Multiuser detection for the uplink of clustered 5G systems with universal frequency reuse

With 5G (Fifth generation) cellular communications, systems have to be able to cope with a massive increase of mobile devices and services and simultaneously improve the system’s spectral efficiency, as well as dealing with high interference levels. Base Station (BS) cooperation architectures jointly with block transmission techniques, such as OFDM (Orthogonal Frequency Division Multiplexing) for the downlink and SC-FDE (Single-Carrier with Frequency-Domain Equalization) for the uplink, are proven to be suitable for broadband wireless transmission systems. In BS cooperation systems MTs (Mobile Terminals) in adjacent cells share the same physical channel allowing the reducing of the frequency reuse and improving the spectral efficiency of cellular systems. In this paper we present a set of multiuser detection techniques for the uplink transmission in clustered architectures based on the C-RAN (Centralized-Radio Access Network) concept. We consider BS cooperation systems employing a universal frequency reuse approach. Our performance results demonstrate that by employing clustered techniques for the detection procedure it is possible to reduce substantially the signal processing complexity and the side information that must be transmitted by the backhaul structure.     

Multiuser scheduling on the LTE downlink with meta-heuristic approaches

In this paper, the issue of multi-user radio resource scheduling on the downlink of a Long Term Evolution (LTE) cellular communication system is addressed. An optimization model has been proposed earlier, where radio resources for multiple users are jointly allocated at the air-interface. It has been shown that an optimal solution to such a problem may provide reasonable gain over a simply greedy approach. However, the complexity of such an optimal approach could be prohibitively high. By exploiting meta-heuristic methods such as Genetic Algorithm (GA) and Simulated Annealing (SA), the results in this paper show that significant reduction in complexity can be obtained while achieving near-optimal solutions.     

Secrecy performance analysis of IRS-assisted D2D communication underlaying cellular network

In this paper, we evaluate the secrecy performance of an intelligent reflecting surface (IRS)-assisted device-to-device (D2D) communication in spectrum-shared cellular networks. To this end, we derive novel closed-form expressions for the secrecy outage probability (SOP) and the asymptotic SOP in the presence of multiple eavesdroppers. In the continue, in order to dynamically access the spectrum band of the licensed users, we define the optimization problem of secrecy spectrum resource allocation to minimize the SOP as a mixed-integer linear programming (MILP) problem. Then, the globally optimal solutions to this problem are obtained by using the Hungarian algorithm. Numerical analyses show that increasing the reflective elements of IRS can improve the secrecy performance.     

简谐振子免疫优化算法求解异构无线网络垂直切换判决问题

本文设计了垂直切换判决方案问题的数学模型, 给出了一种基于简谐振子免疫优化算法的垂直切换判决方案, 并与文献方案进行了对比实验实验结果表明, 本文方案能够有效地平衡网络负载、增加终端电池的生存时间, 具有较好的应用价值.     

Distributed spatial modulation with dynamic frequency allocation

This paper proposes a distributed implementation of spatial modulation (SM) using cognitive radios. In distributed spatial modulation (DSM), multiple relays form a virtual antenna array and assist a source to transmit its information to a destination. The source broadcasts its signal, which is independently demodulated by all the relays. Each of the relays then divides the received data in two parts: the first part is used to decide which one of the relays will be active, and the other part decides what data it will transmit to the destination. An analytical expression for symbol error probability is derived for DSM in independent and identically distributed (i.i.d.) Rayleigh fading channels. The analytical results are later compared with Monte Carlo simulations. Further, an instantaneous symbol error rate (SER) based selection combining is proposed to incorporate the direct link between the source and destination with existing DSM. Next, DSM implementation is extended to a cognitive network scenario where the source, relays, and destination are all cognitive radios. A dynamic frequency allocation scheme is proposed to improve the performance of DSM in this scenario. The frequency allocation is modeled through a bipartite graph with end-to-end SER as a weight function. The optimal frequency allocation problem is formulated as minimum weight perfect matching problem and is solved using the Hungarian method. Finally, numerical results are provided to illustrate the efficacy of the proposed scheme.     

Resource allocation for LTE-based cognitive radio network with queue stability and interference constraints

We consider the problem of interference management and resource allocation in a cognitive radio network (CRNs) where the licensed spectrum holders (primary users) share their spare capacity with the non-licensed spectrum holders (secondary users). Under such shared spectrum usage the transmissions of the secondary users should have a minimal impact on the quality of service (QoS) and the operating conditions of the primary users. Therefore, it is important to distinguish the two types of users, and formulate the problem of resource allocation considering hard restrictions on the user-perceived QoS (such as packet end-to-end delay and loss) and physical-layer channel characteristics (such as noise and interference) of the primary users. To achieve this goal, we propose to assign the bandwidth and transmission power to minimize the total buffer occupancy in the system subject to capacity constraints, queue stability constraints, and interference requirements of the primary users. We apply this approach for resource allocation in a CRN built upon a Third Generation Partnership Project (3GPP) long-term evolution (LTE) standard platform. Performance of the algorithm is evaluated using simulations in OPNET environment. The algorithm shows consistent performance improvement when compared with other relevant resource allocation techniques.     

Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).     

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%).     

On the dimensioning of cellular OFDMA networks

In this paper, we address the issue of cellular OFDMA network dimensioning. Network design consists of evaluating cell coverage and capacity and may involve many parameters related to environment, system configuration, and quality of service (QoS) requirements. In order to quickly study the impact of each of these parameters, analytical formulas are needed. The key function for network dimensioning is the Signal to Interference Ratio (SIR) distribution. We thus analyze in an original way the traditional issue of deriving outage probabilities in OFDMA cellular networks. Our study takes into account the joint effect of path-loss, shadowing, and fast fading effects. Starting from the Mean Instantaneous Capacity (MIC), we derive the effective SIR distribution as a function of the number of sub-carriers per sub-channel. Our formula, based on a fluid model approach, is easily computable and can be obtained for a mobile station (MS) located at any distance from its serving base station (BS). We validate our approach by comparing all results to Monte Carlo simulations performed in a hexagonal network, and we show how our analytical study can be used to analyze outage capacity, coverage holes, and network densification. The proposed framework is a powerful tool to study performances of cellular OFDMA networks (e.g. WiMAX, LTE).     

Wireless Network Optimization for Federated Learning with Model Compression in Hybrid VLC/RF Systems

In this paper, the optimization of network performance to support the deployment of federated learning (FL) is investigated. In particular, in the considered model, each user owns a machine learning (ML) model by training through its own dataset, and then transmits its ML parameters to a base station (BS) which aggregates the ML parameters to obtain a global ML model and transmits it to each user. Due to limited radio frequency (RF) resources, the number of users that participate in FL is restricted. Meanwhile, each user uploading and downloading the FL parameters may increase communication costs thus reducing the number of participating users. To this end, we propose to introduce visible light communication (VLC) as a supplement to RF and use compression methods to reduce the resources needed to transmit FL parameters over wireless links so as to further improve the communication efficiency and simultaneously optimize wireless network through user selection and resource allocation. This user selection and bandwidth allocation problem is formulated as an optimization problem whose goal is to minimize the training loss of FL. We first use a model compression method to reduce the size of FL model parameters that are transmitted over wireless links. Then, the optimization problem is separated into two subproblems. The first subproblem is a user selection problem with a given bandwidth allocation, which is solved by a traversal algorithm. The second subproblem is a bandwidth allocation problem with a given user selection, which is solved by a numerical method. The ultimate user selection and bandwidth allocation are obtained by iteratively compressing the model and solving these two subproblems. Simulation results show that the proposed FL algorithm can improve the accuracy of object recognition by up to 16.7% and improve the number of selected users by up to 68.7%, compared to a conventional FL algorithm using only RF.     

Distributed power allocation for spectrum sharing in mutually interfering wireless systems

Allocation of transmit power is critical for spectrum sharing and coexistence of mutually interfering wireless systems. In this paper we present a novel approach for allocation of transmit power, which is based on a non-greedy procedure that aims at maximizing transmission rate while also controlling interference levels. The proposed approach is fully distributed and requires no central control or coordination. Numerical results obtained from simulations are presented to illustrate the performance of the proposed approach in both sparse and dense environments. In sparse wireless environments, where there are fewer mutually interfering wireless links than available frequency bands, the proposed approach yields power allocations which outperform those obtained by applying alternative power allocation strategies, while in dense environments, where there are more interfering links than available frequency bands, the proposed approach yields power allocations with performance similar to those of existing power strategies. Thus, the distributed power allocation procedure based on the proposed approach is a drop-in replacement algorithm that yields better system throughput than existing algorithms for spectrum sharing.     

基于质心迭代估计的无线传感器网络节点定位算法

针对无线传感器网络非测距定位方法的应用,提出了基于质心迭代估计的节点定位算法.该算法首先计算当前连通信标节点所围成的平面质心的坐标及其与未知节点间的接收信号强度,然后用计算所得质心节点替代距离未知节点最远的连通信标节点,缩小连通信标节点所围成的平面,并通过多次迭代的方法提高节点定位精度.仿真实验结果表明,该算法的各项指标均为良好,适用于无线传感器网络的节点定位.     

Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems

Multicast hybrid precoding reaches a compromise among hardware complexity, transmission performance and wireless resource efficiency in massive MIMO systems. However, system security is extremely challenging with the appearance of eavesdroppers. Physical layer security (PLS) is a relatively effective approach to improve transmission and security performance for multicast massive MIMO wiretap systems. In this paper, we consider a transmitter with massive antennas transmits the secret signal to many legitimate users with multiple-antenna, while eavesdroppers attempt to eavesdrop the information. A fractional problem aims at maximizing sum secrecy rate is proposed to optimize secure hybrid precoding in multicast massive MIMO wiretap system. Because the proposed optimized model is an intractable non-convex problem, we equivalently transform the original problem into two suboptimal problems to separately optimize the secure analog precoding and secure digital precoding. Secure analog precoding is achieved by applying singular value decomposition (SVD) of secure channel. Then, employing semidefinite program (SDP), secure digital precoding with fixed secure analog precoding is obtained to ensure quality of service (QoS) of legitimate users and limit QoS of eavesdroppers. Complexity of the proposed SVD-SDP algorithm related to the number of transmitting antennas squared is lower compared with that of constant modulus precoding algorithm (CMPA) which is in connection with that number cubed. Simulation results illustrate that SVD-SDP algorithm brings higher sum secrecy rate than those of CMPA and SVD-SVD algorithm.     

A tree-structured DFT filter bank based spectrum detector for estimation of radio channel edge frequencies in cognitive radios

Cognitive Radio (CR) aims to provide efficient spectrum utilization in spectrum scarce wireless environments. One of the key CR functionalities is the spectrum sensing, which allows CRs to monitor the electromagnetic spectrum and detect unused bands of spectrum. Wideband spectrum sensing needs to be employed for better spectrum opportunity detection and interference avoidance both in the case of commercial and military applications. Accurate sensing needs to be employed for blocker detection in commercial systems such as LTE for the design of transmit/receive path. In military radios, the challenge lies in the robust detection of the location of the center frequencies and bandwidths of individual radio channels in the wideband input signal. In this paper, an energy detector based on tree-structured discrete Fourier transform based filter bank (TDFTFB) is proposed for detecting the edges of the channels in the spectrum. The proposed method is compared with the conventional wavelets based method for complexity and performance. The design example and simulations show that the gate count resource utilization of the proposed detection scheme is 22.9% lesser than the wavelets method at the cost of a slight degradation (0.5%) in detection accuracy. Over-the-air tests performed using Universal Software Radio Peripheral 2 (USRP2) and MATLAB/SIMULINK showed that the present method is not input specific whereas the conventional wavelet based approach depends on the spectral location of the input.     

On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission

The goal of this paper is to evaluate the performance of a proposed resource management approach that mitigates the co-channel interference (CCI) in non-orthogonal multiple access (NOMA) scenarios and maintains their enhanced spectral efficiency in distributed massive multiple input multiple output (d-massive MIMO) configurations as well. In this context, dedicated resource scheduling algorithms (RSAs) in power domain (PD) and frequency domain (FD) are studied in terms of resources’ orthogonality. Specifically, in PD case, adjusted power levels (denoted as PD-NOMA) per subcarrier and mobile terminal (MT) are assigned, while in FD case, the subcarriers are either orthogonal (FD orthogonal multiple access, FD-OMA) or non-orthogonal (FD-NOMA) to each other. The response of the d-massive MIMO system is evaluated statistically via independent Monte Carlo (MC) simulations considering a multicellular multi-user network topology and compared to typical multicellular MIMO configurations. In this framework, a simulation platform is implemented that integrates both the PD and FD RSAs. In PD, both intercell co-channel interference (Inter-CCI) and intracell co-channel interference (Intra-CCI) are modelled analytically in order to estimate the assigned power per subcarrier and MT. In the latter case (Intra-CCI), the worst-case scenario is assumed: a subcarrier can be assigned to multiple MTs (full spectral overlapping) leading to intense Intra-CCI. In FD, two subcarrier allocation approaches are considered: Pseudo-Random or maximum signal-to-noise ratio (MSNR). The simulations in both FD implementations (FD-NOMA, FD-OMA) show that, thanks to the proposed PD-NOMA scheme, each MT requires 1/4 of the maximum available power for downlink transmission. Moreover, in any of the investigated NOMA schemes, despite the intense Intra-CCI, roughly the same number of MTs as in the OMA case can access the network. Therefore, it is straightforward that, even in worst-case scenarios, the NOMA RSAs (i) are wisely exploiting the available resources; (ii) can inherently combat intense Intra-CCI and, in this way, maintain the system’s performance (number of MTs, power savings, resilience against CCI, computation complexity). Finally, it is worth noting that in contrary to typical MIMO configurations, the d-massive MIMO architecture alone can lead up to a 13.63% increase in the system’s capacity (10% maximum allowed blocking probability, 1 tier, 1 subcarrier per MT). In this case, the increased spatial separation that is achieved, along with the exploitation of NOMA RSAs, lead to a decreased CCI (both Intra- and Inter-); hence, SNR is improved and consequently the number of accepted MTs as well.     

