Transmit Power Of Base Station Research Articles

Optimizing secure multimedia communication in embedded systems a parallel convolutional neural network approach to RIS and D2D resource allocation

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods.

Dynamic computation scheduling for hybrid energy mobile edge computing networks

The rapid expansion of Mobile Edge Computing (MEC) is driven by the growing demand for resource-intensive applications within the Internet of Things. Computation offloading allows these applications to be executed at the network edge, but it leads to significant electricity expenses for network operators. To mitigate these costs, one promising solution is to power base stations (BSs) with a hybrid energy supply that combines unpredictable harvested energy with stable energy from the smart grid. This paper investigates joint computation scheduling for mobile devices (MDs) and resource allocation in a MEC network incorporating hybrid energy sources. Our objective is to maximize long-term time-averaged service utility by optimizing parameters such as BS battery supply, harvestable energy, CPU frequency, transmission power, task-partition factor, and MD-BS associations. To tackle this complex problem, we exploit the Lyapunov optimization framework to decompose it into deterministic subproblems for each time slot and propose an online network service utility maximization scheduling (NSUMS) algorithm. Experimental results show that our algorithm outperforms benchmark schemes in service utility and energy expenditure, improving the completion ratio by 32%, reducing the failure rate by 80%, and decreasing MD energy consumption by 28%.

Reconfigurable Intelligent Surface Assisted Wireless Network Rate Optimization Design

To enhance the communication rate, we deploy one active reconfigurable intelligent surface (RIS) and one passive RIS to cooperatively assist the downlink communication, while meeting the constraints of base station transmission power, total Active RIS power, and RIS unit modulus constraint. Due to the nonlinear coupling of multiple variables in the objective function, non-convexity is caused. Therefore, an alternating optimization algorithm (AO) is proposed, which uses semi-definite programming (SDP) based algorithm to optimize the transmit beamforming vector, ARIS reflection matrix, and PRIS diagonal phase shift matrix. The simulation results have verified the effectiveness of the proposed algorithm, which indicates that optimizing the coefficients can significantly improve the spectrum efficiency of wireless networks.

Efficient machine learning‐based hybrid resource allocation for device‐to‐device underlay communication in 5G wireless networks

SummaryDevices in close proximity can directly communicate with each other through device‐to‐device (D2D) communication, bypassing the need for base stations. To minimize the delay for D2D active clients, we use a sequential approach to reuse cellular‐type resources. Unlike conventional methods that focus solely on uplink or downlink resource distribution, our study introduces a novel optimization technique to maximize network throughput. Our proposal incorporates a hybrid approach that combines both downlink and uplink techniques for resource allocation. We utilize random forest and game theory algorithms to ensure smooth D2D communication while reducing interference between cellular and D2D pairings. Our hybrid structure effectively addresses challenges arising from intra‐ and inter‐cell interferences resulting from spectrum reusability and deployment. This approach also improves power control and quality of service. Traditionally, NP‐hard optimization solutions are proposed for mixed‐integer nonlinear problems. In our study, the critical stages include channel assignment and energy allocation. The objective problem in resource allocation considers parameters such as the transmission power of D2D active clients, cellular users, connection distance, base stations, and quality of service constraints. Our proposed hybrid method aims to enhance overall spectrum efficiency and network throughput. Simulated results demonstrate the superiority of our modified hybrid technique compared to existing joint resource allocation methods. This comprehensive approach represents a significant advancement in addressing the complexities associated with D2D communication, offering improved efficiency and performance in contemporary network environments.

