Remote and resource-constrained Internet-of Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.

In this paper, we address the resource allocation problem for task offloading from Internet of Things (IoT) devices to a non-terrestrial network. The proposed architecture contains clusters of IoT devices that can either execute their computing tasks locally or offload them to a dedicated unmanned aerial vehicle (UAV) functioning as a multi-access edge computing (MEC) server. The UAV can process the tasks itself or further offload them to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite within line-of-sight for remote computing. We formulate an optimization problem that aims to minimize the weighted sum of the total task-execution delay and the energy consumption of the IoT devices. Due to non-convexity of the problem and the inherent complexity-performance trade-off in optimization algorithms, we propose a set of low-complexity solutions. These include optimal methods based on convex subproblem decomposition and a greedy heuristic guided by convex optimization criteria. The framework jointly optimizes the computing resources and transmission power of IoT devices, the digital precoders and combiners at the UAV, the computing resources at the remote nodes (UAV, HAPS, and LEO), as well as task offloading decisions and subchannel allocation through a one-shot block coordinate descent approach. Simulation results highlight the performance gains of the proposed methods, demonstrating the impact of algorithmic complexity on key system metrics and the benefits of incorporating multiple non-terrestrial nodes compared to architectures lacking such capabilities.

This paper investigates the design of distributed precoding for multi-satellite massive MIMO transmissions. We first conduct a detailed analysis of the transceiver model, in which delay and Doppler precompensation is introduced to ensure coherent transmission. In this analysis, we examine the impact of precompensation errors on the transmission model, emphasize the near-independence of inter-satellite interference, and ultimately derive the received signal model. Based on such signal model, we formulate an approximate expected rate maximization problem that considers both statistical channel state information (sCSI) and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation fails to maintain equivalence in the considered scenario. To address this, we introduce an equivalent covariance decomposition-based WMMSE (CDWMMSE) formulation derived based on channel covariance matrix decomposition. By exploiting the channel characteristics, we develop a low-complexity decomposition method and propose an optimization algorithm. To further reduce computational complexity, we introduce a model-driven scalable deep learning (DL) approach that leverages the equivariance of the mapping from sCSI to the unknown variables in the optimal closed-form solution, enhancing performance through novel dense Transformer network and scaling-invariant loss function design. Simulation results validate the effectiveness and robustness of the proposed method in some practical scenarios. We also demonstrate that the DL approach can adapt to dynamic settings with varying numbers of users and satellites.

Mobile edge computing (MEC) has emerged as a promising technology to extend the functionality of end-users' wireless devices while prolonging their battery life by offloading computationally intensive tasks to remote edge servers. However, the inherent broadcast nature of wireless transmission during offloading introduces notable security challenges. To address this issue, we propose leveraging intelligent reflecting surface (IRS) technology to enhance physical layer security (PLS). Nevertheless, attaining high PLS for all users in dense networks with multiple malicious terminals is challenging. In this paper, we investigate the physical layer encryption (PLE) to complement the PLS in enabling secure wireless transmission. Since such encryption and decryption processes require computation resources, we aim to optimize the encryption decision, offloading decision, as well as wireless and computing resource allocations. Our objective is to minimize the maximum weighted energy consumption while satisfying practical constraints, including limited computing and wireless resources, fulfilling minimum user rate requirements, and complying with IRS conditions. To tackle the non-convex objective and constraints, we explore the utilization of bisection search and successive convex approximation (SCA) methods. Our numerical results confirm the efficiency of the proposed design in terms of energy consumption and network capacity within a secure MEC network.

peer reviewed

This paper presents a digital beamforming architecture based on the discrete Fourier transform, designed for medium-Earth orbit satellite payloads to serve multiple ground users. The system leverages a 16×16 16-point two-dimensional fast Fourier transform (2DFFT) to address the growing demand for high-speed data traffic and adaptable satellite communications. The architecture features a routing algorithm for flexible user allocation to any beam position and a cluster-based linear precoding approach to reduce resource and power consumption. Two versions of the 2DFFT module—quantized and non-quantized—are compared in terms of resource usage, power consumption, and performance. Experimental results show that the non-quantized version provides better power efficiency, while the quantized version removes the need for DSP blocks.

