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It is important to know the community coping strategies during the rapid uprise of a pandemic, as this helps to predict the consequences, especially in the mental health spectrum. This study aims to explore coping strategies used by Bangladeshi citizens during the major wave of the COVID-19 pandemic. Design: Prospective, cross-sectional survey of adults living in Bangladesh. Methods: Participants were interviewed for socio-demographic data and completed the Bengali-translated Brief-COPE Inventory. COPING indicators were categorized in four ways, such as approach, avoidant, humor, and religion. Results: Participants (N = 2001), aged 18 to 86 years, were recruited from eight administrative divisions within Bangladesh (mean age 31.85 ± 14.2 years). The male-to-female participant ratio was 53.4% (n = 1074) to 46.6% (n = 927). Higher scores were reported for approach coping styles (29.83 ± 8.9), with lower scores reported for avoidant coping styles (20.83 ± 6.05). Humor coping scores were reported at 2.68 ± 1.3, and religion coping scores at 5.64 ± 1.8. Both men and women showed similar coping styles. Multivariate analysis found a significant relationship between male gender and both humor and avoidant coping (p < 0.01). Male gender was found to be inversely related to both religion and approach coping (p < 0.01). Marital status and education were significantly related to all coping style domains (p < 0.01). The occupation was related to approach coping (p < 0.01). Rural and urban locations differed in participants’ coping styles (p < 0.01). Exploratory factor analysis revealed two cluster groups (factors 1 and 2) of mixed styles of coping. Conclusions: Participants in this study coped with the COVID-19 pandemic by utilizing mixed coping strategies. This study finds female gender, the married, elderly, and rural populations were adaptive to positive approaches to coping, whereas the male and educated population had the avoidant approach to coping.

In this paper, a downlink (DL) precoding scheme is introduced for time-division-duplex (TDD) multi-cell multi-user 3D massive MIMO/full-dimension MIMO (FD-MIMO) systems. A pre-beamformer based on uplink direction-of-arrival (DoA) is incorporated with interference alignment (IA) scheme to provide more degrees of freedom for each cell. We analyze the feasible conditions of applying IA schemes and provide the corresponding precoding schemes for different inter-cell interference scenarios. Simulation results show that the introduced IA-DoA-based multi-cell multi-user precoding and power allocation scheme significantly outperform the existing IA-based precoding strategies for FD-MIMO systems.

Reconfigurable intelligent surface (RIS) offers tremendous spectrum and energy efficiency in wireless networks by adjusting the amplitudes and/or phases of passive reflecting elements to optimize signal reflection. With the agility and mobility of unmanned aerial vehicles (UAVs), RIS can be mounted on UAVs to enable three-dimensional signal reflection. Compared to the conventional terrestrial RIS (TRIS), the aerial RIS (ARIS) enjoys higher deployment flexibility, reliable air-to-ground links, and panoramic full-angle reflection. However, due to UAV's limited payload and battery capacity, it is difficult for a UAV to carry a RIS with a large number of reflecting elements. Thus, the scalability of the aperture gain could not be guaranteed. In practice, multiple UAVs can form a UAV swarm to enable the ARIS cooperatively. In this article, we first present an overview of the UAV swarm-en-abled ARIS (SARIS), including its motivations and competitive advantages compared to TRIS and ARIS, as well as its new transformative applications in wireless networks. We then address the critical challenges of designing the SARIS by focusing on the beamforming design, SARIS channel estimation, and SARIS's deployment and movement. Next, the potential performance enhancement of SARIS is showcased and discussed with preliminary numerical results. Finally, open research opportunities are illustrated.

