Full-duplex communication for future wireless networks: Dynamic resource block allocation approach

In this paper, we discuss a full-duplex (FD) communication scenario, where multiple FD user equipments (UEs) share same spectrum resources (or resource blocks) simultaneously. The FD eNodeB deploys digital precoding and successive interference cancellation with optimal ordering algorithm, to allow coexistence of multiple UEs in downlink and uplink, respectively. The sharing of same resource blocks, results in co-channel interference (CCI), in downlink of a UE, from uplink signals of other UEs. To mitigate the interference, a smart antenna approach is adopted. The approach includes using multiple antennas at UEs to form directed beams towards eNodeB and nulls towards other UEs. However, the approach fails when the UEs due to their mobility align themselves in the same direction with respect to the eNodeB (eNB). In this paper, we propose a dynamic resource block allocation (DRBA) algorithm for avoiding CCI due to mobility of UEs, sharing the spectrum resource, in a FD communication scenario. The proposed algorithm shows significant improvement of the quality of service (QoS) of the communication links.     

Fairness-based user association and resource blocks allocation in satellite–terrestrial integrated networks

To meet the futuristic communications needs, a satellite–terrestrial integrated network (STIN) has been proposed and is a strong contender amongst emerging architectures. In our STIN model, we have considered a satellite-based base station (BS), dovetailed with a terrestrial N-tier heterogeneous network (HetNets). Our work considers jointly admission control of user equipment (UE), power allocation , fairness-based user association (UA), and fair spectrum resource allocation to UEs in STIN. With throughput maximization as an objective, considering such an environment, has not been investigated in the past. The formulated problem is a mixed integer non-linear programming (MINLP) problem that is Non-deterministic Polynomial-time Hard (NP-hard) and to achieve an optimal solution, it requires an exhaustive search. But, the computational load of exhaustive search increases exponentially as the number of UEs increases. Therefore, to obtain a near-optimal solution having low computational load an outer approximation algorithm (OAA) is proposed. To evaluate the proposed algorithm, extensive simulation work has been performed. The effectiveness of the proposed approach is verified by the results in terms of fairness in UA, fairness in resource block (RB) allocation, and throughput in the downlink (DL).     

Outage probability and ergodic capacity analysis of satellite–terrestrial NOMA system with mixed RF/mmWave relaying

This paper investigates the performance of hybrid radio frequency/millimeter wave (RF/mmWave) satellite–terrestrial relay network with non-orthogonal multiple access (NOMA) scheme. In particular, the satellite network operates at the Ka-band while the mmWave band is adopted at decode-and-forward (DF) relay–destination link. The blockage effect in mmWave communication is considered to deduce the ordered probability density function (PDF) of the user equipment (UE) distance. However, the randomness of the UEs’ location and the random beamforming design cause the difficulty of the performance analysis. Herein, we provide a closed-form approximation for the outage probability. Moreover, the system diversity order is derived from the asymptotic expressions of the outage probability at high signal-to-noise ratio (SNR) slopes. Besides, approximate ergodic capacity performances of the multi-UEs are evaluated with numerical integration. Simulations are applied to demonstrate the accuracy of the theoretical analysis and show that the performance of the ordered selection NOMA scheme outperforms the orthogonal multiple access scheme and random selection scheme.     

Improving spectral efficiency via pilot assignment and subarray selection under realistic XL-MIMO channels

The main requirements for 5G and beyond connectivity include a uniform high quality of service, which can be attained in crowded scenarios by extra-large MIMO (XL-MIMO) systems. Another requirement is to support an increasing number of connected users in (over)crowded machine-type communication (mMTC). In such scenarios, pilot assignment (PA) becomes paramount to reduce pilot contamination and consequently improve spectral efficiency (SE). We propose a novel quasi-optimal low-complexity iterative pilot assignment strategy for XL-MIMO systems, based on a genetic algorithm (GA). The proposed GA-based PA procedure turns the quality of service more uniform, taking into account the normalized mean-square error (NMSE) of channel estimation from each candidate of the population. The simulations reveal that the proposed iterative procedure minimizes the channel estimation NMSE averaged over the UEs. The second procedure is the subarray (SA) selection. In XL-MIMO systems, commonly a UE is close to an SA antenna subset such that a sufficient data rate can be achieved if only a specific SA serves that UE. Thus, an SA selection procedure is investigated to make the system scalable by defining the maximum number of UEs each SA can help. Hence, the SA clusters are formatted based on the PA decision. Furthermore, we introduce an appropriate channel model for XL-MIMO, which considers a deterministic LoS component with a distance-dependent probability of existence combined with a stochastic spatially correlated Rayleigh NLoS fading component. The developed simulations and analyses rely on this suitable channel model under realistic assumptions of pilot contamination and correlated channels.     

Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks

Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment strategies. In this work, we found that these two problems are related by the same performance metric, which makes it possible to optimize the two problems simultaneously. To solve this joint optimization problem, we propose a UAV diffusion deployment algorithm based on the virtual force field method. Firstly, according to the unique performance metric, we define two new virtual forces, which are the UAV-UAV force and UE-UAV force defined by FU and FV, respectively. FV is the main contributor to load balancing and UEs’ data rate fairness, and FU contributes to fine tuning the UEs’ data rate fairness performance. Secondly, we propose a diffusion control stratedy to the update UAV-UAV force, which optimizes FV in a distributed manner. In this diffusion strategy, each UAV optimizes the local parameter by exchanging information with neighbor UAVs, which achieve global load balancing in a distributed manner. Thirdly, we adopt the successive convex optimization method to update FU, which is a non-convex problem. The resultant force of FV and FU is used to control the UAVs’ motion. Simulation results show that the proposed algorithm outperforms the baseline algorithm on UAVs’ load balancing and UEs’ data rate fairness.     

Deep learning aided beam vector assignment for massive MIMO maritime communication considering location information and handover impact

Location aided beamforming (LAB) has been proposed as a potential solution to fast beam vector (BV) selection for massive multi-input multi-output (M-MIMO) maritime communication systems. However, the existing LAB schemes hardly consider the impact from handover. In this paper, we propose to apply the Gaussian anomaly detection (GAD) to improve the system performance, particularly in the handover zone, by exploiting the information of sea lane and locations of the user equipment (UE). Furthermore, inspired by the rationale of deep learning (DL) aided image processing methods, we first transform the BV assignment problem to a location-based artificial gray value classification problem, and then utilize a convolutional deep belief networks (CDBN) to predict the BV indices from a predefined codebook constituted by location-related feature matrices. In addition, we exploit GAD to rectify the training data of CDBN, such that the prediction on BV assignments can be more accurate to achieve a higher capacity than known methods. Finally, an extensive comparison with selected existing schemes through simulations demonstrates the effectiveness of the proposed scheme.     

RIS-assisted cell-free massive MIMO systems with reflection area: AP number reduction

Cell-free massive multiple-input multiple-output (MIMO) has been regarded as a promising technology due to high spectral efficiency. However, as large number of access points (APs) are deployed with fibers connecting to the central processing unit, the increase of energy consumption and hardware cost raise concerns. The reconfigurable intelligent surface (RIS) with impressing potential for low energy and cost finds a way to solve this problem. In this paper, we investigate the performance of the cell-free massive MIMO system with a RIS. As RIS can only reflect signals from the front, user equipments (UEs) and APs are divided into two categories according to their relative position with the RIS, i.e., one is on the reflection area of the RIS and the other is not. A closed-form approximation of the UE achievable downlink rate is derived. Based on it, we obtain the optimal RIS position and phase shift that can maximize the UE sum rate, through alternating optimization method. It is found that compared with the cell-free massive MIMO system without RIS, to achieve the same rate performance, the number of required AP in the RIS-assisted system can be significantly reduced. Moreover, as the RIS component increases, the number of required AP can be reduced almost linearly without rate reduction, which means the hardware and energy cost can be greatly cut down. Furthermore, from our simulation results, we can see that when users are densely distributed, the optimal location of the RIS should be closer to users. When users are uniformly distributed, the optimal position of RIS is close to the central position.     