This paper studies the distributed precoding design for multi-satellite massive MIMO transmission. We first conduct a detailed analysis of the transceiver process, examining the effects of delay and Doppler compensation errors and emphasizing the nearly independent nature of inter-satellite interference. Based on the derived signal model, an approximate expected sum rate maximization problem is formulated, incorporating statistical channel state information and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation cannot hold equivalence in the considered scenario. To address this, we propose a modified WMMSE formulation leveraging channel covariance matrix decomposition. By exploiting channel characteristics, a low-complexity decomposition method is then developed, accompanied by an efficient algorithm. Simulation results validate the effectiveness and robustness of the proposed method in some practical simulated scenarios.

This paper investigates downlink transmission in 5G Integrated Satellite-Terrestrial Networks (ISTNs) supporting automotive users (UEs) in urban environments, where base stations (BSs) and Low Earth Orbit (LEO) satellites (LSats) cooperate to serve moving UEs over shared C-band frequency carriers. Urban settings, characterized by dense obstructions, together with UE mobility, and the dynamic movement and coverage of LSats pose significant challenges to user association and resource allocation. To address these challenges, we formulate a multi-objective optimization problem designed to improve both throughput and seamless handover (HO). Particularly, the formulated problem balances sum-rate (SR) maximization and connection change (CC) minimization through a weighted trade-off by jointly optimizing power allocation and BS-UE/LSat-UE associations over a given time window. This is a mixed-integer and non-convex problem which is inherently difficult to solve. To solve this problem efficiently, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) technique. Furthermore, we introduce a practical prediction-based algorithm capable of providing efficient solutions in real-world implementations. Especially, the simulations use arealistic 3D map of Londonand UE routes obtained from the Google Navigator application to ensure practical examination. Thanks to these realistic data, the simulation results can show valuable insights into the link budget assessment in urban areas due to the impact of buildings on transmission links under the blockage, reflection, and diffraction effects. Furthermore, the numerical results demonstrate the effectiveness of our proposed algorithms in terms of SR and the CC-number compared to the greedy and benchmark algorithms.

The escalating number of wireless users requiring different services, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC), has led to exploring non-orthogonal multiplexing methods like heterogeneous non-orthogonal multiple access (H-NOMA). This method allows users demanding divergent services to share the same resources. However, implementing the H-NOMA scheme faces major resource management challenges due to unpredictable interference caused by the random access mechanism of mMTC users. To address this issue, this paper proposes a joint optimization and cooperative multi-agent (MA) deep reinforcement learning-based resource allocation mechanism, aimed at maximizing the energy efficiency (EE) of H-NOMA-based networks. Specifically, this work initially establishes an optimization framework capable of determining the optimal power allocation for any specific sub-channel assignment (SA) setting for all users. Based on that, a cooperative MA double deep Q network (CMADDQN) scheme is carefully designed at the base station to conduct SA among users. In addition, a distributed full learning-based approach using MADDQN for both SA and power allocation is also designed for comparison purposes. Simulation results show that the proposed joint optimization and machine learning method outperforms the solely-learning-based approach and other benchmark schemes in terms of convergence rate and EE performance.

In this work, we consider a typical scenario in a harsh urban propagation environment which is typical for a smart city scenario where multiple reconfigurable intelligent surfaces (RISs) are deployed in different hotspot areas to overcome signal blockage between the base station and users. Our goal is to ensure uninterrupted service availability to users in different hotspot areas regardless of their location. Consistent service availability can be achieved by guaranteeing that each RIS deployed in a hotspot area can support a certain number of users. This plays a critical role in smart city applications in the context of emergency communications and ubiquitous connectivity since the design ensures service availability to as many users as possible in all relevant locations. Taking into consideration the challenges in obtaining channel state information (CSI) given the passive nature of RIS and dynamic environments, we formulate a robust fairness problem to maximize the minimum expected number of served users in proximity to each RIS while considering the available transmit power and the worst-case quality of service (QoS) constraints within the bounded CSI error model framework. The resulting problem is a mixed integer non-convex program which is highly coupled and challenging to solve in polynomial time. Thus, we resort to binary variable relaxation, convex approximation techniques, and alternating optimization to tackle the problem. Additionally, we handle the semi-infinite uncertainty constraints by employing the S-procedure and general sign-definiteness. Simulation results demonstrate the effectiveness of the proposed design in obtaining consistent and reliable service in different hotspot areas compared to the relevant benchmark schemes. In addition, the proposed design shows flexibility in serving users with their target QoS given different channel uncertainty levels.