This study aims to investigate the health-related quality of life and coping strategies among COVID-19 survivors in Bangladesh.MethodsThis is a cross-sectional study of 2198 adult, COVID-19 survivors living in Bangladesh. Data were collected from previously diagnosed COVID-19 participants (confirmed by an RT-PCR test) via door-to-door interviews in the eight different divisions in Bangladesh. For data collection, Bengali translated Brief COPE inventory and WHO Brief Quality of Life (WHO-QOLBREF) questionnaires were used. The data collection period was from June 2020 to March 2021.ResultsMales 72.38% (1591) were more affected by COVID-19 than females 27.62% (607). Age showed significant correlations (pConclusionSurvivors of COVID-19 showed mixed types of coping strategies; however, the predominant coping strategy was avoidant coping, followed by problem focused coping, with emotion focused coping reported as the least prevalent. Marital status, occupation, living area and co-morbidities showed a greater effect on QoL in all participants. This study represents the real scenario of nationwide health associated quality of life and coping strategy during and beyond the Delta pandemic.

Scalable video coding (SVC) and video caching are two promising techniques in the 5th generation networks to improve the users’ quality of experience (QoE) in terms of video retrieval. In this paper, we study the video content retrieval in cache-aided device-to-device (D2D) networks, where each video content is coded into multiple layers via SVC. In video caching placement phase, the probabilistic caching placement policy is applied, while in content retrieval phase, the non-orthogonal transmission scheme is utilized. Besides, different D2D transmitter selection algorithms are considered and cache-aided data rate (CADR) is formulated as a metric in this paper, and it is maximized by jointly optimizing the probability caching policy and the power allocation policy in two phases. Analytical results show that probabilistic caching policy incorporated with power domain non-orthogonal transmission scheme can achieve significant benefits compared with other benchmark schemes.

Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users’ distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynamically based on users’ distribution in the network. Taking directly UE measurement results as input, deep reinforcement learning agent can track and predict the UE distribution pattern and come up with the best broadcast beams for each cell. Extensive simulation results show that the introduced framework can achieve the optimal coverage, and converge to the oracle solution for both single sector and multiple sectors environment, and for both periodic and Markov mobility patterns.

In this paper, superimposed pilot based communication strategies are investigated for a time-division duplex (TDD)-based massive Full-Dimension MIMO (FD-MIMO) network jointly considering both uplink (UL) and downlink (DL) performance. To be specific, the DL multi-cell multi-user MIMO operation is connected to UL channel estimation through the superimposed pilot. The performance of UL channel estimation is analytically characterized and the estimated UL channel is linked to the DL MIMO operation for the massive MIMO network. In this way, the corresponding feedback for DL channel state information (CSI) can be eliminated while the UL pilot overhead can be minimized. Results suggest that superimposed pilot could significantly improve the overall network performance of a massive FD-MIMO network.

Mobile network operators (MNOs) are in the process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in fifth generation (5G) and beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top five challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond- 5G and sixth generation (6G) networks.

Introduction This study aims to investigate the health-related quality of life and coping strategies among COVID-19 survivors in Bangladesh. Methods This is a cross-sectional study of 2198 adult, COVID-19 survivors living in Bangladesh. Data were collected from previously diagnosed COVID-19 participants (confirmed by an RT-PCR test) via door-to-door interviews in the eight different divisions in Bangladesh. For data collection, Bengali-translated Brief COPE inventory and WHO Brief Quality of Life (WHO-QoLBREF) questionnaires were used. The data collection period was from October 2020 to March 2021. Results Males 72.38% (1591) were more affected by COVID-19 than females 27.62% (607). Age showed significant correlations (p Conclusion Survivors of COVID-19 showed mixed types of coping strategies; however, the predominant coping strategy was avoidant coping, followed by problem-focused coping, with emotion-focused coping reported as the least prevalent. Marital status, occupation, living area and co-morbidities showed a greater effect on QoL in all participants. This study represents the real scenario of nationwide health-associated quality of life and coping strategies during and beyond the Delta pandemic.

