A Full-Space Spectrum-Sharing Strategy for Massive MIMO Cognitive Radio Systems

Xie, Huimin; Wang, Bolei; Gao, Feifei; Jin, Shi

doi:10.1109/jsac.2016.2605238

Cited by 172 publications

(67 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, the advantage of ADMA is drawn from the frequency insensitivity of the angular parameters, i.e., even if the frequency changes (uplink and downlink in FDD systems), the multiusers access designed from ADMA is still applicable. Moreover, the ADMA would link the users' transmission with their individual locations such that many geometric theories could be utilized to design the multi-user scheduling, even for multiple BS stations or the cell-free case [48]. An example that adopts two BS and geometric theory to eliminate the blockage problem is shown in Fig.…”

Section: A Angle Division Multiple Access (Adma)mentioning

confidence: 99%

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Wang

Gao

Jin

et al. 2019

IEEE J. Sel. Top. Signal Process.

Self Cite

140

View full text Add to dashboard Cite

In the past ten years, there have been tremendous research progresses on massive MIMO systems, most of which stand from the communications viewpoint. A new trend of investigating massive MIMO, especially for the sparse scenario like millimeter wave (mmWave) transmission, is to re-build the transceiver design from array signal processing viewpoint that could deeply exploit the half-wavelength array and provide enhanced performances in many aspects. For example, the high dimensional channel could be decomposed into small amount of physical parameters, e.g., angle of arrival (AoA), angle of departure (AoD), multi-path delay, Doppler shift, etc. As a consequence, transceiver techniques like synchronization, channel estimation, beamforming, precoding, multi-user access, etc., can be re-shaped with these physical parameters, as opposed to those designed directly with channel state information (CSI). Interestingly, parameters like AoA/AoD and multi-path delay are frequency insensitive and thus can be used to guide the downlink transmission from uplink training even for FDD systems. Moreover, some phenomena of massive MIMO that were vaguely revealed previously can be better explained now with array signal processing, e.g., the beam squint effect. In all, the target of this paper is to present an overview of recent progress on merging array signal processing into massive MIMO communications as well as its promising future directions.Index Terms-massive MIMO, array signal processing, mmWave transmission, angle-delay-Doppler reciprocity, beam squint.

show abstract

Section: A Angle Division Multiple Access (Adma)mentioning

confidence: 99%

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Wang

Gao

Jin

et al. 2019

IEEE J. Sel. Top. Signal Process.

Self Cite

140

View full text Add to dashboard Cite

show abstract

“…For ease of derivation, we assume that the channel at each delay shares the same uniform incident AoA set as in [24], i.e., the AoA associated to the pth subpath of the lth delay is θ l,p = 2π p P for p = 1, 2, · · ·, P . By denoting θ p = θ l,p , l = 1, 2, · · ·, L and ϕ p = f d cos θ p + ξ, the received pilot signal (15) can be re-expressed as (16) where h p = L l=1 h (θ l,p ) is an L×1 vector whose lth element is g l,p and α (ε) is simplified as α for conciseness. Irrespective of the inter-subarray mismatches, we still adopt the joint estimation algorithm in Section III-B to estimate the CFO.…”

Section: A Mse Performance Analysis Of the Joint Estimation Algorithmentioning

confidence: 99%

Angle-Domain Approach for Parameter Estimation in High-Mobility OFDM With Fully/Partly Calibrated Massive ULA

Zhang

Gao

et al. 2019

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

In this paper, we consider a downlink orthogonal frequency division multiplexing (OFDM) system from a base station to a high-speed train (HST) equipped with fully/partly calibrated massive uniform linear antenna-array (ULA) in wireless environments with abundant scatterers. Multiple Doppler frequency offsets (DFOs) stemming from intensive propagation paths together with transceiver oscillator frequency offset (OFO) result in a fast time-varying frequency-selective channel. We develop an angle domain carrier frequency offset (CFO, general designation for DFO and OFO) estimation approach.A high-resolution beamforming network is designed to separate different DFOs into a set of parallel branches in angle domain such that each branch is mainly affected by a single dominant DFO. Then, a joint estimation algorithm for both maximum DFO and OFO is developed for fully calibrated ULA.Next, its estimation mean square error (MSE) performance is analyzed under inter-subarray mismatches.To mitigate the detrimental effects of inter-subarray mismatches, we introduce a calibration-oriented beamforming parameter (COBP) and develop the corresponding modified joint estimation algorithm for partly calibrated ULA. Moreover, the Cramér-Rao lower bound of CFO estimation is derived. Both theoretical and numerical results are provided to corroborate the proposed method. Index TermsHigh-mobility OFDM, massive MIMO, partly calibrated antenna array, multiple carrier frequency offsets (CFOs), angle-domain approach, calibration-oriented beamforming parameter (COBP).

show abstract

“…2) Phase I (Steps [6][7][8][9][10][11][12][13][14][15][16]: This phase consists of initially selecting the uncolored clusters ∈ , forming independent sets and assigning them to patterns, till the first cluster that cannot join any independent set formed in any color, the algorithm then proceeds to Phase II. Interestingly, Phase I is an unweightededge graph coloring algorithm, which exploits the common case of sparse graphs scenarios that can be colored with ( ) = 0 without violating the color constraint, in which case all clusters are colored by Phase I algorithm, and Phase II is bypassed.…”

Section: Graph Coloringmentioning

confidence: 99%

Low Overhead Weighted-Graph-Coloring-Based Two-Layer Precoding for FDD Massive MIMO Systems

Anis

Abdelhamid

Elramly

2018

2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

View full text Add to dashboard Cite

A massive multiple-input multiple-output (MIMO) system, operating in Frequency Division Duplexing (FDD) mode of operation, suffers from prohibitively high overhead associated with downlink channel state information (CSI) acquisition and downlink precoding, due to the lack of uplink/downlink channel reciprocity. In this paper, a heuristic edge-weighted vertexcoloring based pattern division (EWVC-PD) scheme is proposed to alleviate the overhead of a two-layer precoding approach, in a practical scenario where the user clusters undergo serious angular-spreading-range (ASR) overlapping. Specifically, under a constraint of limited number of subchannels, an undirected edgeweighted graph (EWG) is firstly constructed, to depict the potential ASR overlapping relationship among clusters. Then, inspired by classical graph coloring algorithms, we develop the EWVC-PD scheme which mitigate the ASR by subchannel orthogonalization between clusters possessing serious ASR overlapping, and multiplexing the ones having slight ASR overlapping. Simulation results reveal that our scheme efficiently outperforms the existing pattern division schemes.Index Terms-ASR overlapping, graph coloring problem, massive MIMO, pattern division, two-layer precoding.

show abstract

A Full-Space Spectrum-Sharing Strategy for Massive MIMO Cognitive Radio Systems

Cited by 172 publications

References 36 publications

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

An Overview of Enhanced Massive MIMO With Array Signal Processing Techniques

Angle-Domain Approach for Parameter Estimation in High-Mobility OFDM With Fully/Partly Calibrated Massive ULA

Low Overhead Weighted-Graph-Coloring-Based Two-Layer Precoding for FDD Massive MIMO Systems

Contact Info

Product

Resources

About