Multiuser Diversity Research Articles

Extending the coverage of user-diversified IM-DD PON by FDM: a mathematical model with experimental verification.

Future passive optical networks (PONs) call for more flexibility to support diversified users with various rate demands and link qualities. Using traditional time-division multiplexing (TDM), the concept of a flexible rate PON was proposed to accommodate more users with link diversity by rate adaptation. In this Letter, we reveal the PON coverage can be further extended through frequency-division multiplexing (FDM) in the presence of multiuser diversity, namely, (i) there exist users with frequency-dependent link conditions and (ii) the link conditions exhibit disparity among users. We build a mathematical model and propose an optimization algorithm based on the binary tree search to optimize diversity gain. We experimentally verify its feasibility by studying the diversity gain concerning chromatic dispersion, optical path loss, and signal-to-noise ratio (SNR) variation in a 200G-class intensity-modulation direct-detection (IM-DD) system.

On the performance of layered transmission with optimum decoding ordering and multiuser diversity for a K-user downlink scenario

On the performance of layered transmission with optimum decoding ordering and multiuser diversity for a K-user downlink scenario

On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users’ target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed, which is then combined with H-N-NOMA to make a novel scheme termed H-N-NOMA/QDUP into being. Numerical results are presented to reveal the impact factors on QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

Analysis of Multiuser Diversity Gain Achieved by MIMO Systems Through User Scheduling Based on Limited Feedback in Dense Cellular Networks

Analysis of Multiuser Diversity Gain Achieved by MIMO Systems Through User Scheduling Based on Limited Feedback in Dense Cellular Networks

Optimizing User-Level Packet Scheduling Performance through Optimal CQI-Based Resource Allocation in LTE

Our research investigates the Dynamic Resource Allocation in the Downlink of OFDMA-based LTE-A networks. It addresses both user-level and system-level packet scheduling performance. At the user-level, a Traffic Differentiator stage segregates packet queues from active users into different service queues based on service types. Users are prioritized within each service queue based on their QoS requirements and wireless channel conditions, utilizing SPSSA. At the system-level, fairness among users is a key consideration. We propose the PITDSA in the TD Scheduler stage, which aims to allocate just enough radio resource to real-time and non-real time services and assign the remaining available resource to background service. In the FD Scheduler stage, we propose an optimal CQI selection algorithm for resource allocation to exploit frequency domain multi-user diversity. We present our simulation results and analyses of all novel algorithms, and the performance of the Optimal CQI selection algorithm is compared with other algorithms. Our proposed scheduling algorithm demonstrates improved QoS for real-time and non-real time services while maintaining a good traded user-level and system-level performance. Future work can focus on optimizing the throughput or the fairness or both, and more advanced and complex techniques can be designed with the same goal.

Design and analysis of quantized feedback based user-antenna joint scheduling scheme for ongoing 5G and beyond multi-user massive MIMO FDD communication systems

For the efficient user scheduling (US) and interference mitigation, channel state information (CSI) is mandatory at the base station (BS), particularly for the frequency division duplexing (FDD) systems where the users need to provide some CSI back to the BS for performing the scheduling process. As the feedback overhead increases drastically with increase of antennas as well as users, so, US with limited/reduced feedback is essential. Deploying massive multiple-input multiple-output frequency division duplexing (mMIMO FDD) systems remains an open area for research with major difficulties of associated uplink CSI feedback load as the downlink and uplink channels are not reciprocal. Therefore, smart balancing is required between feedback data and achievable throughput while scheduling the users. In order to handle the above mentioned issue, we employed a selective 4-bit quantized CSI feedback based user-antenna paired/joint scheduling scheme for the multi user (MU) FDD mMIMO systems. The key idea involves in this process is that, only a limited set of users qualifying a predefined selection threshold feed back the 4-bit quantized signal-to-interference-plus-noise ratio (SINR) values based on angle-of-departure (AoD) along with the antenna indices for the scheduling process. The BS schedules the users with respective best antenna. Furthermore, using this scheme the multi user diversity gain is also achieved. This dynamic adjustment of antenna/user selection and collision free antenna sharing between the users majorly helps in improving the throughput of the system with this selective 4-bit quantized limited feedback. The mathematical sum rate performance analysis has been demonstrated along with simulation results for different system parameters.

UAV-Assisted Opportunistic Beamforming in Internet of Things Networks

Opportunistic beamforming (OBF) is a promising multiple-input-multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high performance and low-complexity channel estimation, OBF is appropriate for internet of things (IoT) networks.However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the quality of service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and spectrum efficiency.