AimThe aim of this research is to focus on gaining an insight into the knowledge, attitudes, behavioural practises (KAP), and psychological impact relating to COVID-19 among the people living with spinal cord injury receiving in-patient rehabilitation.MethodsA prospective, cross-sectional survey of people with SCI (N = 207), who were in active in-patient rehabilitation from two tertiary SCI Rehabilitation Centres in Bangladesh. Data were collected via face-to-face interviews, after voluntary consent, using a pretested, language validated questionnaire on Knowledge, Attitude and Behavioural practises (KAP) and the Depression, Anxiety, Stress Scale (DASS-21). Ethical approval and trial registration were obtained prospectively.ResultsA total of 207 people with SCI responded, among which 87% were men and 13% were women, with a mean age of 34.18 ± 12.9 years. Within the sample group, people living with tetraplegia comprised 33.8%, and people living with paraplegia comprised 66.2%. Overall, 63.8% of the participants were diagnosed with an SCI categorised as ASIA-A. Overall, the “knowledge score” was 8.59 ± 2.3 out of 12, “depression” was 11.18 ± 8, “anxiety” was 7.72 ± 5.1, and “stress” was 9.32 ± 6.7 from a total of 21 scores each category. The strong correlation was between knowledge, DASS scores, and age (p < 0.05). In addition, there was a strong correlation between knowledge, gender (p < 0.05) and education (p < 0.01). Binary logistic regression found a stronger association of knowledge and DASS scores with gender, young age, illiteracy (p < 0.01), and rural residence (p < 0.05). A positive relationship was found between depression and anxiety scores (p < 0.01) and a moderate positive relationship was found between depression and stress scores (p < 0.01). A positive attitude was reported by the majority of participants (p < 0.05). In terms of behavioural practises, participants reported both self and caregiver had followed health advice with regard to consulting health professionals (65.7%), implementing isolation (63.8%), taking droplet precaution care (87.4%), and hygiene care (90.3%).ConclusionParticipants in this study reported high levels of knowledge, adoption of positive attitudes, and the practise of positive health advisory behaviours related to COVID-19 prevention procedures. However, high levels of depression, anxiety, and stress were also reported. Overall, women and younger participants were more likely to have high KAP, whereas those living in rural areas and with literacy challenges were less likely to report high knowledge scores.

Executive summary: The first confirmed case of COVID 19 in Bangladesh was on 8th March 2020, then it subsequently rises and makes the government worried as well as for mass people of the country. Within 9th March it reaches 100 and exceeded 200 cases within next two days. Almost every country taken therapeutic and nontherapeutic measures to control the devastating situation of SARS-CoV where in Bangladesh there is an ongoing debate whether the measures have been adopted adequately or efficiently. It seems government did not impose any strict rules and the mass people also seem not too serious to maintain just a simple rule of wearing a Musk, which seems fancy and impractical to them. However, the consequences of the impact of SARS CoVwas frustrating, until today confirmed case was 371631, death case 5405, recovered 284833 and in Isolation 81393 (evident from corona.gov.bd).Developed countries investing a lot in research to discover the vaccine that can fight SARS CoV. Health care professionals with adequate training act as frontline fighters for SARS-CoV where all over the globe Rehabilitation professionals act as crucial health care providers for post-COVID patients. For instance, our research has four phases where phase one is completed and phase two is under process. For now, we aimed to complete our phase three and four. Phase Three aim: Identify the Functioning, disability, and other indicators of COVID-19 positive patients after recovery. and Phase four aim: evaluate the impact of physiotherapy and rehabilitation among the recovered COVID-19 patients.