This paper proposes a joint optimization framework for energy-efficient linear precoding and feeder-link-beam matching design in a multi-gateway multi-beam bent-pipe satellite communication system. The proposed scheme jointly optimizes the precoding vectors at the gateway antennas and amplifying-and-matching mechanism at the satellite to maximize the system-weighted energy efficiency under the transmit power budget constraint. The technical designs are formulated into a non-convex sparsity problem consisting of a fractional-form objective function and sparsity-related constraints. To address these challenges, two iterative efficient designs are proposed by utilizing the concepts of Dinkelbach's method and the compressed-sensing approach. The simulation results demonstrate the effectiveness of the proposed scheme compared to another benchmark method.

This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT) beamforming (BF) technique. Regarding serving users at their target SINRs and minimizing the total transmit power, the scheme aims to efficiently determine satellites for users to associate with and activate the best cluster of beams together with optimizing LP for every satellite-to-user transmission. These technical objectives are first framed as a complex mixed-integer programming (MIP) challenge. To tackle this, we reformulate it into a joint cluster association and LP design problem. Then, by theoretically analyzing the duality relationship between downlink and uplink transmissions, we develop an efficient iterative method to identify the optimal solution. Additionally, a simpler duality approach for rapid beam selection and LP design is presented for comparison purposes. Simulation results underscore the effectiveness of our proposed schemes across various settings.

This paper examines integrated satellite-terrestrial networks (ISTNs) in urban environments, where terrestrial networks (TNs) and non-terrestrial networks (NTNs) share the same frequency band in the C-band which is considered the promising band for both systems. The dynamic issues in ISTNs, arising from the movement of low Earth orbit satellites (LEOSats) and the mobility of users (UEs), are addressed. The goal is to maximize the sum rate by optimizing link selection for UEs over time. To tackle this challenge, an efficient iterative algorithm is developed. Simulations using a realistic 3D map provide valuable insights into the impact of urban environments on ISTNs and also demonstrates the effectiveness of the proposed algorithm.

This paper delves into the dynamics of resource allocation in ultra-dense Low Earth Orbit (LEO) satellite networks within a cell-free massive MIMO framework, focusing on the impact of residual synchronization errors. We conduct various analyses to understand how these errors - encompassing time, phase, and frequency - influence the Signal-to-Interference-plus-Noise Ratio (SINR) and the average number of satellite links connected to each user. Our approach measures the effects of these remaining synchronization errors and uses these values to inform and optimize power and resource allocation decisions. The study reveals that as synchronization errors increase, the number of effective satellite links to users diminishes, consequently reducing the number of satellites actively connected to each user. This research not only highlights the critical impact of synchronization errors on network performance but also demonstrates how advanced knowledge of these error variances can significantly enhance resource allocation strategies and network efficiency in future ultra-dense LEO satellite systems.

The advent of Satellite as a Service (SaaS) platforms has empowered satellite service providers (SPs) to rent portions of satellite capacity from infrastructure providers (IPs) to cater to the diverse demands of their users across multiple satellite services. To effectively manage costs and maintain a high Quality of Experience (QoE) for numerous concurrent connections, SPs should secure flexible capacity from IPs. However, the irregular and unpredictable nature of traffic demands from various applications complicates the capacity-renting framework. This study presents a dynamic capacity allocation framework that efficiently handles diverse traffic flows with varying arrival rates, aiming to minimize rental costs while meeting blocking probability and QoE requirements. Utilizing theMt/Mt/1 queuing model and a continuous-time Markov chain, the technical designs are framed as a statistical optimization problem. In this context, the system waiting-queue lengths are estimated using the transient probabilities of Kolmogorov equations. Subsequently, cumulative distribution functions are employed to re-formulate this stochastic optimization problem into a convex form, which can be tackled through the Lagrangian duality method.Through extensive simulations and numerical assessments, we illustrate our method’s efficacy, with the proposed algorithm outperforming benchmarks by reducing costs by up to 9.85% and 3.1%.