Angle-Domain Hybrid Beamforming-Based mmWave Massive MIMO-NOMA Systems

The millimeter-wave (mmWave) large-scale antenna arrays (LSAAs) systems play a vital role in increasing the beamforming (BF) gain and acquiring highly directional propagation. Recently, non-orthogonal multiple access (NOMA) has been integrated into these systems to manage massive connectivity and achieve spectral-efficient communications. This paper focuses on angle-domain (AD) hybrid beamforming (BF) for mmWave LSAAs and NOMA systems, thanks to the low complexity, power consumption, and channel estimation overhead. However, with limited radio-frequency chains, the hybrid BF-based single-beam (SB)-NOMA scheme generating a single beam to serve the NOMA users fails to exploit the multi-user diversity due to narrow beams with LSAAs. To tackle this limitation, we design schemes offering additional degrees of freedom. More importantly, they require only the knowledge of angular information and are suitable for either linear or rectangular antenna arrays, unlike those proposed in the literature. The first scheme exploits the time-domain resources to schedule groups having high spatial interference within distinct time slots. To minimize the need for fast and precise synchronization when applying time division multiple access (TDMA) with mmWave NOMA, we leverage the multi-beam (MB)-NOMA framework. And we propose a joint SB-and MB-NOMA scheme to benefit from NOMA multi-user diversity, whatever the cell load and the users’ positions. Using the New York University channel simulator (NYUSIM), we further validate the performance of the proposed schemes compared to the solution proposed in the literature and others using fully digital BF. Specifically, the proposed TDMA-based scheme achieves a sum-rate gain of up to 83% over the TDMA-based one existing in the literature. Moreover, we verify the superiority of applying both SB-and MB-NOMA instead of only MB-NOMA.

Performance Analysis of Intelligent Reflecting Surface Assisted Opportunistic Communications

Intelligent reflecting surfaces (IRSs) are a promising technology for enhancing coverage and spectral efficiency, both in the sub-6 GHz and the millimeter wave (mmWave) bands. Existing approaches to leverage the benefits of IRS involve the use of a resource-intensive channel estimation step followed by a computationally expensive algorithm to optimize the reflection coefficients at the IRS. In this work, focusing on the sub-6 GHz band of communications, we present and analyze several alternative schemes, where the phase configuration of the IRS is randomized and multi-user diversity is exploited to opportunistically select the best user at each point in time for data transmission. We show that the throughput of an IRS assisted opportunistic communication (OC) system asymptotically converges to the optimal beamforming-based throughput under fair allocation of resources, as the number of users gets large. We also introduce schemes that enhance the rate of convergence of the OC rate to the beamforming rate with the number of users. For all the proposed schemes, we derive the scaling law of the throughput in terms of the system parameters, as the number of users gets large. Following this, we extend the setup to wideband channels via an orthogonal frequency division multiplexing (OFDM) system and discuss two OC schemes in an IRS assisted setting that clearly elucidate the superior performance that IRS aided OC systems can offer over conventional systems, at very low implementation cost and complexity.

Outage Performance Analysis of RSMA-Aided Semi-Grant-Free Transmission Systems

In this paper, we analyze the outage performance of a rate-splitting multiple access (RSMA)-aided semi-grant-free (SGF) transmission system, in which a grant-based user (GBU) and multiple grant-free users (GFUs) access the base station by sharing the same resource blocks. In the RSMA-aided SGF (RSMA-SGF) transmission system, the GBU and admitted GFU are respectively treated as the primary and secondary users by using the cognitive radio principle. With the aid of RSMA, the admitted GFU’s transmit power allocation, target rate allocation, and successive interference cancellation decoding order are jointly optimized to attain the maximum achievable rate for the admitted GFU, without deteriorating the GBU’s outage performance compared to orthogonal multiple access. Taking into account the extended non-outage zone achieved by rate-splitting, a closed-form expression is derived for the outage probability of the admitted GFU in the considered RSMA-SGF system. Asymptotic analysis for the admitted GFU’s outage probability is also provided. The superior outage performance and full multiuser diversity gain achieved by the RSMA-SGF transmission system are verified by the analytical and simulation results.

A Joint Hybrid Precoding/Combining Scheme Based on Equivalent Channel for Massive MIMO Systems

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones.

Exploiting User Diversity in OTFS Transmission for Beyond 5G Wireless Systems

Orthogonal time frequency space (OTFS) modulation is a recent technology that offers a significant advantage on link reliability due to its strong delay-Doppler resilience. In classical cellular technology, overall system performance is limited by the fading and interference experienced by particular user equipment (UE) over its radio resources (RR). Besides, OTFS performance depends on the channel diversity of the UEs. Therefore, this letter examines the potential of multi-user diversity for OTFS systems considering practical scenarios. Specifically, we introduce a novel scheduling algorithm that proposes the assignment of the available RR of OTFS frame to the UE with the largest number of channel taps, resulting in enhanced channel diversity. It is shown via both mathematical analysis and simulation results that the proposed scheduling technique outperforms the scheme with random access to delay-Doppler RR in terms of system bit error rate (BER) with maximum likelihood (ML) receiver. The BER results validate the accuracy of the proposed technique for OTFS transmission with minimum mean square error (MMSE) detector considering various system configurations.