Background: A Delphi-Based Consensus Statement: Recommendation for Physiotherapy Management and Rehabilitation of people living with Long COVID in Bangladesh Background: Covid-19 is a new and deadly virus with global consequences with special vulnerabilities in Bangladesh. The World Health Organization confirmed 22,451,122,614 cases and 4,627,540 deaths, worldwide (13th September 2021). In the United Kingdom, 1.5% of the estimated population of 970,000, suffers from Long COVID. Physiotherapy plays a vital role both in decreasing the number of deaths during the acute stage and improving function for patients across the spectrum of severity in the acute, sub-acute, and long-term context. Objectives: This Delphi-Based Consensus, provides recommendations for physiotherapy management and rehabilitation of people living with Long COVID. It includes recommendations for physiotherapy management for acute and subacute respiratory complications, a specific prescriptive exercise guideline for fatigue and musculoskeletal dysfunction and for improving psychological outcomes through combination of both counseling and rehabilitation. Methods: A team of 8 national and 6 international experts working in different disciplines were provided with studies on physiotherapy interventions in Long COVID-19 and were asked to provide treatment opinions based on the current literature for musculoskeletal, neurorehabilitation, respiratory, cardiac and psychological health domains. Statements were ranked and presented with individual opinions measured on a 10-point Likert Scale. The mean scores for each intervention were calculated and a manuscript with recommended physiotherapy interventions was prepared based on currently available evidence as presented and agreed upon by expert consensus. Findings: Experts recommended the following interventions based on mean agreement scores: diaphragmatic breathing (Mean = 7.5), thoracic expansion (with shoulder elevation) (Mean =8.5) and expiratory muscle training (Mean=6.3) for respiratory rehabilitation. Mild to moderate graded exercise (Mean= 7.7) was suggested for recovery from fatigue and McKenzie directional preference exercises for low back pain (Mean= 8.6), neck pain (Mean =7.9) and knee pain (Mean =8.3). Additionally, communicative and educative strategies were recommended for the rehabilitation of COVID-19. Conclusion: This consensus is structured according to the expert opinions of medical and rehabilitation professionals and based on current literature to recommend a series of rehabilitation interventions for physiotherapy professionals to manage Long COVID cases. Funding: This research has not been funded by any other external organization nor received any grants from commercial or not-for-profit organizations. Declaration of Interests: None declared. Ethics Approval Statement: Ethical permission was obtained from the Centre for the Rehabilitation of the Paralysed (CRP) ethics committee. The reference number CRP-R&E-0401-337. Trial Registration: Trial registration from the WHO Clinical trial registry platform, the reference number CTRI/2020/10/028179.

Study DesignA prospective cross-sectional survey.ObjectiveThe study aimed to examine the Knowledge, Attitudes, and Practices (KAP) of people living with Spinal cord injury (SCI) towards COVID-19 and their psychological status during in-patient rehabilitation in Bangladesh.SettingThe Centre for the Rehabilitation of the Paralyzed (CRP) and the National Institute of Traumatology and Orthopedic Rehabilitation (NITOR), two tertiary level hospitals in Dhaka, Bangladesh.MethodsFrom July to September 2020, a prospective, cross-sectional survey of SCI subjects, 13-78 years of age, carried out in two SCI rehab centers in Bangladesh. Data has been collected by face to face interview through a pretested, and language validated questionnaire on KAP and Depression, Anxiety, Stress (DASS). Ethical approval and trial registration obtained prospectively. As all the patients were previously living with Spinal cord injury (SCI), therefore, all the patients admitted/ attend SCI rehab centers were considered as SCI positive samples.ResultsA total of 207 people with SCI responded, 87%were male, and 13% were female with mean age34.18±12.9 years. 33.8% was tetraplegic and 66.2% was paraplegic and 63.8% of them were diagnosed ASIA-A, with motor score 45.38±19.5, sensory score 97.2±52, SpO2 95.07±3.3, and Vo2max 35.7±3.7mL/kg/min. 178 people had at least one health issue. Overall knowledge score was 8.59±2.3 out of 12, depression 11.18±8, anxiety 7.72±5.1, and stress was 9.32±6.7 from a total of 21 scores each. There was a correlation between Knowledge and DASS with age (POR 6.6, 6.6, .95, 6.6; POR.418, POR3.81, POR.48, Pr.45, Pr.58, PConclusionsDuring in-patient rehabilitation in Bangladesh, the majority of SCI reported that they had communicated with health professionals and practiced behaviors that would reduce transmission and risk of COVID-19.