This paper investigates the uplink transmission of an integrated satellite-terrestrial network, wherein the low-earth-orbit (LEO) satellites provide backhaul services to isolated cellular base stations (BSs) for forwarding mobile user (UE) data to the core network. In this integrated system, the high mobility of LEO satellites (LEOSats) introduces significant challenges in managing radio resource allocation (RA), as well as the associations between UEs, BSs, and LEOSats for supporting users’ demands efficiently, while also dynamically balancing the capacity of UE-BS access and BS-LEO backhaul links. Regarding these critical issues, the paper aims to jointly optimize the two-tier UE-BS and BS-LEOSat association, sub-channel assignment, bandwidth allocation, and power control to meet users’ demands in the shortest transmission time. This optimization problem, however, falls into the category of mixed-integer non-convex programming, making it very challenging and requiring advanced solution techniques to find optimal solutions. To tackle this complex problem efficiently, we first develop an iterative centralized algorithm by utilizing convex approximation and compressed-sensing-based methods to deal with binary variables. Furthermore, for practical implementation and to offload computation from the central processing node, we propose a Dec-Alg that can be implemented in parallel at local controllers and achieve efficient solutions. Numerical results are also illustrated to strengthen the effectiveness of our proposed algorithms compared to traditional greedy and benchmark algorithms.

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

The space communications industry is challenged to develop a technology that can deliver broadband services to user terminals equipped with miniature antennas, such as handheld devices. One potential solution to establish links with ground users is the deployment of massive antennas in one single spacecraft. However, this is not cost-effective. Aligning with recent NewSpace activities directed toward miniaturization, mass production, and a significant reduction in spacecraft launch costs, an alternative could be distributed beamforming from multiple satellites. In this context, we propose a distributed beamforming modeling technique for wideband signals. We also consider the statistical behavior of the relative geometry of the swarm nodes. The paper assesses the proposed technique via computer simulations, providing interesting results on the beamforming gains in terms of power and the security of the communication against potential eavesdroppers at non-intended pointing angles. This approach paves the way for further exploration of wideband distributed beamforming from satellite swarms in several future communication applications.

Medium Earth orbit (MEO) constellation is an appealing solution between geostationary equatorial orbit (GEO) and lower Earth orbit (LEO) in terms of latency and number of satellites required. On-board processing of digital beam-former in MEO satellites is an efficient solution for achieving wider bandwidth, increased flexibility, and lower latency. Power constraints, however, make it impractical to digitally create thousands of beams at once. In this paper, area-power efficient digital beamformer architectures are proposed considering key metrics of a typical MEO scenario. The proposed efficient digital beamformer is comprised of a sparse-matrix-based user selection, a 2D discrete Fourier transform (DFT)-based digital beam generation, which is implemented by a fast Fourier transform (FFT) algorithm, and a spatial windowing module for selecting the antenna pattern. Furthermore, architectures of digital beam-former using conventional 2D-FFT approach, fully unrolled 2D-FFT, and an area-power efficient twiddle factor (TF) quantized fully unrolled 2D-FFT are proposed. The spatial windowing architecture concerning 10 × 10 radio frequency chains and sparse matrix architecture for user selection is also proposed. The proposed architectures are implemented targeting Virtex ultrascale FPGA and the area-power utilization is reported. It is noticed that more than 50%-reduction in area and power is achieved with the beamformer incorporating the proposed TF quantized fully unrolled 2D-FFT.

