Dr Manijeh Bashar


Research Fellow in Distributed and virtual MIMO and MIMO wireless communications
BSc, MSc, PhD, MIEEE

Biography

My publications

Publications

Bashar Manijeh, Cumanan Kanapathippillai, Burr Alister G., Debbah Merouane, Ngo Hien Quoc (2019) On the Uplink Max-Min SINR of Cell-Free Massive MIMO Systems,IEEE Transactions on Wireless Communications pp. 1-1 IEEE
A cell-free Massive multiple-input multiple-output (MIMO) system is considered using a max-min approach to maximize the minimum user rate with per-user power constraints. First, an approximated uplink user rate is derived based on channel statistics. Then, the original max-min signal-to-interference-plus-noise ratio (SINR) problem is formulated for optimization of receiver filter coefficients at a central processing unit (CPU), and user power allocation. To solve this max-min non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design and power allocation. The receiver filter coefficient design is formulated as a generalized eigenvalue problem whereas geometric programming (GP) is used to solve the user power allocation problem. Based on these two sub-problems, an iterative algorithm is proposed, in which both problems are alternately solved while one of the design variables is fixed. This iterative algorithm obtains a globally optimum solution, whose optimality is proved through establishing an uplink-downlink duality. Moreover, we present a novel sub-optimal scheme which provides a GP formulation to efficiently and globally maximize the minimum uplink user rate. The numerical results demonstrate that the proposed scheme substantially outperforms existing schemes in the literature.
Bashar Manijeh, Cumanan Kanapathippillai, Burr Alister G., Ngo Hien Quoc, Larsson Erik G., Xiao Pei (2019) On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO,Proceedings of the 2019 IEEE International Conference on Communications (ICC): Green Communication Systems and Networks Symposium Institute of Electrical and Electronics Engineers (IEEE)
We investigate the energy efficiency performance of
cell-free Massive multiple-input multiple-output (MIMO), where
the access points (APs) are connected to a central processing
unit (CPU) via limited-capacity links. Thanks to the distributed
maximum ratio combining (MRC) weighting at the APs, we
propose that only the quantized version of the weighted signals
are sent back to the CPU. Considering the effects of channel
estimation errors and using the Bussgang theorem to model the
quantization errors, an energy efficiency maximization problem
is formulated with per-user power and backhaul capacity constraints
as well as with throughput requirement constraints. To
handle this non-convex optimization problem, we decompose the
original problem into two sub-problems and exploit a successive
convex approximation (SCA) to solve original energy efficiency
maximization problem. Numerical results confirm the superiority
of the proposed optimization scheme.
Bashar Manijeh, Cumanan Kanapathippillai, Burr Alister G., Ngo Hien Quoc, Hanzo Lajos, Xiao Pei (2019) NOMA/OMA Mode Selection-Based Cell-Free Massive MIMO,Proceedings of the 2019 IEEE International Conference on Communications (ICC): Wireless Communications Symposium Institute of Electrical and Electronics Engineers (IEEE)
In this paper, non-orthogonal-multiple-access
(NOMA)-based cell-free massive multiple-input multiple-output
(MIMO) is investigated, where the users are grouped into
multiple clusters. Exploiting conjugate beamforming, the
bandwidth efficiency (BE) of the system is derived while
the assumption that the users performing realistic successive
interference cancellation (SIC) based on only the knowledge of
channel statistics. The max-min fairness problem of maximizing
the lowest user BE is investigated and an iterative bisection
method is developed to determine the optimal solution to the
max-min BE problem. Numerical results are presented for
validating the proposed design?s performance, and a mode
switching scheme is conceived for selecting a specific Mode = f
OMA, NOMA g that maximizes the system?s BE.
Bashar Manijeh, Burr Alister G., Haneda Katsuyuki, Cumanan Kanapathippillai, Molu Mehdi M., Khalily Mohsen, Xiao Pei (2019) Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions,IEEE Transactions on Vehicular Technology Institute of Electrical and Electronics Engineers (IEEE)
A low complexity massive multiple-input multipleoutput
(MIMO) technique is studied with a geometry-based
stochastic channel model, called COST 2100 model. We propose
to exploit the discrete-time Fourier transform of the antenna
correlation function to perform user scheduling. The proposed
algorithm relies on a trade off between the number of occupied
bins of the eigenvalue spectrum of the channel covariance matrix
for each user and spectral overlap among the selected users.
We next show that linear precoding design can be performed
based only on the channel correlation matrix. The proposed
scheme exploits the angular bins of the eigenvalue spectrum
