Cell-free massive multiple-input multiple-output (CF-mMIMO) has emerged as a new paradigm shift for future wireless communication networks to cope with the limitations of traditional cellular architectures. The deployment of CF-mMIMO networks comes with challenges, such as enabling reliable and low-latency fronthaul connections, particularly for isolated geographical areas where the fiber optics are costly to deploy or wireless fronthaul links are inaccessible. To address this, high altitude platform station (HAPS) emerges as a promising solution for fronthauling and ensuring cooperation tasks as a central processing unit (CPU) in the CF-mMIMO networks. The HAPS located in the stratosphere provides a wide coverage area, low latency, and cost-efficient fronthauling. Therefore, cell-free networks can achieve seamless connectivity between the distributed access points (APs) at a higher effective data rate by leveraging the HAPS. This paper investigates the sum rate maximization problem in the HAPS-assisted CF-mMIMO systems. Performance evaluations demonstrate that the proposed system design increases users’ data rates by leveraging both the high-capacity fronthauling supported by HAPS and the superior spatial multiplexing gain provided by CF-mMIMO. Furthermore, the study highlights the potential of HAPS-assisted architectures in providing services for users in underserved areas.

In the next generation wireless communication paradigm, hybrid communication strategies and vertical network integration are critical to ensure high-capacity and resilient connectivity. In this manner, we consider a high-altitude platform station (HAPS)-assisted bidirectional relaying system operating with hybrid terahertz (THz)/free-space optical (FSO) links in a multi-satellite and multi-ground station communication scenario. Unlike conventional setups, both the satellite and ground station nodes are dynamically selected, and bidirectional relaying is employed to enhance spectral efficiency. Realistic physical impairments, such as beam divergence and misalignment are incorporated into the system model, and system outage probability expression is derived to evaluate the overall performance under various configurations. The results show that the hybrid THz/FSO approach not only enhances reliability against pointing errors but also benefits significantly from multi-node diversity, making it a strong candidate for future reliable and high-capacity integrated non-terrestrial network architectures.

Mobile edge computing (MEC) has been a promising technology that leverages cloud computing capabilities at the network edge to address compute-intensive and delay-sensitive applications of mobile users with limited resources. Employing massive multiple-input multiple-output (mMIMO) and nonorthogonal multiple access (NOMA) in the MEC system facilitates simultaneous task offloading for multiple users, resulting in increased spectral efficiency and decreased offloading delay. Despite the great potential of the mMIMO-NOMA-based MEC system, offloading computation tasks to MEC servers can introduce inherent security concerns and vulnerabilities. We address a notable gap in the existing literature by investigating the effect of user selection to minimize the delay in MEC while enhancing the security of this framework. Specifically, this paper presents a user selection strategy for an uplink mMIMO-NOMA-based secure MEC system in the presence of a malicious eavesdropper (Eve) to minimize offloading and computing delays, subject to the transmit power, computing resource, and secrecy rate constraints with remote computing. We propose a two-step secure user selection algorithm and solve the optimization problem with the active-set algorithm. The simulation results demonstrate the effectiveness of the proposed user selection strategy on secure MEC with a malicious Eve by minimizing the task execution delay compared to the benchmark schemes.

Space has been reforming and this evolution brings new threats that, together with technological developments and malicious intent, can pose a major challenge. Space domain awareness (SDA), a new conceptual idea, has come to the forefront. It aims sensing, detection, identification and countermeasures by providing autonomy, intelligence and flexibility against potential threats in space. In this study, we first present an insightful and clear view of the new space. Secondly, we propose an integrated SDA and communication (ISDAC) system for attacker detection. We assume that the attacker has advanced communication capabilities to vary attack scenarios, such as random attacks on some receiver antennas. To track random patterns and meet SDA requirements, a lightweight convolutional neural network architecture is developed. The proposed ISDAC system shows superior and robust performance under 12 different super-attacker configurations with a detection accuracy of over 97.8%.

