Uplink User Equipments Research Articles

Machine Learning Based Throughput Enhancement in Fifth Generation Networks

The 5G Enhanced Mobile Broadband (eMBB) category offers faster data rates, network capacity, and user experiences than prior generations. This research aims to boost the 5G uplink User Equipment (UE) user data transfer rate. We use Python to build frameworks and analyze data. A 250-m-radius center-excited Picocell Base Station (PBS) is investigated to support 15 clients. Cell-range Poisson distribution determines user position. All UEs send Channel State Information (CSI) to the PBS, which evaluates signal transmission channel conditions. The study uses Rayleigh, Rician, free space path, and longdistance route loss models. This inquiry produces a channel state dataset and then it is formulated dataset is dynamic. For servicespecific requirements, UEs use K-means clustering. Clustering concatenates bandwidth, enhancing system efficiency and UE sum rate. Observations from simulation findings. UEs are grouped by channel gain, achievable data rate, and minimum servicerequired data rate. Users in cluster 3 achieve the highest cumulative rate of 9.09 Mbps after clustering with an average of 7.16 Mbps. Bandwidth concatenation increased system capacity, meeting each UEs service needs. After evaluating different clustering models performance criteria, K-means remains the best algorithm for the framework. The methodology was carefully designed to satisfy study goals. This research will investigate beamforming and dynamic clustering to improve user fairness and performance.

Network-Assisted Full-Duplex Cell-Free Massive MIMO: Spectral and Energy Efficiencies

We consider network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) systems, where full-duplex (FD) transmission is virtually realized via half-duplex (HD) hardware devices. The HD access points (APs) operating in uplink (UL) mode and those operating in downlink (DL) mode simultaneously serve DL and UL user equipments (UEs) in the same frequency bands. We comprehensively analyze the performance of NAFD CF-mMIMO from both a spectral efficiency (SE) and energy efficiency (EE) perspectives. Specifically, we propose a joint optimization approach that designs the AP mode assignment, power control, and large-scale fading (LSFD) weights to improve the sum SE and EE of NAFD CF-mMIMO systems. We formulate two mixed-integer nonconvex optimization problems of maximizing the sum SE and EE, under realistic power consumption models, and the constraints on minimum individual SE requirements, maximum transmit power at each DL AP and UL UE. The challenging formulated problems are transformed into tractable forms and two novel algorithms are proposed to solve them using successive convex approximation techniques. More importantly, our approach can be applied to jointly optimize power control and LSFD weights for maximizing the sum SE and EE of HD and FD CF-mMIMO systems, which, to date, has not been studied. Numerical results show that: (a) our joint optimization approach significantly outperforms the heuristic approaches in terms of both sum SE and EE; (b) in CF-mMIMO systems, the NAFD scheme can provide approximately 30% SE gains, while achieving a remarkable EE gain of up to 200% compared with the HD and FD schemes.

Intelligent Non-Orthogonal Beamforming With Large Self-Interference Cancellation Capability for Full-Duplex Multiuser Massive MIMO Systems

This work introduces a novel full-duplex hybrid beamforming (FD-HBF) technique for the millimeter-wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) systems, where a full-duplex (FD) base station (BS) simultaneously serves half-duplex (HD) downlink and uplink user equipments over the same frequency band. Our main goal is jointly enhancing the downlink/uplink sum-rate capacity via the successful cancellation of the strong self-interference (SI) power. Furthermore, FD-HBF remarkably reduces the hardware cost/complexity in the mMIMO systems by interconnecting the radio frequency (RF) and baseband (BB) stages via a low number of RF chains. First, the RF-stage is constructed via the slow time-varying angular information, where two schemes are proposed for both maximizing the intended signal power and canceling the SI power. Particularly, orthogonal RF beamformer (OBF) scheme only aims canceling the far-field component of SI, while non-orthogonal RF beamformer (NOBF) scheme applies perturbations to the orthogonal beams for also suppressing the near-field component of SI channel. Considering the high computational complexity during the search for optimal perturbations, we apply swarm intelligence to find the optimal perturbations. Second, the BB-stage is designed based on only the reduced-size effective intended channel matrices, where the BB precoder/combiner solutions are obtained via regularized zero-forcing (RZF) and minimum mean square error (MMSE). Hence, the proposed FD-HBF technique does not require the instantaneous SI channel knowledge. It is shown that FD-HBF with NOBF+MMSE achieves 78.1 dB SI cancellation (SIC) on its own. Additionally, FD-HBF with the practical antenna isolation can accomplish more than 130 dB SIC and reduce the SI power below the noise floor. The numerical results present that FD-HBF greatly improves the sum-rate capacity by approximately doubling it compared to its HD counterpart.

Correlated Downlink-to-Uplink Interference Suppression for Full-Duplex C-RAN Systems

In full-duplex cloud radio access network systems, the uplink received signal of each remote radio head (RRH) contains downlink-to-uplink (D2U) interference signal which is a linear combination of the transmit signals of nearby RRHs including itself. Since the fronthaul links that connect RRHs to a baseband processing unit (BBU) have finite capacity, the RRHs forward quantized versions of the uplink received signals to the BBU. To improve the fidelity of the quantization, it is important to suppress the impact of the D2U interference signals prior to the quantization. This paper tackles the problem of jointly optimizing the D2U interference suppression and fronthaul quantization strategies with the goal of maximizing the sum rate of uplink user equipments. To tackle the formulated non-convex problem, we derive an iterative algorithm based on the successive convex approximation approach. To save the complexity, a simpler algorithm, which non-iteratively pre-fixes the D2U interference suppression matrices, is also proposed. Extensive numerical results confirm the advantages of the proposed schemes compared to conventional schemes.

Energy and Traffic Aware Full-Duplex Communications for 5G Systems

In this paper, we consider the problem of resource allocation in a dense small-cell network. Each small-cell base station is powered by a renewable energy source and operates in the full-duplex mode. We account for the rate-dependent energy term for data decoding into the total energy consumption at the small-cell base station. Owing to this new energy term, the transmitter and receiver operations now draw the energy from a common source. For a new energy consumption model and high interference scenario, which arises due to full-duplex communications, we formulate an energy and load aware resource management optimization problem under the energy causality and total transmit power constraints of the small-cell base station and uplink user equipments. In particular, the problem minimizes the data queue length of each network user equipment by jointly designing the beamformers, power, and sub-carrier allocation and their scheduling. Owing to the non-convexity of the problem, a global solution is inefficient; thus, we opt for the successive parametric convex approximation method to obtain a sub-optimal solution. This method solves for the convex approximate of the non-convex problem in each iteration and leads to faster convergence. For practical implementation, we further develop a distributed algorithm by using the dual decomposition framework, which relies on limited exchange of information between the involved base stations. Numerical simulations compare the network scenario which accounts for uplink channel rate-dependent energy consumption with that which ignores it. Results advocate the need for redesigning of the resource allocation scheme. In addition, numerical simulations also validate the usefulness of full-duplex communications over the half-duplex communications in terms of minimizing the sum data queue length of the users.