Interleaved block subcarrier allocation and power combination for frequency symbol spreading multiuser diversity OFDMA

In a FSS-OFDMA, the OFDM symbol is spread, and the subcarriers are assigned for each user in the frequency band. As the principal subcarrier allocation for each user, localized and interleaved allocations have been proposed, and the interleaved allocation is improved for the BER performance to avoid continuous deep faded subcarrier channels. Also, in the independent channel fluctuation for each user, the MUDiv gain is obtained by the individual and block subcarrier allocations. However, they have a trade-off problem between the BER performance and the computational complexity. To solve this problem, the interleaved block subcarrier allocation has been proposed. On the other hand, the interleaved block is only evaluated for the individual subcarrier and is not evaluated for the several subcarriers. Moreover, the power combination between the subcarrier and the channel is also improved for the BER performance, but the individual and block subcarrier allocations are only evaluated. Therefore, in this paper, we propose the interleaved block subcarrier allocation and the power combination for a FSS-MUDiv/OFDMA.

Proportional Fair Scheduling for Downlink mmWave Multi-User MISO-NOMA Systems

In this paper, we study non-orthogonal multiple access (NOMA) user scheduling and resource allocation problem for a generic downlink single-cell multiple input and single output (MISO) millimeter wave (mmWave) system. The larger number of packed antennas and the highly directional property of mmWave communications enable directional beamforming to achieve spatial diversity. Toward this end, we consider two different hybrid precoding schemes which are based on orthogonal matching pursuit (OMP). Users are assigned into different clusters and the base station (BS) transmits superposed signals that share the same precoding vector. Moreover, both fixed number of users per cluster and dynamic number of users per cluster are investigated. We aim to jointly optimize the user clustering, service scheduling, and power allocation strategy, in maximizing the proportional fairness (PF) among the users and exploring the multiuser diversity and multiplexing gain. Since the formulated joint user clustering, scheduling and power allocation problem is a mixed integer non-convex optimization problem, we propose a two-fold methodology. First, we apply a hybrid precoding and user clustering scheme, where the hybrid precoder is constructed by singular vector division (SVD) or minimum mean square error (MMSE). Then, with the obtained result, we approximate the proportional fairness power allocation problem by a sequence of Geometric Programming (GP) problems which are solved iteratively. The proposed scheme strikes a balance between the spectral efficiency and service fairness. Results show that the proposed MISO-NOMA scheme which is based on MMSE hybrid precoder and the proposed user scheduling and power allocation strategy under proportional fairness metric can outperform various conventional MISO schemes. Furthermore, our proposed dynamic number of users per cluster scheme outperforms the fixed scheme and can be considered as an upper bound in several aspects, including spectral efficiency and fairness.

A Deep Learning-Based User Selection Scheme for Cooperative NOMA System with Imperfect CSI

This paper studies the user selection problem for a cooperative nonorthogonal multiple access (NOMA) system consisting of a base station, a far user, and N near users. The selected near user receives its own message and assists the far user by relaying the far user’s message. Firstly, we propose a user selection strategy to maximize the selected near user’s data rate while satisfying the quality-of-service (QoS) requirement of the far user. Considering that the channel state information (CSI) of users in actual communication is usually imperfect, we then analyze the outage probability of the NOMA system based on the user selection strategy under imperfect CSI and obtain a closed-form expression. The theoretical analysis shows that the diversity order of the NOMA system under imperfect CSI is 0, which means the multiuser diversity order disappears. In order to improve the impact of imperfect CSI on system performance, we use the deep learning method to identify and classify channels of imperfect CSI and improve the accuracy of CSI. The simulation results show that the theoretical analysis of outage performance is consistent with the numerical results. Compared with the strategy without the deep learning method, the proposed deep learning-based user selection scheme significantly improves the system performance. Furthermore, we verify that our scheme recovers the diversity gain.

Performance evaluation of MIMO DFT-Spread WR-OFDM system for spectrum efficiency and power efficiency