Millimetre wave (mmWave) communication is a promising technology which can fulfil the growing demands for spectrum for future wireless networks. One of the key areas for the development of the mmWave networks is the Internet of Things (IoT) communications within fifth generation (5G) and beyond 5G networks. For significant analysis and development of the compliant IoT systems through testbed implementation, current mmWave spectrum transceivers are too expensive when substantial number of the nodes is required by the IoT applications. Considering all the above, it is suggested to use Software Defined Radio (SDR) transceivers with a lower frequency band and with an increased distance between the nodes. The idea is to scale observation time and distance to emulate mmWave radio without actual mmWave hardware. Using scaling factors for the certain system parameters to keep the signal characteristics in accordance with the mmWave band makes it possible. This approach allows to develop mmWave IoT testbeds with significant improvement in the system scalability and cost-effectiveness without the need to transmit and receive the signal in the mmWave band. In this paper, the concept of SDR-based Hardware-in-the-loop (HIL) system combined with the observation time and distance scaling approach is proposed. As an example, a testbed with a simple Wireless Physical Network Coding scheme is implemented and demonstrated.

Nonorthogonal multiple access (NOMA)-based multiple-input-multiple-output (MIMO), which has the potential to provide both massive connectivity and high spectrum efficiency, is considered as one of the efficient techniques for sixth-generation (6G) wireless systems. In massive Internet of Things (IoT) networks, the user-set selection is crucial for enhancing the overall performance of NOMA-based systems when compared with orthogonal multiple access (OMA) techniques. In this article, we propose a user-set selection algorithm for IoT uplink transmission to improve the sum data rate of the NOMA-based MIMO systems. In order to exchange data between the selected IoT pairs, we propose to employ wireless physical-layer network coding (PNC) to further improve the spectral efficiency and reduce the delay to fulfill the requirements of future IoT applications. Performance evaluations are provided based on both the sum data rate and bit error rate for the proposed NOMA-based MIMO with PNC in the considered massive IoT scenarios.

The next-generation wireless networks are expected to support a number of computation-intensive and delay-sensitive applications such as virtual reality (VR), autonomous driving, telesurgery and unmanned aerial vehicles (UAVs). Since many devices are computation and power limited, mobile edge computing (MEC) has been deemed as a promising way to enhance computation service. In this paper, we propose a novel cooperative MEC that exploits the combination of non-orthogonal multiple access (NOMA) and multiple helpers. In the proposed system featuring a user, multiple helpers and a base station (BS), the user can simultaneously offload its computation-intensive tasks to the helpers using NOMA when there is no strong direct transmission link between the user and the BS. Then, the helpers can compute and offload these tasks through NOMA. Thus, in the proposed scheme, the computation and offloading modes at the helpers are determined with respect to the optimized task offloading decision factor. The simulation results show that the proposed NOMA-based cooperative MEC significantly increases the total offloading data under the latency constraints compared to the benchmark schemes featuring one helper with strong direct transmission link.

In this article, we investigate the resilience of Massive MIMO Physical Layer Network Coding (PNC) to jamming attack in both sub-6 GHz and millimeter-Wave (mmWave) systems in vehicular networks. Massive MIMO generally is resilient to jamming attacks, and we investigate the impact that PNC has on this resilience, if combined with Massive MIMO. The combination of Massive MIMO and PNC has shown a significant improvement in the bit error rate (BER) in our previous investigation. The corresponding framework is analysed against a barraging attack from a jammer, where the jamming channel is not known to the base station (BS), and the jammer can use any number of transmit antennas. Over Rayleigh channel, our simulation results reveal that Massive MIMO PNC performs better in the lower signal-to-noise ratio (SNR) regions to jamming attacks and this is achieved at twice the spectral efficiency. A similar performance is observed over mmWave channel.

Secure communication is critical in wireless networks as the networks are prone to eavesdropping from unintended nodes. To address this challenge, physical layer security (PLS) is being employed to combat information leakage. In this paper, we present the performance evaluations based on the secrecy rate and the secrecy outage probability for multiuser multiple-input multiple-output (MIMO) millimeter-wave (mmWave) communications by employing hybrid beamforming (HBF) at the base station, legitimate users and eavesdropper. Using a 3-dimensional mmWave channel model and uniform planar antenna arrays (UPA), we employ artificial noise (AN) beamforming to jam the channels of eavesdropper and to enhance the secrecy rate. The transmitter uses the minimum mean square error (MMSE) precoder to mitigate multiuser interference for the secure MIMO mmWave systems. It is shown that the overall system performance highly depends on the power allocation factor between AN and the signal of legitimate users.

