arXiv subjects

Tadilo Endeshaw Bogale

Publications and source records attributed to Tadilo Endeshaw Bogale.

At least 19 recordsLinked to original sources

arXiv2018

Joint CSI Estimation, Beamforming and Scheduling Design for Wideband Massive MIMO System

This paper proposes a novel approach for designing channel estimation, beamforming and scheduling jointly for wideband massive multiple input multiple output (MIMO) systems. With the proposed approach, we first quantify the maximum number of user equipments (UEs) that can send pilots which may or may not be orthogonal. Specifically, when the channel has a maximum of $L$ multipath taps, and we allocate $\tilde{M}$ sub-carriers for the channel state information (CSI) estimation, a maximum of $\tilde{M}$ UEs CSI can be estimated ($L$ times compared to the conventional CSI estimation approach) in a massive MIMO regime. Then, we propose to schedule a subset of these UEs using greedy based scheduling to transmit their data on each sub-carrier with the proposed joint beamforming and scheduling design. We employ the well known maximum ratio combiner (MRC) beamforming approach in the uplink channel data transmission. All the analytical expressions are validated via numerical results, and the superiority of the proposed design over the conventional orthogonal frequency division multiplexing (OFDM) transmission approach is demonstrated using extensive numerical simulations in the long term evolution (LTE) channel environment. The proposed channel estimation and beamforming design is linear and simple to implement.

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arXiv2018

Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks

The next generation wireless networks (i.e. 5G and beyond), which would be extremely dynamic and complex due to the ultra-dense deployment of heterogeneous networks (HetNets), poses many critical challenges for network planning, operation, management and troubleshooting. At the same time, generation and consumption of wireless data are becoming increasingly distributed with ongoing paradigm shift from people-centric to machine-oriented communications, making the operation of future wireless networks even more complex. In mitigating the complexity of future network operation, new approaches of intelligently utilizing distributed computational resources with improved context-awareness becomes extremely important. In this regard, the emerging fog (edge) computing architecture aiming to distribute computing, storage, control, communication, and networking functions closer to end users, have a great potential for enabling efficient operation of future wireless networks. These promising architectures make the adoption of artificial intelligence (AI) principles which incorporate learning, reasoning and decision-making mechanism, as natural choices for designing a tightly integrated network. Towards this end, this article provides a comprehensive survey on the utilization of AI integrating machine learning, data analytics and natural language processing (NLP) techniques for enhancing the efficiency of wireless network operation. In particular, we provide comprehensive discussion on the utilization of these techniques for efficient data acquisition, knowledge discovery, network planning, operation and management of the next generation wireless networks. A brief case study utilizing the AI techniques for this network has also been provided.

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arXiv2017

Pilot Contamination for Wideband Massive MMO: Number of cells Vs Multipath

This paper proposes a novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. With the proposed channel estimation and beamforming approach, we determine the number of cells $N_c$ that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has a maximum of $L$ multipath taps, \textcolor{red}{it is shown that $N_c\leq L$ cells can estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination.} In a typical long term evolution (LTE) channel environment having delay spread $T_d=4.69μ$ second and channel bandwidth $B=2.5$MHz, we have found that $L=18$ cells can use this band. In practice, $T_d$ is constant for a particular environment and carrier frequency, and hence $L$ increases as the bandwidth increases. All the analytical expressions have been validated, and the superiority of the proposed design over the existing ones is demonstrated using extensive numerical simulations both for correlated and uncorrelated channels. The proposed channel estimation and beamforming design is linear and simple to implement.

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arXiv2017

Multipath Multiplexing for Capacity Enhancement in SIMO Wireless Systems

This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN) data transmission and detection approach for a single input multiple output (SIMO) system. It is assumed that the signal having a bandwidth $B$ is transmitted through a wireless channel with $L$ multipath components. Under this assumption, the current paper provides a novel and simple OFTN transmission and symbol-by-symbol detection approach that exploits the multiplexing gain obtained by the multipath characteristic of wideband wireless channels. It is shown that the proposed design can achieve a higher transmission rate than the existing one (i.e., orthogonal frequency division multiplexing (OFDM)). Furthermore, the achievable rate gap between the proposed approach and that of the OFDM increases as the number of receiver antennas increases for a fixed value of $L$. This implies that the performance gain of the proposed approach can be very significant for a large-scale multi-antenna wireless system. The superiority of the proposed approach is shown theoretically and confirmed via numerical simulations. {Specifically, we have found {upper-bound average} rates of 15 bps/Hz and 28 bps/Hz with the OFDM and proposed approaches, respectively, in a Rayleigh fading channel with 32 receive antennas and signal to noise ratio (SNR) of 15.3 dB. The extension of the proposed approach for different system setups and associated research problems is also discussed.