In this paper, downlink (DL) precoding and power allocation strategies are identified for a time-division-duplex (TDD) multi-cell multi-user massive Full-Dimension MIMO (FD-MIMO) network. Utilizing channel reciprocity, DL channel state information (CSI) feedback is eliminated and the DL multi-user MIMO precoding is linked to the uplink (UL) direction of arrival (DoA) estimation through estimation of signal parameters via rotational invariance technique (ESPRIT). Assuming non-orthogonal/non-ideal spreading sequences of the UL pilots, the performance of the UL DoA estimation is analytically characterized and the characterized DoA estimation error is incorporated into the corresponding DL precoding and power allocation strategy. Simulation results verify the accuracy of our analytical characterization of the DoA estimation and demonstrate that the introduced multi-user MIMO precoding and power allocation strategy outperforms existing zero-forcing based massive MIMO strategies., 32 pages, 8 figures, submitted to IEEE Transactions on Wireless Communications

Background: The aim of this study was to identify the prevalence of ‘Long COVID’ symptoms (LCS) in a large cohort of survivors and identify any potential associated risk factors. Methods: A prospective survey was undertaken of an inception cohort of confirmed COV|ID-19 survivors (Aged 18 to 87 years). 14392 participants were recruited from 24 testing facilities across Bangladesh between June, and November 2020. All participants had a previously confirmed positive COVID-19 diagnosis, and reported persistent symptoms and difficulties in performing daily activities. Participants who consented, were contacted by telephone, and were interviewed regarding LCS, and restriction of activities of daily living. Cardio-respiratory parameters were also measured. Statistical analysis included binary logistic and multiple linear regressions were performed with LCS as the dependent variable. Associated risk factors were identified using Structural Equation Modelling (SEM). Findings: Among 2198 participants, the prevalence of LCS at 12 weeks was 16·1%. Overall, eight LCS were identified and in descending order of prominence are: fatigue, pain, dyspnea, cough, anosmia, appetite loss, headache, and chest pain. COVID survivors experienced between 1 to 5 LCS with an overall duration period of 21·8 ± 5·2 weeks. SEM predicted the length of LCS to be related to younger age, female gender, rural residence, prior functional limitation and smoking. Interpretation: In this cohort of survivors, at 31 weeks post diagnosis, the prevalence of LCS was 16·1%. The risk factors identified for presence and longer length of LCS warrant further research and consideration to support public health initiatives. Clinical Trial Registration Details: The study was registered at World Health Organization (WHO) Primary Clinical trial registry platform (CTRI/2020/09/028165) on 30/09/2020 with the title “Symptoms presentation among the COVID-19 survivors in Bangladesh”. Funding Information: No funding. Declaration of Interests: We declare no competing interests. Ethics Approval Statement: Ethical permission was obtained from the Institutional Review Board at the Institute of Physiotherapy, Rehabilitation, and Research (Ethical review committee at Bangladesh Physiotherapy Association) on September 17, 2020 (BPA-IPRR/IRB/17/09/2020/028). Verbal consent was obtained during the initial telephone call and written consent was obtained at interview. The principles of the Helsinki Declaration19 were followed throughout the research to ensure confidentiality, ethics and privacy.

In order to meet the challenge of increasing data-rate demand as well as the form factor limitation of the base station (BS), 3-D massive multiple-input multiple-output (MIMO) technology has been introduced as one of the enabling technologies for fifth generation mobile cellular systems. In 3-D massive MIMO systems, a BS will rely on the uplink sounding signals from mobile stations to figure out the spatial information for downlink MIMO operations. Accordingly, multi-dimensional parameter estimation of a MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the angle and delay estimation for 3-D massive MIMO systems under a parametric channel modeling. To be specific, we first introduce separate low complexity time delay and angle estimation algorithms based on unitary transformation, and analytically characterize the mean squared errors (MSEs) of these estimations for massive MIMO systems. Then, a matrix-based estimation of signal parameters via rotational invariance technique algorithm is applied to jointly estimate the delay and the angles where the MSEs are also analytically characterized. Our results show that the antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance. Simulation results suggest that the characterized MSEs match well with the simulated ones.