Particle swarm optimization based network selection in heterogeneous wireless environment

Deployment of heterogeneous wireless networks is spreading throughout the world as users want to be connected anytime, anywhere, and anyhow. Meanwhile, users are increasingly interested in multimedia applications such as audio, video streaming and Voice over IP (VoIP), which require strict Quality of Service (QoS) support. Provisioning of Always Best Connected (ABC) network with such constraints is a challenging task. Considering the availability of various access technologies, it is difficult for a network operator to find reliable criteria to select the best network that ensures user satisfaction while reducing multiple network selection. Designing an efficient Network selection algorithm, in this type of environment, is an important research problem. In this paper, we propose a novel network selection algorithm utilizing signal strength, available bit rate, signal to noise ratio, achievable throughput, bit error rate and outage probability metrics as criteria for network selection. The selection metrics are combined with PSO for relative dynamic weight optimization. The proposed algorithm is implemented in a typical heterogeneous environment of EDGE (2.5G) and UMTS (3G). Switching rate of the user between available networks has been used as the performance metric. Moreover, a utility function is used to maintain desired QoS during transition between networks, which is measured in terms of the throughput. It is shown here that PSO based approach yields optimal network selection in heterogeneous wireless environment.     

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme.     

Equilibrium of a two-route system with delayed information feedback strategies

In intelligent transport system, some advanced information feedback strategies have been developed to reduce the oscillations and enhance the capacity on the road level. However, seldom strategies have considered the information delay and user equilibrium (UE) objective. Here, a derivative cost feedback strategy (DCFS) is proposed to reduce the influence of the delay, based on the UE principle. The simulation results show that in both no-delay and delay information cases, DCFS are the best and can make the system reaching the UE. Because DCFS can predict the trend of the travel cost.     

A dual phase genetic algorithm for improved initial access in 5G millimeter wave communication

Initial access (IA) in 5G millimeter wave (mmWave) communication is the problem of establishing a directional link between the base station (BS) and the user equipment (UE). For a multiple-input multiple-output (MIMO) system, where both the BS and UE have many antennas, finding the optimal beams can be prohibitively expensive in terms of delay and computation. In this work, we propose a meta-heuristic approach which is a modified dual-phase genetic algorithm. Since it is a meta-heuristic approach, it is generic and hence does not require extensive modifications to apply to different scenarios, it also does not require context information such as prior knowledge of channel state or statistics of user behavior. The proposed method is using iterative search for the optimal beams, but switch to a different fine-grained search phase on later iterations in order to quickly converge to the local optimum. The effect of this approach is analyzed in terms of capacity achieved vs number of transmit and receive antennas at BS and UE, codebook size, outage probability, total transmitted power, and other parameters specific to this particular dual-phase method. The proposed work has shown improved performance when compared to the existing similar work done in Souto et al. (2019) in terms of capacity achieved (2.12%), reduced power consumption (8.57%), and reduced IA delay (35% to 50%).     

3D beamforming in Intelligent Reflecting Surface (IRS)-assisted multi-user cognitive radio networks

In this paper, we consider a multi-user cognitive radio network (CRN) equipped with an intelligent reflecting surface (IRS). We examine the network performance by evaluating the fairness of the secondary system, which is satisfying the minimum required signal to interference and noise ratio (SINR) for each secondary user (SU). The minimum SINR of the SUs is maximized by joint optimization of the beamforming vector and three-dimensional beamforming (3DBF) angles at the secondary base station (SBS) and also the phase shifts of the IRS elements. This optimization problem is highly non-convex. To solve this problem, we utilize Dinkelbach’s algorithm along with an alternating optimization (AO) approach to achieve some sub-problems. Accordingly, by further applying a semi-definite relaxation method, we convert these sub-problems to equivalent convex forms and find a solution. Furthermore, analytically we propose an algorithm for optimizing 3DBF angles at the SBS. Through numerical results, the improvement of the sum SINR of the secondary system using the proposed method is illustrated. Moreover, it is shown that as the number of reflecting elements of IRS increases, the sum SINR significantly augments while satisfying fairness. Also, the convergence of the proposed algorithm is verified utilizing numerical results.     

Outage performance of CoMP-CNOMA networks with duplex mode selection

This paper investigates the outage performance of a downlink coordinated multipoint cooperative non-orthogonal multiple access (CoMP-CNOMA) network, where two near users cooperatively relay the information to a far user. It is assumed that the near users support both the half-duplex relaying mode and the full-duplex relaying mode. Since the full-duplex mode may be inferior to the half-duplex mode due to the residual self-interference under the full-duplex mode, we propose a duplex mode selection strategy for the near users to dynamically choose the duplex mode for relaying. Under the proposed duplex mode selection strategy, the closed form expressions for the outage probabilities of all the users are derived. The theoretical results match well with the Monte Carlo simulations. It is shown that the proposed duplex mode selection strategy can provide low outage probabilities of all the users simultaneously, while the existing duplex mode selection strategies either achieve higher outage probabilities of all the users or achieve slightly better outage performance of the far user at the sacrifice of much worse outage performance of the near users.     

