Uplink Of Cellular System Research Articles

Resource Allocation for UAV-Assisted NOMA Systems With Dual Connectivity

In this letter, we consider a non-orthogonal multiple access (NOMA)-based uplink cellular system assisted by an unmanned aerial vehicle (UAV) through dual connectivity. To balance efficiency and fairness, we aim to maximize the sum weighted bit rate by jointly optimizing the three-dimensional UAV placement and power allocation. Considering the non-convexity of the original problem, we propose a hybrid offline-online algorithm: In the offline training, when the UAV placement is given, the property of fractional programming is utilized to obtain a sub-optimal power allocation solution; Based on the results of power allocation, the deep deterministic policy gradient algorithm is leveraged to learn the optimal UAV trajectory policy that can be deployed in an online fashion. Finally, simulations are conducted to show the efficiency of the proposed algorithm.

Proactive approach for preamble detection in 5G-NR PRACH using supervised machine learning and ensemble model

The physical random access channel (PRACH) is used in the uplink of cellular systems for initial access requests from the users. It is very hard to achieve low latency by implementing conventional methods in 5G. The performance of the system degrades when multiple users try to access the PRACH receiver with the same preamble signature, resulting in a collision of request signals and dual peak occurrence. In this paper, we used two machine learning classification technique models with signals samples as big data to obtain the best proactive approach. First, we implemented three supervised learning algorithms, Decision Tree Classification (DTC), naïve bayes (NB), and K-nearest neighbor (KNN) to classify the outcome based on two classes, labeled as ‘peak’ and ‘false peak’. For the second approach, we constructed a Bagged Tree Ensembler, using multiple learners which contributes to the reduction of the variance of DTC and comparing their asymptotes. The comparison shows that Ensembler method proves to be a better proactive approach for the stated problem.

Unsupervised Learning Based Emission-Aware Uplink Resource Allocation Scheme for Non-Orthogonal Multiple Access Systems

The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the ever-growing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMF-aware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques.

On the Performance Gain of NOMA Over OMA in Uplink Communication Systems

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by $\gamma$ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to $M$-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of $M$ for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, $M$, and the number of users, $K$, the ESG of NOMA over OMA increases linearly with both $M$ and $K$. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

셀룰러 상향링크에서 다중사용자 릴레이 전송 기법

셀룰러 시스템 상향링크에서 전송 신뢰도를 향상시킬 수 있는 신호 중첩과 메시지 전달 알고리즘 기반 다중 사용자 릴레이 전송 방식을 제안한다. 먼저 상향링크 릴레이 시스템의 자원 할당 구조를 제시하고 중첩 자원할당과 신뢰 전파 기반 메시지 전달 알고리즘을 도출한다. 모의실험을 통해 제안한 방법의 성능을 기존의 직교 자원할당을 하는 경우와 비교한다. A multiuser relaying scheme based on signal superposition and message passing algorithm is proposed to improve communication reliability in the uplink of cellular systems. We first present a resource allocation structure for the uplink relaying system, and then devise the overlapped resource allocation and the message passing algorithm based on belief propagation. Simulation results are provided to compare the performance of the proposed approach with that of the conventional orthogonal resource allocation.

A machine learning-based design of PRACH receiver in 5G

The physical random access channel (PRACH) in the uplink of cellular systems is used for the initial access requests from users. In fifth generation (5G) systems three different types of services are available, which are massive machine-type communication, enhanced mobile broadband communication, and ultra-reliable low-latency communication. Considering the tight requirements in terms of latency, a robust design of PRACH receiver is one of the priorities. In this paper we first explore the simple extension of a technique proposed for fourth generation (4G) systems to 5G. Then we propose the application of machine learning techniques to make the PRACH receiver more robust to false peaks, which are responsible of performance degradation in the extension of the 4G technique to 5G. Monte Carlo simulations are used to evaluate and compare the performance of the proposed algorithms.