Millimeter wave (mmWave) communication is a promising technology to fulfill the requirements of future wireless networks. It provides very large spectrum and a large number of antennas can be practicable due to the small wavelength to exploit the array gain. However, there are several challenges, restricting the utilization of mmWave, such as hardware complexity and power consumption. To overcome these challenges, hybrid analog/digital architecture providing lower dimensional beamspace multiple input multiple output (MIMO) system is used. For the hybrid architecture, beam selection techniques exploiting the sparse nature of the mmWave channel become significant. In this paper, we consider a downlink mmWave communication when the large number of antenna is utilized at the base station. For that system, we propose a beam selection and a correlation based user selection algorithms to maximize the sum data rate.

Massive Multiple-input Multiple-output (MIMO) is widely considered as a key enabler of the next-generation networks. In these systems, user selection strategies are important to achieve spatial diversity and maximize spectral efficiency. In this paper, a user selection algorithm is proposed with the reconstruction of the sparse Massive MIMO channel using Compressive Sensing (CS) algorithm. The proposed algorithm eliminates the users based on the channel correlation by employing the CS algorithm which reduces the feedback overhead in the system. The simulation results show that the proposed algorithm outperforms the traditional user selection algorithms in terms of sum data rate and computational complexity. Moreover, the effects of the sparsity level and feedback measurement on the performance are examined.

The need for software defined mobile networking (SDMN) increases to manage the complexity in communication networks for the fifth generation mobile networks and beyond, with increasing diverse demand on data traffic in wireless environments. The separation of the data and control planes offers flexibility in future networks with SDMN by taking into account to the wireless access problem in complex radio environments. In this paper, we propose an efficient joint routing and resource allocation algorithm to minimize the cost based on power consumption while satisfying both throughput and delay requirement of the flows under a given capacity of links in night time traffic through SDMN. The power consumption is determined based on the number of active OpenFlow switches and active ports in the network. In the proposed joint algorithm, we put the selected network components to sleep mode in order to reduce overall network power consumption. The performances of the proposed algorithm are illustrated under different throughput constraints in various network topologies and scenarios in night-time traffic.

In this paper, we address the resource allocation problem of device-to-device (D2D) communications underlaying orthogonal frequency division multiple access (OFDMA) based cellular systems by exploiting the efficiency that comes from an ensemble of graph theory and Knapsack problem. It is possible to construct the conflict graph of the D2D pairs by finding the maximal independent sets. Then, we use those independent sets as inputs to Knapsack problem iteratively in order to find D2D groups which allocate the subchannels. In Knapsack problem, we consider a maximum interference level that the base station is exposed at each subchannel. We illustrate that the proposed resource allocation method significantly outperforms graph coloring in terms of average data rate for the high number of underlaying D2D pairs in cellular systems.

Physical layer security is a promising approach to provide secure communications by considering the characteristics of wireless channels. In this work, we propose a secure multiple input single output (MISO) multiuser system with a quantized feedback link. We assume that eavesdropper is passive and its channel state information (CSI) is not available at transmitter. In order to disrupt reception of a passive eavesdropper, we schedule more than one legitimate user. For the sake of ensuring secure communication, the CSI of legitimate users has great impact on overall performance of secrecy sum capacity. The proposed solution applies a semi-orthogonal selection with a specific codebook to reduce the quantization errors for legitimate user side while disrupting the reception of the eavesdropper. The proposed solution improved secrecy sum capacity while reducing the feedback overhead for secure MISO multiser system.

This paper presents a hierarchical stream selection approach to deal with the interference in a heterogeneous network where different cell types are coexisting with each other to increase the sum capacity. Due to the variety of the transmit powers between the macro and small cells, interference levels are different. The proposed solution hierarchically selects the strongest streams of each cell with a contribution to the sum rate, while constructing the streams via singular value decomposition (SVD). In order to reduce the interference, the channel matrices of the remaining streams are projected orthogonally to the virtual transmit channel and virtual receive channel of the selected stream. The performance evaluations are obtained by considering different locations of small cells with respect to the macro cell. It is shown that the proposed method can dynamically select more streams in heterogeneous networks and achieve higher data rates compared to the existing algorithms.

In this paper, we focus on the radio resource management problem for the Orthogonal Frequency Division Multiple Access (OFDMA)-based mobile relay-enhanced heterogenous cellular networks. We combine mobile relaying and data offloading scenarios to increase the capacity of the system and cope with the mobile data traffic volume that is increased by the number of wireless subscribers accessing mobile data services. We propose network interface selection, relay selection and resource allocation solutions for this scenario and show effect of relaying and data offloading on the system capacity and on the ratio of satisfied users.

