Wideband outdoor MIMO channel model derived from directional channel measurements at 2 GHz

This paper describes the use of directional channel measurements to derive a MIMO channel model. The measurements were obtained using a wideband channel sounder and eight element circular array in a metropolitan area in central Bristol, U.K. The raw measurements were processed using SAGE to extract the parameters of multipath components. The analysis of these parameters revealed several interesting features, notably that their amplitude distribution was well modelled as log-normal, and that there was little evidence of clustering in the angles of arrival. Hence a MIMO channel based on the assumption of finite scattering was derived, using the distributions obtained. The model allows the channel matrix H to be derived in the narrow-band case, and a tapped delay line model is also obtained for wideband systems. While the model derived is based on only a small set of measurements, it provides a case study for MIMO modelling based on measurements

Implementation of uplink network-coded modulation for two-hop networks

With the fast growing number of wireless devices and demand of user data, the backhaul load becomes a bottleneck in wireless networks. Physical layer network coding (PNC) allows Access Points (APs) to relay compressed, network coded user data, therefore reducing the backhaul traffic. In this paper, an implementation of uplink Network Coded Modulation (NetCoM) with PNC is presented. A 5-node prototype NetCoM system is established using Universal Software Radio Peripherals (USRPs) and a practical PNC scheme designed for binary systems is utilised. An orthogonal frequency division multiplexing (OFDM) waveform implementation and the practical challenges (e.g. device synchronisation and clock drift) of applying OFDM to NetCoM are discussed. To the best of our knowledge this is the first PNC implementation in an uplink scenario in radio access networks and our prototype provides an industrially-applicable implementation of the proposed NetCoM with PNC approach

On The Uplink Throughput of Zero-Forcing in Cell-Free Massive MIMO with Coarse Quantization

The recently proposed Cell-Free massive MIMO architecture is studied for the uplink. In contrast to most previous works, joint detection is performed using global CSI. Therefore, we study strategies for transferring CSI to the CPU taking into account the fronthaul capacity which limits CSI quantization. Two strategies for pilot-based CSI acquisition are considered: estimate-and-quantize and quantize-and-estimate. These are analysed using the Bussgang decomposition. For a given quantization constraint for the data and CSI the achievable rate per user with Zero-Forcing is determined. Numerical results show that quantize-and-estimate (the simpler strategy) is similar to or better than estimate-and-quantize at low resolution, especially for 1-bit

On the Performance of Cell-Free Massive MIMO Relying on Adaptive NOMA/OMA Mode-Switching

The downlink (DL) of a non-orthogonal-multiple-access (NOMA)-based cell-free massive multiple-input multipleoutput (MIMO) system is analyzed, where the channel state information (CSI) is estimated using pilots. It is assumed that the users are grouped into multiple clusters. The same pilot sequences are assigned to the users within the same clusters whereas the pilots allocated to all clusters are mutually orthogonal. First, a user’s bandwidth efficiency (BE) is derived based on his/her channel statistics under the assumption of employing successive interference cancellation (SIC) at the users’ end with no DL training. Next, the classic max-min optimization framework is invoked for maximizing the minimum BE of a user under peraccess point (AP) power constraints. The max min user BE of NOMA-based cell-free massive MIMO is compared to that of its orthogonal multiple-access (OMA) counter part, where all users employ orthogonal pilots. Finally, our numerical results are presented and an operating mode switching scheme is proposed based on the average per-user BE of the system, where the mode set is given by Mode = { OMA, NOMA }. Our numerical results confirm that the switching point between the NOMA and OMA modes depends both on the length of the channel’s coherence time and on the total number of users