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arXiv2017

Adaptive Channel Prediction, Beamforming and Scheduling Design for 5G V2I Network

One of the important use-cases of 5G network is the vehicle to infrastructure (V2I) communication which requires accurate understanding about its dynamic propagation environment. As 5G base stations (BSs) tend to have multiple antennas, they will likely employ beamforming to steer their radiation pattern to the desired vehicle equipment (VE). Furthermore, since most wireless standards employ an OFDM system, each VE may use one or more sub-carriers. To this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without the training signal and channel impulse response (CIR) model. In this regard, first we utilize the well known adaptive recursive least squares (RLS) technique for predicting the next block CIR from the past and current block received signals (a block may have one or more OFDM symbols). Then, we jointly design the beamforming and VE scheduling for each sub-carrier to maximize the uplink channel average sum rate by utilizing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution is guaranteed. And, the VE scheduling design is formulated as an integer programming problem which is solved by employing a greedy search. The superiority of the proposed channel prediction and scheduling algorithms over those of the existing ones is demonstrated via numerical simulations.

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arXiv2016

On the Number of RF Chains and Phase Shifters, and Scheduling Design with Hybrid Analog-Digital Beamforming

This paper considers hybrid beamforming (HB) for downlink multiuser massive multiple input multiple output (MIMO) systems with frequency selective channels. For this system, first we determine the required number of radio frequency (RF) chains and phase shifters (PSs) such that the proposed HB achieves the same performance as that of the digital beamforming (DB) which utilizes $N$ (number of transmitter antennas) RF chains. We show that the performance of the DB can be achieved with our HB just by utilizing $r_t$ RF chains and $2r_t(N-r_t + 1)$ PSs, where $r_t \leq N$ is the rank of the combined digital precoder matrices of all sub-carriers. Second, we provide a simple and novel approach to reduce the number of PSs with only a negligible performance degradation. Numerical results reveal that only $20-40$ PSs per RF chain are sufficient for practically relevant parameter settings. Finally, for the scenario where the deployed number of RF chains $(N_a)$ is less than $r_t$, we propose a simple user scheduling algorithm to select the best set of users in each sub-carrier. Simulation results validate theoretical expressions, and demonstrate the superiority of the proposed HB design over the existing HB designs in both flat fading and frequency selective channels.

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arXiv2015

Hybrid Analog-Digital Beamforming for Multiuser MIMO Millimeter Wave Relay Systems

This paper proposed new hybrid, analog-digital, beamforming for a multiuser millimeter wave (mm-wave) relay system. For this system, we consider a sum rate maximization problem. The proposed hybrid beamforming is designed indirectly by considering a sum mean square error (MSE) minimization problem while utilizing the solution of digital beamforming. To this end, we assume that the digital beamforming utilizes the well known block diagonalization (BD) approach. Under this assumption, we solve our problem as follows: First, we formulate the sum rate maximization problem as the minimization of the MSE between the received signal of the hybrid and digital beamforming designs. Then, we design the hybrid beamformings of the source, relay and each destination by leveraging compressive sensing techniques. Simulation results confirm that the proposed hybrid beamforming design achieves performance very close to that of the digital one. Furthermore, we have examined the effects of the number of radio frequency (RF) chains and paths together, and the accuracy of angle of arrival (AoA) and angle of departure (AoD) estimators on the sum rate of the hybrid beamforming designs. Computer simulations reveal that the total sum rate of the hybrid beamforming increases when the number of RF chains and paths increase (or the accuracy of the AoA (AoD) estimator improves).

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arXiv2015

Massive MIMO and Millimeter Wave for 5G Wireless HetNet: Potentials and Challenges

There have been active research activities worldwide in developing the next-generation 5G wireless network. The 5G network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections, achieve better cost- and energy-efficiency as well as quality of service (QoS) in terms of communication delay, reliability and security. To this end, the 5G wireless network should exploit potential gains in different network dimensions including super dense and heterogeneous deployment of cells and massive antenna arrays (i.e., massive multiple input multiple output (MIMO) technologies) and utilization of higher frequencies, in particular millimeter wave (mmWave) frequencies. This article discusses potentials and challenges of the 5G heterogeneous wireless network (HetNet) which incorporates massive MIMO and mmWave technologies. We will first provide the typical requirements of the 5G wireless network. Then, the significance of massive MIMO and mmWave in engineering the future 5G HetNet is discussed in detail. Potential challenges associated with the design of such 5G HetNet are discussed. Finally, we provide some case studies, which illustrate the potential benefits of the considered technologies.