Device-to-Device Communications Underlaying an Uplink SCMA System

Device-to-device (D2D) communication has been a potential solution to improve spectral efficiency of cellular systems due to frequency resource sharing. This paper considers D2D communications underlaying an uplink cellular system enabling sparse code multiple access (SCMA) technology, where the base station (BS) can decode the signals of cellular users without mutual interference. The demands for high data rate as well as low latency in massive connectivity is the main challenging requirement in D2D communications, along with ensuring the quality of service (QoS) for the on-running cellular user equipment (CUEs). To tackle the problem, the BS is designed to first assigns the codewords in a codebook to CUEs based on the lower bound of the achievable CUE rates, so that the sum rate (SR) of CUEs is maximized. With the usage of mutual-interference suppression in the SCMA-enabled system, the BS then attempts to maximize the SR of D2Ds in a transmission block through a joint power and resource allocation subject to the QoS requirements for both CUEs and D2Ds. This task is formulated as a mixed-integer non-convex programming. We propose a low-complexity two-phase algorithm of joint heuristic and inner approximation method to efficiently solve the problem. The numerical results verify that the codebook assignment problem based on the lower bound of SR is easily solved, and the proposed algorithm to maximize the SR of D2Ds outperforms existing methods.

A Space-time Diversity Scheme for Interference Alignment in Uplink Cellular Systems

A Space-time Diversity Scheme for Interference Alignment in Uplink Cellular Systems

Joint ZF and Partial ML Detection for Multi Cellular Network Environment

In this paper, we discuss joint detection schemes for an uplink cellular system when Base Station (BS) cooperation is possible for multi-cell users in a multi-cell scenario. Further, we analyze and evaluate the ML, ZF and ZF based SIC detection schemes. Although ML attains optimal performance, its complexity increases exponentially. Further, although ZF is simple, but exhibits the poor performance and SIC may give rise to error propagation. We propose a novel detection scheme, which combines the ZF detection and partial ML decoding scheme in order to improve the detection performance as well as to decrease the decoder complexity. Simulation results show that the proposed scheme attains nearly 2 dB reduction in the required SNR values to achieve the same BER performance. Further, the proposed scheme can be applied to MIMO detection for a single user and extended to other types of multi-user and multi-antenna based future smart BS cooperation.

제한된 궤환 채널 기반 상향링크 MIMO/FDD 시스템에서의 효율적인 간섭 정렬 기법

Assuming perfect channel state information (CSI), the conventional interference alignment (IA) algorithm in the uplink cellular system suppresses inter-cell interference (ICI) by aligning ICI to a randomly selected reference vector. However, IA in practice relies on limited feedback between base stations and users, resulting in residual ICI. In this paper, we propose the optimization of the reference vector that minimizes the upper-bound of residual ICI power. Secondly, the iterative IA that designs the direction of transmit and receive filter is proposed to minimize the residual ICI as well as maximize the desired signals. Moreover, we propose the user scheduling method combined with proposed IA schemes which provides the multiuser diversity gain in multi-cell environments. Finally, the performance gain of the proposed IA algorithms compared with the existing IA are analyzed and demonstrated by simulation results.

Interference Alignment for Uplink Cellular Systems with Limited Feedback

Assuming perfect channel state information, the existing interference alignment (IA) algorithm proposed in [2] suppresses inter-cell interference (ICI) by aligning ICI to a randomly selected reference vector. However, IA in practice relies on limited feedback, resulting in residual ICI. In this letter, we propose the optimization of the reference vector for regulating the residual ICI. Specifically, it is shown that the reference vector that minimizes an upper bound on the residual ICI power is the eigenvector corresponding to the largest eigenvalue of the sum of the interference-channel matrices multiplied by their corresponding Hermitian matrices. Moreover, the performance gain of the proposed IA algorithm compared with the existing one in [2] is analyzed and demonstrated by simulation to be significant.

Adaptive Coordinated Reception for Multicell MIMO Uplink

We study the coordinated reception amongst multiple base stations (BS) in the uplink cellular system, where each mobile station (MS) is served by an associated multiple BS set (MBS). Under the individual signal-to-interference-plus-noise ratio (SINR) constraint per MS, power control and receive beamforming are jointly optimized with adaptive MBS selection to minimize the total transmit power. An iterative optimization algorithm is presented accordingly. The joint optimization problem is nonconvex in general, but it can be optimally solved by the proposed algorithm as long as it is feasible. To find the optimum, the proposed algorithm requires the exhaustive search over all BSs per MS. To reduce the complexity in the large-scale cellular network, we propose simplified schemes where a subset of BSs is preselected based on the large-scale fading factors. By limiting the search in the subset, the complexity is reduced significantly. Although the obtained power vector with the simplified algorithm is not optimal, a performance close to the macronetwork full coordination can be achieved by carefully choosing the sizes of the pre-selected BS subset and MBS. The significant advantage is proven by simulations.