Semi-grant-free non-orthogonal multiple access (semi-GF NOMA) has emerged as a promising technology for the fifth-generation new radio (5G-NR) networks supporting the coexistence of a large number of random connections with various quality of service requirements. However, implementing a semi-GF NOMA mechanism in 5G-NR networks with heterogeneous services has raised several resource management problems relating to unpredictable interference caused by the GF access strategy. To cope with this challenge, the paper develops a novel hybrid optimization and multi-agent deep (HOMAD) reinforcement learning-based resource allocation design to maximize the energy efficiency (EE) of semi-GF NOMA 5G-NR systems. In this design, a multi-agent deep Q network (MADQN) approach is employed to conduct the subchannel assignment (SA) among users. While optimization-based methods are utilized to optimize the transmission power for every SA setting. In addition, a full MADQN scheme conducting both SA and power allocation is also considered for comparison purposes. Simulation results show that the HOMAD approach outperforms other benchmarks significantly in terms of the convergence time and average EE.

This paper proposes a novel resource allocation scheme for optimizing the downlink of a user-centric cell-free massive multiple-input multiple-output (MIMO) system. The proposed approach aims to optimize the number of users served by each access point based on channel conditions while adapting to variable packet error rate and modulation and coding schemes. To enhance the received signal-to-noise plus interference ratio, the authors use a precoding design approach called the local protective partial zero-forcing that categorizes users based on their channel gain. The problem is formulated as a joint optimization of user assignment, resource allocation, and the precoding design. Closed-form expressions for the data rate are derived, and a new algorithm for resource allocation is introduced that outperforms several different scenarios while keeping the computational complexity reasonable. Compared to fixed parameter schemes, the proposed approach provides an optimal selection of the number of users for each access point and has the potential to significantly improve the system throughput, making it a novel and impactful solution for the practical implementation of user-centric cell-free massive MIMO systems.

On-board processing of digital beamforming in satellites is an efficient solution for the higher data rates, more capacity, and lower latency, but the available on-board limited power makes it impractical to digitally create thousands of beams at once. A significant portion of the analog hardware in a satellite communications payload can be replaced with highly integrated digital components, which are often more affordable, lighter, smaller, and reprogrammable by employing digital beamforming. In comparison to matrix-by-vector multiplication beamforming, the discrete Fourier transform (DFT) beamformer enables the finer realization of real-time beamformers with reduced circuit complexity and lower power consumption. Fast Fourier transform (FFT) methods can further reduce the computing cost of the DFT computation. Therefore, in this paper, area-power efficient two-dimensional (2D) FFT digital beamforming techniques are analyzed and implemented. The major implementation challenge is to produce N samples per cycle with lower area-power consumption. Fully unrolled 4-bit twiddle factor (TF) quantized FFT is proposed in this regard. The optimization techniques through quantization, truncation, and complex multipliers are thoroughly discussed for efficient implementation. The behavioral and post-route timing simulations are validated, and implementation results like area and power consumption are estimated and compared among conventional , fully unrolled, and the proposed 4-bit TF quantized 2D-FFT.

Beam Hopping (BH) is a popular technique considered for next-generation multi-beam satellite communication system which allows a satellite focusing its resources on where they are needed by selectively illuminating beams. While beam illumination plan can be adjusted according to its needs, the main limitation of convectional BH is the adjacent beam avoidance requirement needed to maintain acceptable levels of interference. With the recent maturity of precoding technique, a natural way forward is to consider a dynamic beam illumination scheme with selective precoding, where large areas with high-demand can be covered by multiple active precoded beams. In this paper, we mathematically model such beam illumination design problem employing an interference-based penalty function whose goal is to avoid precoding whenever possible subject to beam demand satisfaction constraints. The problem can be written as a binary quadratic programming (BQP). Next, two convexification frameworks are considered namely: (i) A Semi-Definition Programming (SDP) approach particularly targeting BQP type of problems, and (ii) Multiplier Penalty and Majorization-Minimization (MPMM) based method which guarantees to converge to a local optimum. Finally, a greedy algorithm is proposed to alleviate complexity with minimal impact on the final performance. Supporting results based on numerical simulations show that the proposed schemes outperform the relevant benchmarks in terms of demand matching performance while minimizing the use of precoding.