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arXiv2015

Hybrid Analog-Digital Channel Estimation and Beamforming: Training-Throughput Tradeoff

This paper designs hybrid analog-digital channel estimation and beamforming techniques for multiuser massive multiple input multiple output (MIMO) systems with limited number of radio frequency (RF) chains. For these systems, first we design novel minimum mean square error (MMSE) hybrid analog-digital channel estimator by considering both perfect and imperfect channel covariance matrix knowledge cases. Then, we utilize the estimated channels to enable beamforming for data transmission. When the channel covariance matrices of all user equipments (UEs) are known perfectly, we show that there is a tradeoff between the training duration and throughput. Specifically, we exploit that the optimal training duration that maximizes the throughput depends on the covariance matrices of all UEs, number of RF chains and channel coherence time ($T_c$). We also show that the training time optimization problem can be formulated as a concave maximization problem {for some system parameter settings} where its global optimal solution is obtained efficiently using existing tools. In particular, when the base station equipped with $64$ antennas and $1$ RF chain is serving one single antenna UE, $T_c=128$ symbol periods ($T_s$) and signal to noise ratio of $10$dB, we have found that the optimal training durations are $4T_s$ and $20T_s$ for highly correlated and uncorrelated Rayleigh fading channel coefficients, respectively. The analytical expressions are validated by performing numerical and extensive Monte Carlo simulations.

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arXiv2015

Pilot Contamination Mitigation for Wideband Massive MMO: Number of Cells Vs Multipath

This paper proposes novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. Using our channel estimation and beamforming approach, we determine the number of cells $N_c$ that can utilize the same time and frequency resource while mitigating the effect of pilot contamination. The proposed approach exploits the multipath characteristics of wideband channels. Specifically, when the channel has $L$ multipath taps, it is shown that $N_c\leq L$ cells can reliably estimate the channels of their user equipments (UEs) and perform beamforming while mitigating the effect of pilot contamination. For example, in a long term evolution (LTE) channel environment having delay spread $T_d=4.69μ$ second and channel bandwidth $B=2.5$MHz, we have found that $L=18$ cells can use this band. In practice, $T_d$ is constant for a particular environment and carrier frequency, and hence $L$ increases as the bandwidth increases. The proposed channel estimation and beamforming design is linear, simple to implement and significantly outperforms the existing designs, and is validated by extensive simulations.

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arXiv2014

Wideband Sensing and Optimization for Cognitive Radio Networks with Noise Variance Uncertainty

This paper considers wide-band spectrum sensing and optimization for cognitive radio (CR) networks with noise variance uncertainty. It is assumed that the considered wide-band contains one or more white sub-bands. Under this assumption, we consider throughput maximization of the CR network while appropriately protecting the primary network. We address this problem as follows. First, we propose novel ratio based test statistics for detecting the edges of each sub-band. Second, we employ simple energy comparison approach to choose one reference white sub-band. Third, we propose novel generalized energy detector (GED) for examining each of the remaining sub-bands by exploiting the noise information of the reference white sub-band. Finally, we optimize the sensing time ($T_o$) to maximize the CR network throughput using the detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to noise ratio (SNR) wall and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED) is quantified analytically. We show that the optimal $T_o$ depends on the noise variance information. In particular, with $10$TV bands, SNR=$-20$dB and $2$s frame duration, we found that the optimal $T_o$ is $28.5$ms ($50.6$ms) with perfect (imperfect) noise variance scenario.

stat.AP↗

arXiv2014

Beamforming for Multiuser Massive MIMO Systems: Digital versus Hybrid Analog-Digital

This paper designs a novel hybrid (a mixture of analog and digital) beamforming and examines the relation between the hybrid and digital beamformings for downlink multiuser massive multiple input multiple output (MIMO) systems. We assume that perfect channel state information is available only at the transmitter and we consider the total sum rate maximization problem. For this problem, the hybrid beamforming is designed indirectly by considering a weighed sum mean square error (WSMSE) minimization problem incorporating the solution of digital beamforming which is obtained from the block diagonalization technique. The resulting WSMSE problem is solved by applying the theory of compressed sensing. The relation between the hybrid and digital beamformings is studied numerically by varying different parameters, such as the number of radio frequency (RF) chains, analog to digital converters (ADCs) and multiplexed symbols. Computer simulations reveal that for the given number of RF chains and ADCs, the performance gap between digital and hybrid beamformings can be decreased by decreasing the number of multiplexed symbols. Moreover, for the given number of multiplexed symbols, increasing the number of RF chains and ADCs will increase the total sum rate of the hybrid beamforming which is expected.

math.OC↗

arXiv2014

Sensing Throughput Tradeoff for Cognitive Radio Networks with Noise Variance Uncertainty