An Improved Interference Mitigation Scheme for Femtocell Networks

Femtocell networks have gained significant attention in recent years due to their ability to improve indoor wireless coverage and capacity. However, they are susceptible to interference from neighboring cells, which significantly degrade their performance and quality of service to the end-user. The study introduced an adaptive power control technique that causes the power level of the femtocell base station to adjust dynamically to reduce the interference caused by adjacent femtocell networks, thereby adjusting the system bandwidth, carrier frequency, path loss and femtocell Base Station (BS) transmission power based on the request made by the users. The interference mitigation scheme was optimized by tuning the parameters of the power control algorithm, such as the transmit power levels and the interference thresholds. The algorithm was simulated with MATLAB simulation tools. Throughput, Signal to Interference Noise Ratio (SINR), coverage enhancement and user satisfaction were used as measurement metrics for the model using descriptive statistics. The results showed that the average performance obtained for the throughput increased by 25.88% from 3.40 X 1010 (bps) to 4.28 X 1010 (bps), and the SINR increased by 35.29% from 1.70 X 109 dB to 2.30 X109 dB for the benchmarked power control and improved power control techniques respectively. Coverage enhancement and user satisfaction were nearly 0, which indicates that the technique was of average performance. This study concluded that the new scheme enhances wider coverage to reach more users and mitigate interference while maintaining a higher level of femtocell user satisfaction.

Bayesian Optimization Framework for Channel Simulation-Based Base Station Placement and Transmission Power Design

Bayesian Optimization Framework for Channel Simulation-Based Base Station Placement and Transmission Power Design

A Neural Network-Based Random Access Protocol for Crowded Massive MIMO Systems.

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs' side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage.

Energy optimization for optimal location in 5G networks using improved Barnacles Mating Optimizer

In recent years, wireless sensor networks have been studied in many applications. One of the important issues studied in these networks is the optimal placement of cells in order to achieve optimal energy consumption. Therefore, intelligent algorithms have been used in most research to achieve this goal. In order to properly roll out 5G, the network must be able to handle the increase in data traffic. The essential aspects of resource allocation systems, such as scalability and throughput, are under too much stress due to the rising amount of traffic. In the present work, network planning is characterized by the optimal selection of the base station location and transmission power in 5G networks. Here, an Amended design of the Barnacles Mating Optimizer (ABMO) Algorithm is proposed and is employed to get higher efficiency in both terms of total connected users and transmit power saving for 5G networks. Therefore, the novelty of this study is in the use of an amended design of the Barnacles Mating Optimizer (ABMO) Algorithm. This amended design improves the accuracy and performance of the ABMO and makes it more effective in finding the best locations for base stations and changing the power level for 5G networks. The amended design also offers several advantages over some other state-of-the-art algorithms and makes the ABMO useful for improving 5G networks Simulations of the proposed ABMO algorithm are then compared with some other state-of-the-art algorithms and the results showed the right location and changing the range of the power level for the proposed method. The outcomes show that the suggested Amended Barnacles Mating Optimizer Algorithm statistically outperforms the original BMO, and some other methods based on DE, and RGA. The findings show that the t-value for BMO and the modified BMO is 4.339e−35 which is so better than that of DE and ABMOO with 1.926e−22, and for the RGA and ABMOO with 4.826e−32.

Analysis and optimization of uplink spectral efficiency in massive multiple-input and multiple-output

Fifth Generation (5G) specifications aims for data rate of 1 Gbps in high mobility and 10 Gbps in low mobility conditions, 15-30 bps/Hz of spectral efficiency with less than 1 milli second (ms) latency reduction. Massive multiple-input and multiple-output (Massive MIMO) is one of the promising technologies in 5G standard which offers a high spectral efficiency improvement. This work focus on the uplink scenario spectral efficiency in a Massive MIMO simulation network based on third generation partnership project (3GPP) and long term evolution (LTE) document of 5G. This work analyzes the spectral efficiency metric by simulating the 5G Massive MIMO network. Then, the research identified major constraint parameters; number of user antennas, K, number of base station antennas, M, transmission power, P, channel bandwidth, B, and coherence time, Tau_C and pilot time Tau_P which plays a significant role in varying this metric. The authors focus on improving the spectral efficiency by passing these constraint parameters through different meta-heurestic optimization algorithms, such as, convex optimization solver, White shark optimization (WSO) and Particle swarm optimization (PSO). The results show an overall, 1-10 percent of improvement of the parameter wnen compared with other research articles. The maximum value achieved is 49.84 bps/Hz, which is three times higher as per to the 3GPP and International Telecommunication Unioin (ITU) release document.

