Performance Analysis of Centralized and Partially Decentralized Co-Operative Networks

Senanayake, Rajitha; Yeoh, Phee Lep; Evans, Jamie

doi:10.1109/tcomm.2015.2512918

Cited by 6 publications

(7 citation statements)

References 31 publications

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“…However, in a distributed system, interference among users is a major barrier to meet the required data rate. Thus, some degree of centralized processing, which coordinates the received signals from the distributed antennas [33], is required to either maximize the desired signal power or reduce the inter-user interference. The processing is done at a central node which has access to the signals received at all distributed antennas [22,33].…”

Section: Distributed Hybrid Mrc In Two Stage Mu-mimo Systemsmentioning

confidence: 99%

“…Thus, some degree of centralized processing, which coordinates the received signals from the distributed antennas [33], is required to either maximize the desired signal power or reduce the inter-user interference. The processing is done at a central node which has access to the signals received at all distributed antennas [22,33]. The importance and limitations of coordinated antennas are further discussed in [14,22] and references within.…”

Section: Distributed Hybrid Mrc In Two Stage Mu-mimo Systemsmentioning

confidence: 99%

“…The importance and limitations of coordinated antennas are further discussed in [14,22] and references within. In fully cooperative distributed systems, where all the received signals are sent from the antenna clusters to a central processor, attractive performance gains are achieved compared to the equivalent non-cooperative network [22,33]. In [13], full cooperation is evaluated in the downlink with dirty paper coding and zero forcing (ZF).…”

Section: Distributed Hybrid Mrc In Two Stage Mu-mimo Systemsmentioning

confidence: 99%

“…Hence, maximum ratio combining (MRC) with user grouping was proposed in [22]. To relieve the need for centralized knowledge of all the signals, twostage processing is proposed in [33]. Here, only the output signals after local (first stage) processing are sent to the (second stage) central processing node.…”

Section: Distributed Hybrid Mrc In Two Stage Mu-mimo Systemsmentioning

confidence: 99%

“…Distributed systems (dividing the antennas into multiple antenna clusters) provide the benefits of reduced spatial correlation [22] and greater coverage than conventional co-located systems [13]. In fully cooperative distributed systems, where all the received signals are sent from the antenna clusters to a central processor, attractive performance gains are achieved compared to the equivalent non-cooperative network [22,33]. In [13], full cooperation is evaluated in the downlink with dirty paper coding and ZF.…”

Section: Introductionmentioning

confidence: 99%

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Matched Filtering in Massive MU-MIMO Systems

Li¹

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<p>This thesis considers the analysis of matched filtering (MF) processing in massive multi-user multiple-input-multiple-output (MU-MIMO) wireless communication systems. The main focus is the analysis of system performance for combinations of two linear processers, analog maximum ratio combining (MRC) and digital MRC. We consider implementations of these processing techniques both at a single base-station (BS) and in distributed BS layouts. We further consider extremely low complexity distributed variants of MRC for such systems. Since MRC relies on the massive MIMO properties of favourable propagation (FP) and channel hardening, we also present a detailed analysis of FP and channel hardening. This analysis employs modern ray-based models rather than classical channel models as the models are more reliable for the large arrays and higher frequencies envisaged for future systems. The importance of MRC processing is being driven by the emergence of massive MIMO and millimetre wave as strong candidates for next generation wireless communication systems. Massive MIMO explores the spatial dimension by providing significant increases in data rate, link reliability and energy efficiency. However, with a large number of antennas co-located in a fixed physical space, correlation between the elements of antennas may have a negative impact. Distributed systems, where the total number of antennas are divided into different locations, make this problem less serious. Also, linear processing techniques, analog MRC and digital MRC, due to their simplicity and efficiency, are more practical in massive MU-MIMO systems. For these reasons we consider MRC processing in both co-located and distributed scenarios. Although distributed systems reduce the adverse impact of correlation caused by closely-spaced large antenna arrays by dividing the antennas into multiple antenna clusters, the correlation within the cluster still exists. Thus, we extend MRC analysis for massive MIMO to correlated channels. Approximations of expected per-user spectrum efficiency (SE) with correlation effects for massive MIMO systems with analog MRC and digital MRC are derived. Useful insights are given for future system deployments. A convergence analysis of the interference behaviour under different correlation models is presented. Furthermore, a distributed fully cooperative system, where all the received signals are sent to the central processor, offers attractive performance gains but at the cost of high computational complexity at the central node. Thus, we propose four low-complexity, two-stage processors, where only processed signals after local processing (first-stage) are transmitted to the global processing node (second-stage). We present analytical expressions for the expected per user SINR in an uplink distributed MU-MIMO system with two-stage beam-forming. This leads to an approximation of expected per-user SE. The analysis of both millimetre wave and massive MIMO systems requires a strong link to the physical environment and ray-based models are more practical and suitable for such systems. However, it is unclear how the key properties in conventional MIMO systems, such as FP and channel hardening, will behave in a ray-based channel model. In this thesis, remarkably simple and general results are obtained demonstrating that: a) channel hardening may or may nor occur depending on the nature of the channel models; b) FP is guaranteed for all models as long as the ray angles are continuous random variables; c) we also propose a novel system metric, denoted large system potential (LSP) as the ratio of the mean desired signal power to the total mean interference power, where both the numbers of antennas and end-users are growing to infinity at a fixed ratio. We derive simple approximations to LSP and demonstrate that LSP will not normally hold as the mean interference power usually grows logarithmically relative to the mean signal power.</p>

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Section: Distributed Hybrid Mrc In Two Stage Mu-mimo Systemsmentioning

confidence: 99%