A matching-theoretic approach to resource allocation in D2D-enabled downlink NOMA cellular networks

This paper investigates the resource allocation problem in non-orthogonal multiple-access (NOMA) cellular networks underlaid with OMA-based device-to-device (D2D) communication. This network architecture enjoys the intrinsic features of NOMA and D2D communications; namely, spectral efficiency, massive connectivity, and low-latency. Despite these indispensable features, the combination of NOMA and D2D communications exacerbates the resource allocation problem in cellular networks due to the tight coupling among their constraints and conflict over access to shared resources. The aim of our work is to maximize the downlink network sum-rate, while meeting the minimum rate requirements of the cellular tier and underlay D2D communication, and incorporating interference management as well as other practical constraints. To this end, many-to-many matching and difference-of-convex programming are employed to develop a holistic sub-channels and power allocation algorithmic solution. In addition to analyzing the properties of the proposed solution, its performance is benchmarked against an existing solution and the traditional OMA-based algorithm. The proposed solution demonstrates superiority in terms of network sum-rate, users’ connectivity, minimum rate satisfaction, fairness, and interference management, while maintaining acceptable computational complexity.     

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.     

Circular antenna design by adaptive position inheritance artificial bee colony algorithm

Reliable communication is a key factor in the development of the fifth generation wireless communication systems. To achieve reliable communication, designing desirable antenna arrays is the basis so as to realize wireless data transmission. Artificial bee colony (ABC) algorithm has shown its powerfulness in engineering design problems, though the performance of ABC could be improved further through information exchange of existing solutions. This paper proposes an adaptive position inheritance strategy for ABC algorithm. It adaptively decides the number of positions to be inherited attached with the evolutionary process of the algorithm. Take circular antenna as design example. The proposed algorithm is tested against with method of exhaustion. Simulation results show the usefulness of the proposed algorithm. Moreover, the proposed algorithm is also compared with state of the art algorithm under ABC paradigm. It turns out that the proposed algorithm attains better design solution. Thus, the proposed algorithm is useful for providing good design solutions in reliable communication.     

ECM and SAGE based joint estimation of timing and frequency offset for DMIMO–OFDM system

Distributed multiple-input multiple-output (DMIMO) technology is a key enabler of coverage extension and enhancement of link reliability in wireless networks through distributed spatial diversity. DMIMO employs classic relay channels in between the source and the destination to opportunistically form a virtual antenna array (VAA) for emulating cooperative diversity. Use of multiple antennas at the relays further increases capacity and reliability of the relay–destination channel through multiplexing and diversity of MIMO antennas respectively. In such network, the signal received at the destination is characterized by multiple timing offsets (MTO) due to different propagation delay and multiple carrier frequency offsets (MCFO) due to independent oscillators of the relays. Hence, synchronization becomes a crucial issue in DMIMO in order to realize the distributed coherence. In this paper, we address joint estimation of MCFO and MTO in DMIMO orthogonal frequency division multiplexing (OFDM) with MIMO configuration at the relays for estimate-and-forward (EF) relaying protocol. Two iterative algorithms, based on expectation conditional maximization (ECM) and space alternating generalized expectation–maximization (SAGE) are proposed for joint estimation in presence of inter carrier interference (ICI). The robustness of both the estimators to ICI is evaluated by mathematical analysis and supported by extensive simulations. The performance of the proposed estimators is assessed in terms of mean square error (MSE) and bit error rate (BER). The theoretical Cramer–Rao lower bound (CRLB) of estimator error variance is also derived.     

一种面向中继协作频谱感知系统的自适应全局最优化算法

该文研究了冗余中继, 次用户及中继用户数目, 检测门限, 信道传输错误率等因素对中继协作频谱感知系统性能的影响, 并提出一种新的自适应全局最优化算法.该算法基于获得最大无干扰功率的自适应中继选择方法, 确定备选认知中继集合;单个次用户以信道传输错误率最小为准则, 从备选认知中继集合中自适应选择最佳中继, 使总体检测率最大;在给定目标检测率的条件下, 以系统吞吐量最大为准则, 给出了自适应全局最优化算法.仿真实验结果表明新算法信道传输精度高, 信道吞吐量大, 节约带宽资源.