A multi-timescale resource allocation algorithm based on self-learning for distributed fog radio access networks

Faced with limited network resources, diverse service requirements and complex network structures, how to efficiently allocate resources and improve network performances is an important issue that needs to be addressed in 5G or future 6G networks. In this paper, we propose a multi-timescale collaboration resource allocation algorithm for distributed fog radio access networks (F-RANs) based on self-learning. This algorithm uses a distributed computing architecture for parallel optimization and each optimization model includes large time-scale resource allocation and small time-scale resource scheduling. First, we establish a large time-scale resource allocation model based on long-term average information such as historical bandwidth requirements for each network slice in F-RAN by long short-term memory network (LSTM) to obtain its next period required bandwidth. Then, based on the allocated bandwidth, we establish a resource scheduling model based on short-term instantaneous information such as channel gain by reinforcement learning (RL) which can interact with the environment to realize adaptive resource scheduling. And the cumulative effects of small time-scale resource scheduling will trigger another round large time-scale resource reallocation. Thus, they constitute a self-learning resource allocation closed loop optimization. Simulation results show that compared with other algorithms, the proposed algorithm can significantly improve resource utilization.     

Energy Efficiency of User-Centric,Cell-Free Massive MIMO-OFDM with Instantaneous CSI

In the user-centric, cell-free, massive multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, a large number of deployed access points (APs) serve user equipment (UEs) simultaneously, using the same time–frequency resources, and the system is able to ensure fairness between each user; moreover, it is robust against fading caused by multi-path propagation. Existing studies assume that cell-free, massive MIMO is channel-hardened, the same as centralized massive MIMO, and these studies address power allocation and energy efficiency optimization based on the statistics information of each channel. In cell-free, massive MIMO systems, especially APs with only one antenna, the channel statistics information is not a complete substitute for the instantaneous channel state information (CSI) obtained via channel estimation. In this paper, we propose that energy efficiency is optimized by power allocation with instantaneous CSI in the user-centric, cell-free, massive MIMO-OFDM system, and we consider the effect of CSI exchanging between APs and the central processing unit. In addition, we design different resource block allocation schemes, so that user-centric, cell-free, massive MIMO-OFDM can support enhanced mobile broadband (eMBB) for high-speed communication and massive machine communication (mMTC) for massive device communication. The numerical results verify that the proposed energy efficiency optimization scheme, based on instantaneous CSI, outperforms the one with statistical information in both scenarios.     

Network selection algorithm based on link quality parameters for heterogeneous wireless networks

Next generation wireless networks consist of heterogeneous access technologies. In order to provide global ubiquitous communication, it is required to provide a framework in which user can move across multiple access interfaces while maintaining its ongoing communication at perceived quality of service. Given the scenario of multiple access networks, it is further required to select the optimum network out of multiple candidate networks to meet the requirements of the ongoing session. The selection of optimum network in such heterogeneous environment is generally based on network conditions and user preference. In this paper, we propose an algorithm for network selection based on averaged received signal strength, outage probability and distance. The proposed algorithm comprises of two stages. Assuming that network conditions are dominant in network selection, in first stage, overlapping region is identified through distance estimation. Network selection algorithm based on averaged received signal strength plus outage is invoked in second stage to select the optimum network. Numerical results are obtained through a simulation model of two disparate networks – GSM and UMTS. It has been shown that the proposed algorithm offers 68% improved performance in terms of network selection rate.     