ABSTRACT It is important to design a spectrum-efficient and power-efficient wireless communication system, which is the main design target in cellular and wireless communication engineering. Basically, to make it, the desired system must have a low peak-to-average power ratio (PAPR), low-level of out-of-band (OOB) power emission and can provide a high throughput. In this paper, we propose a high-throughput cellular communication system, namely Discrete Fourier Transform-Spread Windowing and Restructuring Orthogonal Frequency Division Multiplexing (DFT-Spread WR-OFDM), which has considerably low PAPR and low OOB power emission for upcoming next-generation beyond 5G (B5G) and 6G cellular communication systems. High PAPR is still one of the challenging issues for the MIMO-OFDM system. The proposed system is an effective arrangement of DFT-Spreading and windowing techniques to reduce PAPR and OOB power emission, respectively. Multiuser diversity is obtained using localized subcarrier mapping. We take advantages of spatial multiplexing to enhance the data throughput significantly. Additionally, frequency-domain equalization (FDE)-minimum mean squared error (MMSE) is used to combat the inter-symbol interference (ISI) effect. Simulation results verify that the suggested system considerably lowers PAPR and OOB power emission compared to the conventional systems. Furthermore, the proposed scheme shows better bit error rate (BER) performance over the MIMO Rayleigh fading channel environment. Simulation results also confirmed that the channel capacity of our suggested system is better than the conventional multi-carrier systems.

Achieving Low Latency in Massive Access: A Mean-Field Approach

The next generation massive access has attracted considerable recent attention due to its potential in smart meters, industrial internet of things (IIoT), and smart traffics, etc. However, how to achieve the minimum queuing delay in massive access still remains open, thereby making quality-of-service (QoS) assurance a challenging issue in practice. In this paper, we aim at minimizing the average queuing delay by applying cross-layer scheduling with joint channel and buffer awareness, the complexity of which increases exponentially with the number of users. Fortunately, with massive users or devices, mean-field approximation can be adopted to substantially simplify the design and analysis of the delay-optimal scheduling. More specifically, we present a cocktail filling policy and a queue-aware multiuser diversity protocol, in which all backlogged packets of a user will be served by either NOMA or TDMA mode respectively, if the user’s channel gain is beyond a certain threshold. The average queuing delay and queue-length-violation probability are derived based on a Markov model. Numerical results will also demonstrate that the mean-field approximation based joint physical and network layer scheduling is capable of improving the QoS in massive access.

A Survey on Multiuser SWIPT Communications for 5G+

Increasing number of devices connected to the networks, applications and demands of new generation wireless communications cause very high energy consumption resulting in large amount of carbon emission. Thus, energy harvesting solutions together with accomplishing information transmission are required for energy efficient communications of new wireless communications generations such as 5G and 6G (i.e., 5G+). As such, due to the promise of energy efficient green communications, simultaneous wireless information and power transfer (SWIPT) techniques are expected to be indispensable component of 5G+. In this survey, the literature of multiuser SWIPT communications is reviewed for 5G+. Different multiuser SWIPT scenarios including multi-antenna communications, cooperative communications, network coding, communications security, and unmanned aerial vehicle (UAV)-enabled communication systems based on multiuser diversity and multiple access methods are thoroughly reviewed. The experimental studies are also discussed.

Cross Layer MAC Protocol for a Peer Conscious Opportunistic Network Coded Cooperation System

This paper presents a peer conscious opportunistic network coded cooperation (PC-O-NCC) system that exploits multi-user diversity (MUD) gain in a simple network coded cooperation (NCC) based network. It prioritizes sources with better channel conditions by granting them earlier access than the other competing nodes unlike the prevalent time division multiple access (TDMA) technique used widely in any NCC network. This improves the outage performance of the overall system. To prioritise sources with better channel conditions, a novel timer-based MAC protocol is proposed. The proposed protocol is designed such that it aims to reduce collisions by taking the network load into account along with channel conditions while generating the timer values. It also minimizes the power consumption in performing clear channel assessment (CCA) by sources which is a costly affair for battery-operated devices. The simulation results show that the proposed algorithm improves the outage performance while keeping the latency to a minimum value. The improvement in the outage performance can prove to be important in taking key decisions which becomes crucial in disaster management, drone assisted scenarios or intelligent transportation systems.

2D BiLSTM Based Channel Impulse Response Estimator for Improving Throughput in Underwater Sensor Network

In underwater acoustic (UWA) sensor network, the channel impulse response (CIR) at the transmitter is important to increase the link reliability and the throughput. The CIR feedback to the transmitter decreases the throughput due to the feedback propagation delay, and the estimation of the CIR at the transmitter is also difficult since the sound of speed profile (SSP) may not be continuously measured. This paper proposes a deep learning based CIR estimator that estimates the SSP from only one water temperature sensor at a depth of the transmitter. The proposed CIR estimator consists of a 2-dimensional temperature network, 2-dimensional bidirectional long short term memory (2D BiLSTM), and a fully connected layer. The proposed algorithm learns the SSP variation with the depth and the time using 2D BiLSTM and estimates the CIR from the SSP. The estimated CIRs are utilized for the multi-user diversity to increase the link reliability and the throughput of the UWA sensor network. The computer simulations and practical ocean experiments were executed to evaluate the estimation error, the bit error rate and the throughput of the proposed algorithm. The proposed CIR estimator demonstrated better performance than other 1D conventional algorithms.