This paper aims to determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates jointly for the GEO satellite communication systems to minimize the payload power consumption and satisfy ground users' demands within a time window. Regarding constraint on the maximum number of illuminated beams per time slot, the technical requirement is formulated as a sparse optimization problem in which the hardware-related beam illumination energy is modeled in a sparsity form of the LP vectors. To cope with this problem, the compressed sensing method is employed to transform the sparsity parts into the quadratic form of pre-coders. Then, an iterative window-based algorithm is developed to update the LP vectors sequentially to an efficient solution. Additionally, two other two-phase frameworks are also proposed for comparison purposes. In the first phase, these methods aim to determine the MODCOD transmission schemes for users to meet their demands by using a heuristic approach or DNN tool. In the second phase, the LP vectors of each time slot will be optimized separately based on the determined MODCOD schemes.

Efficiently utilizing the network resources to minimize the operation costs while satisfying customer’s Quality-of-Experience (QoE) related requirement as well as dynamic demands is a challenging task of all network operators. This paper aims to develop a stationary capacity allocation method that anticipates time-varying demand and keeps the network operating under constraints on a stochastic blocking probability. Queuing delay requirement is also regarded as an QoE-oriented practical design. Employing an approximation of time-varying queuing model and continuous time Markov chain (CTMC) for queue length, the technical designs are stated as a convex stochastic optimization based on which a dynamic capacity allocation is proposed by using Lagrangian and gradient descent searching method. Numerical studies confirm that our proposed framework can efficiently and dynamically allocate optimal capacity for a blocking probability of less than 1% and the probability of violating the queuing-delay requirement is less than 5%.

Very High Throughput Satellite (VHTS) systems are characterized by a multi-beam footprint covering wide areas and providing service to large numbers of users. Multicasting comes naturally to exploit the multiuser diversity in VHTS systems, where the data of different users are multiplexed in a single PHY frame. Following the DVB-S2(X) standard, the resulting PHY frame is encoded using a single codeword. The latter brings some practical implementation challenges when precoding is considered, as the precoder can no longer be designed on a user-by-user basis. Avoiding overambitious and impractical precoding designs, our work focuses on the low-complexity MMSE-based precoding, which has been considered as the baseline for early satellite over-the-air precoding tests. While the multicast scheduling has been widely investigated in the literature, we will show in this work that its performance is significantly impacted by the equivalent multicast channel calculation. Therefore, in this paper, we analyze and report the impact of the user scheduling (i.e., selection of users to be multiplexed together in a single PHY frame) as well as the methodology employed for the equivalent multicast channel considered for the precoding computation.

Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC-enabled 5G system with strict requirements on reliability and latency. However, GF-NOMA-based systems can suffer from severe interference caused by the grant-free (GF) access manner which may degrade the system performance and violate the URLLC-related requirements. To overcome this issue, the paper proposes a novel reinforcement-learning (RL)-based random access (RA) protocol based on which each device can learn from the previous decision and its corresponding performance to select the best subchannels and transmit power level for data transmission to avoid strong cross-interference. The learning-based framework is developed to maximize the system access efficiency which is defined as the ratio between the number of successful transmissions and the number of subchannels. Simulation results show that our proposed framework can improve the system access efficiency significantly in overloaded scenarios.

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges. One of these is how to optimize the channelization, resource allocation, beamforming design, and MCS selection jointly for IEEE 802.11ax-based WLANs. In this paper, this technical requirement is formulated as a mixed-integer non-linear programming problem maximizing the total system throughput for the WLANs consisting of unicast users with multicast groups. A novel two-stage solution approach is proposed to solve this challenging problem. The first stage aims to determine the precoding vectors under unit-power constraints. These temporary precoders help re-form the main problem into a joint power and radio resource allocation one. Then, two low-complexity algorithms are proposed to cope with the new problem in stage two. The first is developed based on the well-known compressed sensing method while the second seeks to optimize each of the optimizing variables alternatively until reaching converged outcomes. The outcomes corresponding to the two stages are then integrated to achieve the complete solution. Numerical results are provided to confirm the superior performance of the proposed algorithms over benchmarks.