Extreme densification is one of the key technologies to get to high data rates in future 5G networks. In this paper, we consider a solar energy harvesting model for small cell base stations (SCBSs). We use the solar energy resource data (http://rredc.nrel.gov/solar/new_data/confrrm/) by the Cooperative Network for Renewable Resource Measurements. The data set includes measurements for solar irradiance, mainly global horizontal irradiance (GHI), along with a set of predictor variables. The data set spans multiple states including Florida, Oregon, Texas, Mississippi, North Carolina, Georgia, New Mexico and West Virginia. Using the data set in North Carolina, we explore the feasibility and reliability of using solar energy to power SCBSs. In this paper, we test the hypothesis that we can accurately predict the availability status of SCBSs, defined as having sufficient power to provide service to end-users, if the GHI value is within a certain range.

Mobile edge and vehicle-based depth sending and real-time point cloud communication is an essential subtask enabling autonomous driving. In this paper, we propose a framework for point cloud multicast in VANETs using vehicle to infrastructure (V2I) communication. We employ a scalable Binary Tree embedded Quad Tree (BTQT) point cloud source encoder with bitrate elasticity to match with an adaptive random network coding (ARNC) to multicast different layers to the vehicles. The scalability of our BTQT encoded point cloud provides a trade-off in the received voxel size/quality vs channel condition whereas the ARNC helps maximize the throughput under a hard delay constraint. The solution is tested with the outdoor 3D point cloud dataset from MERL for autonomous driving. The users with good channel conditions receive a near lossless point cloud whereas users with bad channel conditions are still able to receive at least the base layer point cloud.

With the promise of meeting future capacity demands, 3-D massive-MIMO/full dimension multiple-input-multiple-output (FD-MIMO) systems have gained much interest in recent years. Apart from the huge spectral efficiency gain, 3-D massive-MIMO/FD-MIMO systems can also lead to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of the system, accurate channel state information is critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for 3-D millimeter wave massive-MIMO orthogonal frequency division multiplexing (OFDM) systems. To be specific, the DoA is estimated using estimation of signal parameter via rotational invariance technique method, and the root mean square error of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system. An ergodic capacity analysis of the system in the presence of DoA estimation error is also conducted, and an optimum power allocation algorithm is derived. Furthermore, it is shown that the DoA-based channel estimation achieves a better performance than the traditional linear minimum mean squared error estimation in terms of ergodic throughput and minimum chordal distance between the subspaces of the downlink precoders obtained from the underlying channel and the estimated channel.

Reservoir Computing (RC) is a class of machine learning approaches that is suitable for prediction tasks with low computational complexity. In this paper, an RC-based symbol detection for MIMO- OFDM systems is presented where RC is realized through the echo state network (ESN). Detailed energy-efficiency analysis is conducted to characterize the energy-efficiency of the introduced symbol detector. To be specific, the transmit power, the circuit power, and the computational power at both transmitter and receiver are jointly considered for the energy-efficiency analysis. The overall system energy-efficiency as well as the receiver energy-efficiency of the introduced RC-based symbol detector are compared with those of the popular linear minimum mean squared error (LMMSE)-based approach. Simulation and numerical results show that the RC-based symbol detector is a ``green'' solution compared to the traditional LMMSE-based method with lower energy consumption per information bit.

While massive multiple-input multiple-output system (MIMO) promises to provide substantial increase in spectral efficiency-per- cell, due to high dimensionality, estimating its channel is considered as one of the major challenges towards extracting all the benefits of such large antenna systems. In this paper, based on parametric channel modeling, we present a direction of arrival (DoA) estimation method for multi-cell multi-user 3D massive-MIMO/Full Dimension (FD-MIMO) orthogonal frequency division multiplexing (OFDM) system using estimation of signal parameter via rotational invariance technique (ESPRIT). Furthermore, we analytically characterize the performance of ESPRIT-based DoA estimation in multiuser scenario, and investigate how pilot contamination, intra-cell, and inter-cell interference affect DoA estimation performance.

