this short contribution, we present two new bounds on the achievable ergodic rate that offer a complementary behavior with respect to the classical bound: while the former performs well in the case of channel hardening and/or when the system is interference-limited (notably, in the case of finite number of antennas and conjugate beamforming transmission), the new bounds perform well when the useful signal coefficient does not harden but the channel coherence block length is large with respect to the number of users, and in the case where interference is nearly entirely eliminated by zero-forcing beamforming. Overall, using the most appropriate bound depending on the system operating conditions yields a better understanding of the actual performance of systems where channel hardening may not occur, even in the presence of a very large number of antennas. Index TermsMassive MIMO, achievable ergodic rate, information theoretic bounds.

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Section: Introductionmentioning

confidence: 99%

On the Ergodic Rate Lower Bounds With Applications to Massive MIMO

Caire

2018

IEEE Trans. Wireless Commun.

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111

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“…Network MIMO helps to avoid rank deficient and poorly conditioned channel matrices which are caused by spatial correlations or by the "keyhole" effect [25]. Network MIMO is sometimes called "distributed MIMO", "MIMO cooperation", "coherently coordinated transmission", "Joint Processing CoMP", "Joint Transmission CoMP", "C-RAN (Cloud-RAN)" or "p-cell" [26]. Figure 1 shows the layout of the indoor office scenario defined as "A1 -Indoor Office" in the WINNER II deliverable D.1.1.2 [15].…”

Section: Network Mimomentioning

confidence: 99%

Information rates of precoding for massive MIMO and base station cooperation in an indoor scenario

Kramer

Panzner

Zirwas

2020

J Wireless Com Network

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The performance of centralized and distributed massive MIMO deployments are analyzed for indoor office scenarios. The distributed deployments use one of the following precoding methods: (1) local precoding with local channel state information (CSI) to the user equipments (UEs) that it serves; (2) large-scale MIMO with local CSI to all UEs in the network; (3) network MIMO with global CSI. For the distributed deployments (2) and (3), it is shown that using twice as many base station antennas as data streams provides many of the massive MIMO benefits in terms of spectral efficiency and fairness. This is in contrast to the centralized deployment and the distributed deployment (1) where more antennas are needed. Two of the main conclusions are that distributing base stations helps to overcome wall penetration loss; however, a backhaul is required to mitigate inter-cell interference. The effect of estimation errors on the performance is also quantified.

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“…The state vector consists of the phase offset φ and normalized CFO F between the master and slave. Assuming the channel variation h 0 is compensated, 5 The normalized CFO is up to F ≈ 0.075 rad based on the maximum USRP frequency offset of ±5ppm, when using a 2.4GHz carrier frequency, and a 1MHz sampling rate. The complex envelop due to CFO has its phase rotating with values larger than 2π, which severely degrades the correlation peak if we used a long ZC sequence as compared to multiple repetitions of a shorter sequence.…”

Section: Cfo Estimation and Compensationmentioning

confidence: 99%

“…Distributed multiple-input multiple-output (DMIMO) is another application of distributed arrays which relies on the increased virtual array dimension to provide the required degrees-of-freedom for spatial multiplexing and interference control. This technique facilitates significant spectral efficiency improvement [5], [6]. A distributed array can also synthesize a larger virtual aperture and provide orders-ofmagnitude higher precision in localization [7] and radar imaging [8] applications.…”

Section: Introductionmentioning

confidence: 99%

“…Synchronization among radios is the key requirement for distributed beamforming, MIMO, localization and imaging, since carrier frequency offset/drift, and sample timing offset introduce non-coherence and severely degrades performance. Synchronization schemes in most recent prototype studies [2,3,5,8] mostly rely on dedicated cables, e.g., copper and optical fiber, or GPS clocks. The over-the-wire reference signals, e.g., 10 MHz and pulse-per-second (PPS) signals, greatly simplify the synchronization, but they are not suitable for mobile systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Software Defined Radio Implementation of Carrier and Timing Synchronization for Distributed Arrays

Han

Hanna

Balke

et al. 2019

2019 IEEE Aerospace Conference

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The communication range of wireless networks can be greatly improved by using distributed beamforming from a set of independent radio nodes. One of the key challenges in establishing a beamformed communication link from separate radios is achieving carrier frequency and sample timing synchronization. This paper describes an implementation that addresses both carrier frequency and sample timing synchronization simultaneously using RF signaling between designated master and slave nodes. By using a pilot signal transmitted by the master node, each slave estimates and tracks the frequency and timing offset and digitally compensates for them. A realtime implementation of the proposed system was developed in GNU Radio and tested with Ettus USRP N210 software defined radios. The measurements show that the distributed array can reach a residual frequency error of 5 Hz and a residual timing offset of 1/16 the sample duration for 70 percent of the time. This performance enables distributed beamforming for range extension applications.

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Achieving large multiplexing gain in distributed antenna systems via cooperation with pCell technology

Cited by 44 publications

References 29 publications

On the Ergodic Rate Lower Bounds With Applications to Massive MIMO

On the Ergodic Rate Lower Bounds With Applications to Massive MIMO

Information rates of precoding for massive MIMO and base station cooperation in an indoor scenario

Software Defined Radio Implementation of Carrier and Timing Synchronization for Distributed Arrays

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