of the channel covariance matrix to build up an ?approximate
eigenchannels? for the users. We investigate the reduction of
average system throughput with no channel state information at
the transmitter (CSIT). Analysis and numerical results show that
while the throughput slightly decreases due to the absence of
CSIT, the complexity of the system is reduced significantly.
Bashar Manijeh, Cumanan Kanapathippillai, Burr Alister G, Ngo Hien Quoc, Xiao Pei (2019) Max-Min Rate of Cell-Free Massive MIMO Uplink with Optimal Uniform Quantization,IEEE TRANSACTIONS ON COMMUNICATIONS IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into
two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.
Bashar Manijeh, Cumanan Kanapathippillai, Burr Alister G., Ngo Hien Quoc, Larsson Erik G., Xiao Pei (2019) Energy Efficiency of the Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization,IEEE Transactions on Green Communications and Networking 3 (4) pp. 971-987 Institute of Electrical and Electronics Engineers (IEEE)
A cell-free Massive multiple-input multiple-output (MIMO) uplink is considered, where the access points (APs) are connected to a central processing unit (CPU) through limited-capacity wireless microwave links. The quantized version of the weighted signals are available at the CPU, by exploiting the Bussgang decomposition to model the effect of quantization. A closed-form expression for spectral efficiency is derived taking into account the effects of channel estimation error and quantization distortion. The energy efficiency maximization problem is considered with per-user power, backhaul capacity and throughput requirement constraints. To solve this non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design, and power allocation. The receiver filter coefficient design is formulated as a generalized eigenvalue problem whereas a successive convex approximation (SCA) and a heuristic sub-optimal scheme are exploited to convert the power allocation problem into a standard geometric programming (GP) problem. An iterative algorithm is proposed to alternately solve each sub-problem. Complexity analysis and convergence of the proposed schemes are investigated. Numerical results indicate the superiority of the proposed algorithms over the case of equal power allocation.
Bashar Manijeh, Cumanan Kanapathippillai, Burr Alister G., Ngo Hien Quoc, Hanzo Lajos, Xiao Pei (2019) On the Performance of Cell-Free Massive MIMO Relying on Adaptive NOMA/OMA Mode-Switching,IEEE TRANSACTIONS ON COMMUNICATIONS pp. 1-1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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 per-access 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.
Bashar Manijeh, Akbari Ali, Cumanan Kanapathippillai, Quoc Ngo Hien, Burr Alister G., Xiao Pei, Debbah Merouane, Kittler Josef (2020) Exploiting Deep Learning in Limited-Fronthaul
Cell-Free Massive MIMO Uplink
,
IEEE Journal on Selected Areas in Communications Institute of Electrical and Electronics Engineers
A cell-free massive multiple-input multiple-output
(MIMO) uplink is considered, where quantize-and-forward (QF)
refers to the case where both the channel estimates and the
received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combinequantize-
and-forward (CQF), the APs send the quantized version
of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing largescale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep
learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem
is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.
Bashar Manijeh, Akbari Ali, Cumanan Kanapathippillai, Quoc Ngo Hien, Burr Alister G., Xiao Pei, Debbah Merouane (2020) Deep Learning-Aided Finite-Capacity Fronthaul
Cell-Free Massive MIMO with Zero Forcing
,
IEEE ICC 2020
We consider a cell-free massive multiple-input
multiple-output (MIMO) system where the channel estimates and
the received signals are quantized at the access points (APs)
and forwarded to a central processing unit (CPU). Zero-forcing
technique is used at the CPU to detect the signals transmitted
from all users.. To solve the non-convex sum rate maximization
problem, a heuristic sub-optimal scheme is proposed to convert
the problem into a geometric programme (GP). Exploiting a deep
convolutional neural network (DCNN) allows us to determine
both a mapping from the large-scale fading (LSF) coefficients
and the optimal power by solving the optimization problem
using the quantized channel. Depending on how the optimization
problem is solved, different power control schemes are investigated;
i) small-scale fading (SSF)-based power control; ii) LSF
use-and-then-forget (UatF)-based power control; and iii) LSF
deep learning (DL)-based power control. The SSF-based power
control scheme needs to be solved for each coherence interval
of the SSF, which is practically impossible in real time systems.
Numerical results reveal that the proposed LSF-DL-based scheme
significantly increases the performance compared to the practical
and well-known LSF-UatF-based power control, thanks to the
mapping obtained using DCNN.