In 3D massive MIMO (multiple-input multiple-output) systems, especially in time division duplex (TDD) mode, a base station (BS) relies on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO processing. Accordingly, multi-dimensional parameter estimation of MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the joint estimation of elevation and azimuth angles as well as the delay parameters for 3D massive MIMO orthogonal frequency division multiplexing (OFDM) systems under a parametric channel modeling. To be specific, we introduce a matrix-based joint parameter estimation method, and analytically characterize its performance for massive MIMO OFDM systems. Results show that antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance, and the characterized MSEs match well with the simulated ones. Also, the parametric channel estimation outperforms the MMSE-based channel estimation in terms of the correlation between the estimated channel and the real channel.

Accurate downlink channel state information at the transmitter (CSIT) is essential to utilize the benefit of 3D Massive MIMO/FD-MIMO systems. Conventional approaches to obtain CSIT for FDD MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for 3D Massive MIMO/FD-MIMO systems because of the large dimensionality of the channel matrix. In this paper, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink (UL) direction-of-arrivals (DoAs) and downlink (DL) direction-of-departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming to compare the performance with traditional method in terms of downlink achievable rate that we derived. Simulation results also verifies that, in terms of achievable rate, our proposed method outperform the traditional beamforming method.

In this paper, based on parametric channel modeling, we present a direction of arrival (DoA) estimation method for multi-cell multi-user 3D massive- MIMO#x002F;Full Dimension multiple-input multiple-output (FD-MIMO) orthogonal frequency division multiplexing (OFDM) system. Using estimation of signal parameter via rotational invariance technique (ESPRIT), we investigate into the performance of DoA estimation for two possible scenarios- first, for the case where no angular domain overlapping from interference signals exists with the target user's DoAs, and second, for the case where a number of DoAs of the target user's channel are completely overlapped with DoAs from interference signals, to be specific, from pilot contamination.

Pulse based communication scheme, e.g., On-OFF Keying modulation Spread in Time (TS-OOK), is currently the only solution for nano-networks. In this paper, we present an initial analysis on the performance of pulse based communication scheme in nano-networks using stochastic geometry. To be specific, by modeling the transmission of pulse as random events, we theoretically characterize the cumulative distribution function of signal-to-interference-plus-noise ratio and link throughput in nano-networks. Furthermore, both simulation and numerical results are presented to verify our analysis. Our results show that low-weight codes for TS-OOK can significantly increase the average throughput in dense nano-networks where the interference cannot be neglected.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive-MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the capacity of the system is analytically characterized. It has been shown that optimum precoding matrix for the massive MIMO OFDM system can be constructed from the DoA estimation at the base station.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive- MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the root mean square error (RMSE) of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system.

Simulation is presented on the effects of polarization mode dispersion (PMD) as a source of system impairment in high bit-rate fiber-optic transmission systems. A simulation model is introduced that illustrates how the differential group delay (DGD) affects the bit error rate (BER) limiting the system performance. Power penalty due to PMD is determined from both the eye diagram and BER curve and their mutual consistency is verified. As a PMD compensation scheme, in this paper, a polarization controller (PC) is used and the performance of two systems with and without PC is compared. Results show that at a bit rate of 10 Gbps, for a DGD of 80 ps, power penalty can be improved by about 1.25 dB when using a polarization controller in the system.

A novel polarization multiplexed coherent optical OFDM system is proposed and experimentally demonstrated for 100 Gb/s transmission. In this technique, two signals can be transmitted at the same wavelength with orthogonal state of polarization through OFDM transmitter and at the receiver end those signals are received by coherent detector. The implemented model can be treated as a special kind of multiple-input multiple output (MIMO) model which is intrinsically represented by a two-element Jones vector. Though PMD represents a major source of performance degradation in polarization multiplexed systems due to its polarization sensitive detection, the special MIMO system successfully mitigates the effect without any extra compensation system. Performance of the system is evaluated through simulation and different criteria are analyzed with the simulation result.