상향링크 셀룰러 시스템에서 그룹 탐색 기반의 분산동적채널할당 방법

주파수 재사용률 1을 사용하는 상향링크 셀룰러 시스템의 동적채널할당 (DCA : Dynamic Channel Allocation) 방법에서는 신규호가 채널 할당을 요구할 때 신규호의 SINR (Signal to Interference and Noise Ratio)이 요구된 SINR 보다 작거나 할당할 채널이 부족한 경우에 신규호 차단이 발생한다. 이러한 차단된 신규호에 대한 추가적인 채널할당을 위하여 인접한 셀에서 채널을 가져오게 된다. 이 경우에 동일 채널 간섭 (CCI: Co-Channel Interference)이 발생하게 되고 이로 인해 기존 호의 SINR이 저하되어 기존 호에 대한 채널 재할당을 해야 하는 경우가 생기게 된다. 따라서 신규호 블록율이 증가할수록 인접한 셀에서 채널을 가져오는 경우의 수를 증가시킴으로써 채널 재할당 과정은 복잡한 연산과정이 필요한 NP-hard 문제가 된다. 그 문제를 해결하기 위하여 본 논문에서는 차단된 신규호를 감소시킴으로써 추가적인 채널할당을 위하여 인접한 셀에서 채널을 가져오는 횟수를 감소시키고 그 결과로 채널 재할당 횟수가 감소되어 복잡한 연산과정을 피할 수 있는 Group Search-based DCA 방법을 제안한다. 제안한 방법은 신규호의 차단율을 감소시키기 위하여 신규호에 대한 채널할당시 단말이 속해 있는 셀뿐만 아니라 그 주변의 셀까지를 한 그룹으로 묶어서 채널 탐색 범위를 확장하는 방법을 사용한다. 시뮬레이션 결과에서 제안한 Group Search-based DCA가 탐색 범위를 단말이 속한 셀로 한정하는 Single Search-based DCA보다 신규호 차단률과 시스템 수율 성능을 향상시킴을 확인할 수 있었다. In DCA (Dynamic Channel Allocation) scheme of uplink cellular system appling a frequency reuse factor of one, when a new call requests a channel, the new call will be blocked if its SINR (Carrier to Noise and Interference Ratio) is less than the required SINR or there is no available channel. The additional channel allocation for the blocked new call can be performed with channel borrowing in the adjacent cells. The channel borrowing causes the CCI (Co-Channel Interference), thus the SINR of the existing calls is deteriorated and the channel reallocation for the existing calls is required. As a result, the channel borrowing leads to a complex calculation so that it is a NP-hard problem. Therefore, to overcome the problem, we propose a novel Group Search-based DCA scheme which decreases the number of the blocked new calls and then reduces the number of the channel reallocation by the channel borrowing for the blocked new calls. The proposed scheme searches the all channels in a group of the adjacent cells and home cell at the same time in order to minimizes the number of the blocked new calls. The simulation results show that proposed Group Search-based DCA scheme provides better new call blocking probability and system throughput than the existing Single Search-based DCA scheme which searches only the channels in home cell.

Distributed Detection for Macrodiversity Enhanced Cellular Communication Systems

This paper considers the design of soft decision distributed detection schemes for achieving macrodiversity in uplink cellular communication systems with and without channel knowledge at the fusion center. Distributed detection macrodiversity (DDM) techniques are proposed where the quatizer thresholds of local detectors at base stations (radio access points) are optimized using a numerical global optimization algorithm called adaptive simulated annealing (ASA). The performance of such DDM schemes is studied for systems with and without channel coding, where the base stations employ multiple receive antennas over quasi-static Rayleigh fading channels. It is shown that properly designed quantizers for the local detectors, with a few resolution bits can make the performance very close to optimal macrodiversity that requires a much larger bandwidth from the connection links to the fusion center.

Self-Encoded Multiple Access Multiuser Convolutional Codes in Uplink and Downlink Cellular Systems

Self-encoded spread spectrum eliminates the need for traditional pseudo noise (PN) code generators. In a self-encoded multiple access (SEMA) system, the number of users is not limited by the number of available sequences, unlike code division multiple access (CDMA) systems that employ PN codes such as m-, Gold or Kassami sequences. SEMA provides a convenient way of supporting multi-rate, multi-level grades of service in multimedia communications and prioritized heterogeneous networking systems. In this paper, we propose multiuser convolutional channel coding in SEMA that provides fewer cross-correlations among users and thereby reducing multiple access interference (MAI). We analyze SEMA multiuser convolutional coding in additive white Gaussian noise (AWGN) channels as well as fading channels. Our analysis includes downlink synchronous system as well as asynchronous system such as uplink mobile-to-base station communication.