Joint power allocation and user assignment for licensed/unlicensed band users in massive MIMO cellular systems

In this paper, we study the joint user assignment and power allocation for the defined utility function (central cell throughput) maximization in massive Multiple Input-Multiple Output (MIMO) cellular system coexistence with Wireless Fidelity (WiFi) network. Firstly, the power allocation of problem is formulated as a convex optimization. Unfortunately, the formulated problem has not a closed-form solution. For solving the mentioned problem, it is converted to three sub-problem based on the number of lemmas that are expressed. Due to two of these problems remain difficult to solve, this two sub-problem are relaxed. The Ellipsoid algorithm is an iterative algorithm that used for solving of the relaxed problems. In the following, joint user assignment and power allocation will be addressed, in which two approaches are proposed for solving. In the first approach, we propose an iterative algorithm that user assignment problem and power allocation problem are solved in each iteration. In the second approach, at first, users are assigned to licensed and unlicensed bands, then for the obtained arrangement, the power allocation problem is solved. The simulation results showed that the proposed algorithms are significantly close to the benchmark methods.     

Binary power allocation for cognitive radio networks with centralized and distributed user selection strategies

Motivated by the desire for efficient spectral utilization, we present a novel algorithm based on binary power allocation for sum rate maximization in Cognitive Radio Networks (CRN). At the core lies the idea of combining multi-user diversity gains with spectral sharing techniques and consequently maximizing the secondary user sum rate while maintaining a guaranteed quality of service (QoS) to the primary system. We consider a cognitive radio network consisting of multiple secondary transmitters and receivers communicating simultaneously in the presence of the primary system. Our analysis treats both uplink and downlink scenarios. We first present a distributed power allocation algorithm that attempts to maximize the throughput of the CRN. The algorithm is simple to implement, since a secondary user can decide to either transmit data or stay silent over the channel coherence time depending on a specified threshold, without affecting the primary users’ QoS. We then address the problem of user selection strategy in the context of CRN. Both centralized and distributed solutions are presented. Simulation results carried out based on a realistic network setting show promising results.     

Network sum-rate maximization via joint power allocation and antenna selection for clustered downlink/uplink NOMA networks

This paper considers the problem of joint power allocation and antenna selection (J-PA-AS) for downlink (DL) and uplink (UL) clustered non-orthogonal multiple-access (NOMA) networks. In particular, the goal is to perform antenna selection for each user cluster and allocate transmit power to its users so as to maximize the network sum-rate in the DL and UL directions, while satisfying quality-of-service (QoS) requirements. The formulated problem happens to be non-convex and NP-hard, and thus, there is no systematic or computationally-efficient approach to solve it directly. In turn, a low-complexity two-stage algorithm is proposed. Specifically, the first stage optimally solves the sum-rate maximizing power allocation for each (antenna, user cluster) pair. After that, antenna selection is optimally solved in polynomial-time complexity via the Kuhn–Munkres with backtracking (KMB) algorithm. Extensive simulation results are provided to validate the proposed algorithm, which is shown to efficiently yield the optimal network sum-rate in each link direction, in comparison to the optimal J-PA-AS scheme (solved via a global optimization package), and superior to other benchmark schemes. Light is also shed on the impact of spatial-diversity on the network sum-rate, where it is shown that the greater the number of base-station antennas is, the higher the network sum-rate, and the lower the outage events. Additionally, the significance of decoupling antenna selection in each link direction on the network sum-rate is highlighted. Lastly, the cases of imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) have been investigated, where it is demonstrated that spatial-diversity gains reduce the adverse effects of imperfect CSI and SIC on the network sum-rate.     

Contract Theory-Based Incentive Mechanism for Full Duplex Cooperative NOMA with SWIPT Communication Networks

Cooperative Non-Orthogonal Multiple Access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) communication can not only effectively improve the spectrum efficiency and energy efficiency of wireless networks but also extend their coverage. An important design issue is to incentivize a full duplex (FD) relaying center user to participate in the cooperative process and achieve a win–win situation for both the base station (BS) and the center user. Some private information of the center users are hidden from the BS in the network. A contract theory-based incentive mechanism under this asymmetric information scenario is applied to incentivize the center user to join the cooperative communication to maximize the BS’s benefit utility and to guarantee the center user’s expected payoff. In this work, we propose a matching theory-based Gale–Shapley algorithm to obtain the optimal strategy with low computation complexity in the multi-user pairing scenario. Simulation results indicate that the network performance of the proposed FD cooperative NOMA and SWIPT communication is much better than the conventional NOMA communication, and the benefit utility of the BS with the stable match strategy is nearly close to the multi-user pairing scenario with complete channel state information (CSI), while the center users get the satisfied expected payoffs.