A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications

With the real restriction of packet size and transmission power, a joint UL-AoA (Uplink Angle of Arrival) and UL-TDOA (Uplink Time Difference of Arrival) positioning method is discussed for the Industrial Internet of Things (IIoT), which demands high accuracy of position information. One motivation of the UL positioning method of IIoT is an asymmetric network and uplink traffic is dominant in most identified scenarios. Further, IIoT sensors are power limited for low cost, compared to the transmission power of base stations and other normal user equipment. This paper considers an environment of a 5G-enabled IIoT network with massive Multiple input multiple output (MIMO) antennas, beamforming and millimeter operating bands. After reviewing downlink positioning reference signal (PRS) and uplink sounding reference signal for positioning (SRS-Pos), a general system model and a theoretical localization problem is derived under the joint positioning method. The provided simulation results help to investigate the impact of system configurations on positioning measurements. Further, through the test in this paper, it is proved that joint positioning outperforms that of the time only or angle only method.

Lower-Bound Capacity-Based Wireless Friendliness Evaluation for Walls as Reflectors

Indoor base stations (BSs) equipped with multiple-input multiple-output (MIMO) antenna arrays are commonly deployed in the vicinity of a wall. The wireless friendliness of the wall, determined by the intrinsic electromagnetic (EM) and physical properties of the wall material, significantly influences the indoor wireless performance and thus needs to be thoroughly considered during building design. In this article, for a rectangular room with a BS deployed near one of the walls, by deriving the asymptotic expression of lower-bound indoor wireless capacity of a UE location-specific channel, we reveal that the impact of the BS transmission power and that of the wall material properties on the lower-bound indoor capacity can be decoupled. More specifically, in our derived lower-bound indoor wireless capacity, the properties of the wall material are captured by the logarithmic eigenvalue summation (LES) and logarithmic eigenvalue product (LEP), which are both independent of the BS transmit signal-to-noise ratio (SNR). To simplify the wireless-friendliness evaluation of a wall by leveraging such decoupling, we derive both the LES and LEP in closed forms for a UE location-specific channel, and define the spatially averaged LES, the spatially averaged LEP, and the upper-bound outage probability (all over the room of interest) as new metrics for fast evaluating the wireless friendliness of the wall closest to the BS. Numerical results verify the effectiveness of the three proposed metrics and reveal the crucial impact of room settings and wall materials on the indoor capacity. The proposed approach will enable architects and civil engineers to quickly select building materials according to their wireless friendliness.

Fractional Frequency Reuse in Ultra Dense Networks

Ultra Dense Network (UDN) in which Base Stations (BSs) are deployed at an ultra high density is a promising network model of the future wireless generation. Due to ultra densification, reuse of frequency bands with an ultra high density is compulsory for this network. Conventionally, the research on frequency reuse technique such as Fractional Frequency Reuse (FFR) classifies the active users into only two groups. However, this approach is not suitable for UDNs where the signal experiences a huge amount of power loss over distances. Thus, this paper proposes a generalized model of FFR for UDNs where the active users are classified into more than two groups The paper introduces a simple approach to obtain the coverage probability of a typical user in the case of a general path loss model. In the case of stretched path loss model for UDNs, the closed-form expression of user coverage probability is derived. From the analytical and simulation results, it is stated that the proposed model can improve user performance without increasing BS power consumption. Furthermore, two additional interesting conclusions are found in this paper: (i) the user coverage probability increases to a peak before passing a decline when the density of BSs increases; (ii) an increase in BS transmission power may decrease the user performance.

Joint Active and Passive Beamforming for Reconfigurable Intelligent Surface Enhanced Symbiotic Radio System

This letter considers a symbiotic radio (SR) system enhanced by a reconfigurable intelligent surface (RIS). The active transmit beamforming of the base station (BS) and the passive beamforming of the RIS are jointly designed to minimize the BS's transmission power, subject to the signal-to-interference-plus-noise ratio constraints for decoding backscatter signal, and the rate constraint for primary communication. To solve the non-convex problem, we propose an efficient iterative algorithm based on alternating optimization and semi-definite relaxation. The algorithm's convergence and complexity are analyzed. Numerical results show that the RIS-enhanced SR achieves lower transmission power than SR without RIS.