In the next generation of multicell networks, adaptive resource allocation techniques will play an important role to improve both quality of service and spectral efficiency. In order to employ distributed power allocation for multicell Orthogonal Frequency-Division Multiple Access (OFDMA) networks, the channel state information (CSI) belonging to all users is required to share among base stations. However, the amount of feedback increases with the number of users, base stations and subcarriers. Therefore, it is important to perform a selection at the user side for multicell networks. In this paper, we propose reduced feedback links by choosing the users based on their approximate signal to interference noise ratio (SINR). The performance of the reduced feedback links are illustrated in multicell OFDMA systems.

In this paper, a resource allocation problem for downlink multiple input-single output orthogonal frequency division multiple access (MISO-OFDMA) systems is investigated. The problem is defined as maximizing the minimum user rate with the constraints of total power and bit error rate (BER). Since it is difficult to obtain the optimal solution to this problem, a suboptimal but efficient solution is proposed based on zero forcing beamforming (ZFBF) to reduce computational complexity. The proposed algorithm is a fairness aware radio resource allocation algorithm that shares the resources equally among the users who has different distances from the Base Station (BS). The simulation results show that the proposed algorithm satisfies the fairness criterion having higher data rates compared to the existing algorithms.

In SDMA-OFDMA wireless communication systems, the feedback load increases with the number of users, subcarriers and antennas in the cell. In this paper, we propose two efficient reduced feedback algorithms by selecting the clusters at the user side. For each cluster, we select the users according to their norm and their orthogonality. We evaluate the performance of the user selection algorithms considering the quantization effect. We also design a specific codebook design to quantize CSI for the proposed criterion.

In this paper, an iterative EM based channel estimation algorithm is studied for STBC-OFDM systems. Compared to the time domain EM based channel estimation algorithm which needs matrix inversion, a frequency domain EM based channel estimation algorithm is proposed by estimating the channel coefficients for each subcarrier. The proposed channel estimation algorithm decreased the complexity without sacrificing the performance. The time domain and proposed frequency domain EM based channel estimation algorithms are compared in terms of bit error rate (BER), mean square error (MSE) and the number of iterations used in the EM algorithm.

We consider a multiple input single output antenna system with a feedback link. While most of the previous works have considered perfect feedback channels, in this paper, we evaluate the impact of a noisy feedback channel on the quality and the performance of the transmission. We compare the finite rate feedback scheme with the analog feedback schemes where the channel parameters are transmitted without quantization over the uplink channel. Two analog feedback schemes are considered : the feedback of the channel vector and the feedback of the normalized channel vector. We provide bounds on the performance and give numerical results. We have shown that at low to medium uplink SNR the analog feedback of the normalized channel vector outperforms the analog feedback of the channel vector. Depending on the range of uplink SNR, quantized feedback can perform poorly compared to analog feedback.

We evaluate the impact of multiple transmit antennas on the performance of multicast OFDM systems by proposing a suboptimal low complexity algorithm. Associated with a powerful erasure code, it is possible to increase the data rate of multicast OFDM systems by selecting for each subcarrier to the users with a good channel condition. We show that the resource allocation which includes the precoding vector selection, subcarrier allocation and bit loading is a difficult optimization problem. We propose a suboptimal algorithm to solve this problem by avoiding the optimization to reduce complexity. When the users are not symmetrically distributed around the base station, we add a fair scheduler to guarantee that each user receives the same amount of data. We present simulation results where we compare the proposed multicast systems with the classical multicast OFDM systems. When the users are symmetrically distributed around the base station, the gain is rather small, whereas when the users are non-symmetrically distributed, the proposed algorithm outperforms OFDM systems with subcarrier allocation.

For iterative decoding of multiple antenna systems concatenated with an outer error correcting code, it is important to use an a posteriori probability detector for the MIMO detection to achieve near capacity performance. To avoid full APP detection, we propose a reduced complexity detector based on breadth first algorithms. Although these algorithms are sub-optimal, we show that they can provide a good list of candidates for the APP calculation. Furthermore, by exploiting the a priori information delivered from the outer decoder, it is possible to decrease the MIMO detector complexity at each iteration. Using simulation results, we will compare the performance of the proposed detectors with the list sphere detector.