Efficient large scale antenna selection by partial switching connectivity

García‐Rodríguez, Adrián; Masouros, Christos; Rulikowski, Pawel

doi:10.1109/icassp.2017.7953362

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…Switching architectures and capacity bounds are also discussed for i.i.d. channels in Gao et al (2018) and practical implementation issues such as insertion losses caused by switching architectures are discussed in Garcia-Rodriguez et al (2017). Also, since S < M, a substantial gain in power consumption with respect to the full M-element system is possible.…”

Section: Antenna Switching Architecturementioning

confidence: 99%

Evaluation of an Antenna Selection Strategy for Reduced Massive MIMO Complexity

et al. 2021

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Mobile connectivity has become not only essential but a necessity for many network users. The number of mobile subscribers is growing rapidly and the demand for more bandwidth and higher data rates continues to increase (CISCO Global Cloud Index (GCI), 2018). To support this demand, 5G systems promise to deliver a major paradigm shift with respect to previous wireless communication systems using new technologies such as Massive MIMO (Andrews et al., 2014;Larsson et al., 2014). MIMO technology has been a breakthrough for wireless communication systems in terms of increased reliability and higher data throughput and it is imagined that more benefits could be harvested from scaled-up versions (Rusek et al., 2013).In Marzetta (2010), the author introduced the concept of Massive MIMO and demonstrated that an excessive number of radiating elements (M) at the transmitting side paired with a number of active users (K) permits the use of simple linear precoders and increases the overall spectrum efficiency. In Björnson et al. (2016), Lu et al. (2014), Ngo et al. (2013), Rusek et al. (2013, an overview of Massive MIMO technology is thoroughly presented alongside its main advantages and challenges. Massive MIMO systems can eliminate the effects of small-scale fading and uncorrelated noise thus reducing intercell interference. As a result, the corresponding channel is formed by a set of nearly perfect orthogonal subchannels (Narasimhan & Chockalingam, 2014). Even though MIMO systems are already operating worldwide, largescale MIMO has only been tested in labs (

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Section: Antenna Switching Architecturementioning

confidence: 99%

Evaluation of an Antenna Selection Strategy for Reduced Massive MIMO Complexity

et al. 2021

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“…Our previous work in [30], [31] inspected the common deterministic sparse array design for multiple switched beams in both cases of fully and partially-connected radio frequency (RF) switch network. Fully-connected switch network can facilitate the inter-connection of any input port to all output ports [32]. While partially-connected switch network constrains regularized antenna switching, which divides the full array into contiguous groups and precisely one antenna is selected in each group to compose the sparse array.…”

Section: B Relevant Work On Sparse Array Designmentioning

confidence: 99%

Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching

Wang,

Greco,

Gini

2021

Preprint

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In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), to swiftly adapt both array configuration and excitation weights in accordance to the dynamic environment for enhancing interference suppression. In order to achieve an implementable design of array reconfiguration, the RCAS is conducted in the framework of regularized antenna switching, whereby the full array aperture is collectively divided into separate groups and only one antenna in each group is switched on to connect with the processing channel. A set of deterministic complementary sparse arrays with good quiescent beampatterns is first designed by RCAS and full array data is collected by switching among them while maintaining resilient interference suppression. Subsequently, adaptive sparse array tailored for the specific environment is calculated and reconfigured based on the information extracted from the full array data. The RCAS is devised as an exclusive cardinalityconstrained optimization, which is reformulated by introducing an auxiliary variable combined with a piece-wise linear function to approximate the l0-norm function. A regularization formulation is proposed to solve the problem iteratively and eliminate the requirement of feasible initial search point. A rigorous theoretical analysis is conducted, which proves that the proposed algorithm is essentially an equivalent transformation of the original cardinality-constrained optimization. Simulation results validate the effectiveness of the proposed RCAS strategy.

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