Cluster oriented resource allocation and power optimisation for D2D network in cellular communications

This work proposes a cluster oriented channel assignment and difference of two convex functions (d.c.) programming based power optimisation algorithm for the downlink device-to-device (D2D) communication underlaying cellular networks. The authors' objective is to maximise D2D throughput while protecting the performance of existing cellular users (CUs) whose channel is reused among multiple D2D pairs, by imposing a minimum rate requirement constraint on each CU. The joint channel and power optimisation problem is a mixed integer non-linear programming problem that is NP-hard to solve. Therefore, a three stage solution is proposed: cluster formation to minimise the interference among the D2D pairs followed by an optimal channel assignment using the Hungarian algorithm and then an iterative power optimisation algorithm based on d.c. programming. Moreover, the transmission power of base station and D2D users is optimised on the same channel while considering the mutual interference among D2D pairs. Numerical results verify the effectiveness of the proposed resource allocation scheme in terms of the D2D sum rate and the number of successful transmission of D2D users. Moreover, the iterative power optimisation algorithm shows a fast convergence behaviour. In addition to this, the energy efficiency is also analysed with respect to various parameters.

Energy-Efficient and Secure Air-to-Ground Communication With Jittering UAV

Unmanned Aerial Vehicle (UAV)-enabled communication platform is recognized as a promising way for secure transmission in the air-to-air and air-to-ground (A2G) networks. However, the jitter feature of UAV platform due to the absence of fixed infrastructure, the inherent randomness of airflow, and the body vibration introduces non-negligible impact on establishing robust and energy-efficient secure communication links. In this paper, we investigate energy-saving and secure transmission in a downlink A2G wiretap system considering the impact of UAV jitter. Particularly, in order to minimize the total transmission power of UAV-mounted base station, a joint beamforming optimization of confidential signal and artificial noise signal is formulated with the constraints for secrecy performance of the worst scenario. Specifically, considering the impact of UAV jittering on the antenna array response, the worst scenario indicates the A2G wiretap system with the minimum legitimate data rate and the maximum eavesdropping data rate. The non-convex problem is then reformulated with linear approximation for the channel variations and linear matrix inequality alternation for the constraints. Then, semidefinite programming relaxation is adopted to obtain the optimal solutions for the reformulated problem. Furthermore, the problem and solution are extended to the multiuser downlink wiretap system. Finally, numerical results are provided to demonstrate the impact of key system parameters on power saving under the secure transmission constraints and the UAV jitter.

Techno-Economic and Energy Efficiency Analysis of Optimal Power Supply Solutions for Green Cellular Base Stations

The widespread proliferation of internet access, affordable wireless gadgets, the user data demand and the corresponding extended cellular networks entailing significant energy consumption and carbon footprints. With the added benefits of renewable energy harvesting (REH) technology, telecom base stations (BSs) are predominantly supplied by green power sources to reduce operational expenditure (OPEX) and atmospheric pollution with guaranteed quality of service (QoS). Accordingly, this paper examines the plausibility of optimal power supply solutions such as standalone solar photovoltaic (PV), hybrid PV/wind turbine (PV/WT), hybrid PV/diesel generator (DG) and hybrid PV/electric grid (PV/EG) to feed the Long Term Evolution (LTE) BSs pertaining to technical, economic and environmental aspects in Bangladesh. An extensive Monte-Carlo based simulations are performed to evaluate wireless network performance in terms of throughput, energy efficiency (EE), energy efficiency gain (EE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gain</sub> ), average energy savings, radio efficiency varying system bandwidth (B) and BS transmission power (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TX</sub> ) considering the dynamic behavior of traffic intensity and renewable energy (RE) generation profile. By leveraging the cell zooming technique and a green traffic steering framework endeavors to minimize the net present cost and maximize the average energy savings as well. The simulation results reveal that the cell zooming technique attained energy savings yielding up to 36% and improvement of EE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gain</sub> achieved about 23% with effective modeling of REH. Subsequently, a comprehensive comparison of the aforementioned schemes is pledged for further validation.