This paper proposes novel spectrum sensing algorithm, and examines the sensing throughput tradeoff for cognitive radio (CR) networks under noise variance uncertainty. It is assumed that there are one white sub-band, and one target sub-band which is either white or non-white. Under this assumption, first we propose a novel generalized energy detector (GED) for examining the target sub-band by exploiting the noise information of the white sub-band, then, we study the tradeoff between the sensing time and achievable throughput of the CR network. To study this tradeoff, we consider the sensing time optimization for maximizing the throughput of the CR network while appropriately protecting the primary network. The sensing time is optimized by utilizing the derived detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to noise ratio (SNR) wall (i.e., robust against noise variance uncertainty) and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED) is quantified analytically. We show that the optimal sensing times with perfect and imperfect noise variances are not the same. In particular, when the frame duration is 2s, and SNR is -20dB, and each of the bandwidths of the white and target sub-bands is 6MHz, the optimal sensing times are 28.5ms and 50.6ms with perfect and imperfect noise variances, respectively.

stat.AP↗

arXiv2014

Pilot Optimization and Channel Estimation for Multiuser Massive MIMO Systems

This paper proposes novel pilot optimization and channel estimation algorithm for the downlink multiuser massive multiple input multiple output (MIMO) system with $K$ decentralized single antenna mobile stations (MSs), and time division duplex (TDD) channel estimation which is performed by utilizing $N$ pilot symbols. The proposed algorithm is explained as follows. First, we formulate the channel estimation problem as a weighted sum mean square error (WSMSE) minimization problem containing pilot symbols and introduced variables. Second, for fixed pilot symbols, the introduced variables are optimized using minimum mean square error (MMSE) and generalized Rayleigh quotient methods. Finally, for $N=1$ and $N=K$ settings, the pilot symbols of all MSs are optimized using semi definite programming (SDP) convex optimization approach, and for the other settings of $N$ and $K$, the pilot symbols of all MSs are optimized by applying simple iterative algorithm. When $N=K$, it is shown that the latter iterative algorithm gives the optimal pilot symbols achieved by the SDP method. Simulation results confirm that the proposed algorithm achieves less WSMSE compared to that of the conventional semi-orthogonal pilot symbol and MMSE channel estimation algorithm which creates pilot contamination.

stat.AP↗

arXiv2014

Transceiver design for single-cell and multi-cell downlink multiuser MIMO systems

This thesis designs linear transceivers for the down link multiple user multiple input multiple output single-cell and multiple-cell systems. The transceivers are designed by assuming perfect and imperfect channel state information at the BS and mobile stations (MS). Different signal to interference plus noise ratio, mean square error and rate-based design criteria are considered. These design criteria are formulated by considering total BS, per BS antenna, per user, per symbol or a combination of per BS antenna and per user (symbol) power constraints. To solve these problems generalized down link up link and down link interference duality approaches are proposed. We have also shown that the weighted sum rate maximization problem can be equivalently formulated as weighted sum mean square error minimization problem with additional optimization variables and constraints. We also develop distributed transceiver design algorithms to solve weighted sum rate and mean square error optimization problems for coordinated BS systems. The distributed transceiver design algorithms employ modify matrix fractional minimization and Lagrangian dual decomposition methods.

math.OC↗

arXiv2014

USRP Implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks

This paper presents the Universal Software Radio Peripheral (USRP) experimental results of the Max-Min signal to noise ratio (SNR) Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks which is recently proposed in \cite{BogaMaxMinSNRJournal2013}. Extensive experiments are performed for different set of parameters. In particular, the effects of SNR, number of samples and roll-off factor on the detection performances of the latter algorithms are examined briefly. We have observed that the experimental results fit well with those of the theory. We also confirm that these algorithms are indeed robust against carrier frequency offset, symbol timing offset and noise variance uncertainty.

stat.AP↗

arXiv2013

Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty

This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled signal of total duration equal to the symbol period is combined linearly. Second, for this combined signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the signal energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm ($P_f$) and detection ($P_d$) expressions are derived for additive white Gaussian noise (AWGN) channel. The proposed algorithms are robust against noise variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better $P_d$ than that of the Eigenvalue decomposition and energy detection algorithms in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed algorithms also guarantee the desired $P_f(P_d)$ in the presence of adjacent channel interference signals.

math.OC↗

arXiv2013

Linear Transceiver design for Downlink Multiuser MIMO Systems: Downlink-Interference Duality Approach

This paper considers linear transceiver design for downlink multiuser multiple-input multiple-output (MIMO) systems. We examine different transceiver design problems. We focus on two groups of design problems. The first group is the weighted sum mean-square-error (WSMSE) (i.e., symbol-wise or user-wise WSMSE) minimization problems and the second group is the minimization of the maximum weighted mean-squareerror (WMSE) (symbol-wise or user-wise WMSE) problems. The problems are examined for the practically relevant scenario where the power constraint is a combination of per base station (BS) antenna and per symbol (user), and the noise vector of each mobile station is a zero-mean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel downlink-interference duality based iterative solution. Each of these problems is solved as follows. First, we establish a new mean-square-error (MSE) downlink-interference duality. Second, we formulate the power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. For the first group of problems, we have established symbol-wise and user-wise WSMSE downlink-interference duality.

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