Transmit power optimisation in cellular networks with nomadic base stations

The increasing demand for cellular network capacity can be mitigated through the installation of nomadic eNodeB, which serve a temporal increase of traffic volume in specific area. When nomadic cells are deployed, the transmission power of neighbour base stations needs to be optimised to limit the inter-cell interferences. The authors analyse the problem of neighbourhood selection for the optimisation, to define what part of the networks needs to be reconfigured when new base station is added. They evaluate the iterative approach, with increasing range of neighbouring cells being reconfigured and propose a novel, sampling-based local TX power reconfiguration method, which is evaluated by a numerical model in both regular (honeycomb) topology and in realistic topology reflecting locations of cells in a city. The analysis confirms that the proposed algorithm allows to select small subset of neighbouring cells to be reconfigured (in majority of the cases < 10 cells), and achieve similar efficacy as global optimisation, with total network throughput different by < 1 % comparing to the global optimisation.

Energy-Efficient Virtual Radio Access Networks for Multi-Operators Cooperative Cellular Networks

5G networks will be characterized by massive densification of base stations (BSs). To minimize the network power consumption due to the increased BS deployment, we leverage emerging paradigms to propose BS energy-saving strategies applicable to 5G networks. We explore the potentials of virtualized radio access and core networks to enable spectrum sharing among mobile network operators (MNOs) by harnessing inter-band non-contiguous carrier aggregation. We propose the sleep-mode with efficient beamformers and spectrum-sharing (SEBS) strategy, which minimizes BS power consumption of cooperative MNOs. The licensed bandwidth of each MNO is partitioned into private and shared bands to provision active BSs with sufficient spectrum resources for serving all UEs. Moreover, an optimization problem is formulated to obtain the optimal intra- and inter-RAN beamforming vectors for efficient BS transmission power and improved user signal reception. Numerical simulations show that the proposed strategies yield significant reductions of total power consumption as compared to other schemes. We present different bandwidth sharing ratio formulations to motivate the cooperation among MNOs by the spectrum-user support trading. We also show that the trading does not always yield a higher spectral efficiency in the cooperative cellular network.

DAMS: D2D-assisted multimedia streaming service with minimized BS transmit power in cellular networks

This paper presents a scheme, named D2D-assisted Multimedia Streaming (DAMS), for selecting relay nodes to assist low-battery active users in a cellular network. The scheme also minimizes the transmission power of base stations (BSs) by transmitting data through D2D relay nodes (DRNs). The proposed scheme uses a maximum-weight bipartite matching and transmission power allocation algorithm to obtain optimal power allocation at the BS. We show that the energy required at the BS for transmitting D2D control signals is negligible in comparison to the transmission power that is saved. Simulation results verify the improved performance of the proposed scheme.

Power Control for Clustering Car-Following V2X Communication System With Non-Orthogonal Multiple Access

Vehicle clustering has been utilized for reducing the complexity of vehicle-to-everything (V2X) communications that would ultimately improve road traffic efficiency. Using a car-following model in a V2X communication system, we propose a vehicle clustering method for dynamically classifying vehicles and adjusting cluster size in real time. The especially important issue for the selected cluster-head vehicles (CHVs) can achieve an optimal trade-off between the CHVs' relative speed and power allocation. Furthermore, in order to balance the power allocation among the CHVs to further increase the downlink throughput, a power control approach for non-orthogonal multiple access (NOMA) is proposed. In this approach, the power allocation coefficients are obtained by maximizing the achievable rate while meeting the predefined target rate of each NOMA user. Finally, numerical simulations are provided to confirm the theoretical results and demonstrate the superior performance of the proposed approach. Note that through the numerical simulations, we can find the critical point of maximizing the minimum achievable rate among the CHVs at low transmission power of base station (BS).