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC.

In this paper, we jointly optimize computation offloading and resource allocation to minimize the weighted sum of energy consumption of all mobile users in a backhaul limited cooperative MEC system with multiple fog servers. Considering the partial offloading strategy and TDMA transmission at each base station, the underlying optimization problem with constraints on maximum task latency and limited computation resource at mobile users and fog servers is non-convex. We propose to convexify the problem exploiting the relationship among some optimization variables from which an optimal algorithm is proposed to solve the resulting problem. We then present numerical results to demonstrate the significant gains of our proposed design compared to conventional designs without exploiting cooperation among fog servers and a greedy algorithm.

This paper aims to study an energy-efficiency (EE) maximization hybrid precoding (HP) design for mmWave multi-user (MU) systems where the analog precoding (AP) matrix is realized by a number of switches and phase shifters so that a connection between an RF chain and a transmit antenna can be switched off for energy saving. By explicitly considering the effect of each connection on the required power of digital precoding (DP) and AP design process, we describe the total power consumption as a sparsity form of the AP matrix. Together with the novel sparsity-modulus constraints of AP matrix, these sparsity terms make our system EE maximization (SEEM) problem be non-convex and challenging to solve. To tackle the SEEM problem, we first transform it into a subtractive-form weighted sum rate and power (WSRP) problem. We then exploit an alternating minimization of the mean-squared error algorithm to solve the WSRP problem where the DP vectors and AP matrix are updated alternatively, and a compressed sensing-based re-weighted quadratic- form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints.

Constrained by the number of transmitted data streams, this paper proposes sub-carrier allocation (SA) and hybrid precoding (HP) designs for sum-rate maximization in mm-wave OFDMA systems. The optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. Two two-stage solution approaches are proposed. In the first approach, a fully digital precoder (FDP) is optimized considering the computation sparsity constraint in the first stage. In the second approach, the sparsity constraint is only imposed in the second stage. To find the FDP, we employ the minimization of the weighted mean-squared error and the ℓ1-reweighted methods to tackle the non-convex objective function and sparsity constraints, respectively. In the second stage of each approach, we exploit an alternating weighted mean-squared error minimization algorithm to reconstruct HP's based on the FDP found in the first stage. Two novel analog precoding designs, namely semi-definite-relaxation-based and projected-gradient-descent-based, are then proposed to optimize the analog part of the obtained HP's. We also study the impacts of various system parameters on the system sum-rate and provide resource provisioning insights for HP systems. Numerical results show the superior performances of the proposed designs over joint SA and HP benchmark algorithms.

This paper studies hybrid precoding (HP) for mmWave multi-user OFDMA systems with sub-carrier allocation (SA) consideration. Constrained by a computation limit on the total number of data streams that can be processed, we aim to jointly optimize the SA and HP design to maximize the system sum-rate. This optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. We then propose two-stage solution approach to tackle the problem. In stage one, we optimize the fully digital precoding (FDP) considering the computation sparsity constraint. In the second stage, we exploit an alternating MMSE minimization algorithm to reconstruct the HP's based on the achieved FDP. A novel analog precoding design, namely “Projected-Gradient-Descent based”, is then proposed to optimize the analog part of the HP's.

In this paper, we study the resource allocation problem for a full-duplex (FD) multiuser wireless system consisting of one FD base-station (BS) and multiple FD mobile nodes. Our main focus is to jointly optimize the power allocation (PA) and subcarrier assignment (SA) for both uplink (UL) and downlink (DL) transmissions of all users to maximize the system sum-rate. Our design captures the self-interference of FD transceivers and allows the utilization of each subcarrier for multiple concurrent UP and DL transmissions. Since the joint optimization problem is a nonconvex mixed integer program, which is difficult to tackle, we propose to employ the bipartite matching method to address the SA. Toward this end, a fast greedy allocation algorithm is developed to perform initial assignment of UL/DL links to each subcarrier that offers the best sum rate. Then from the obtained SA solution, we adopt the successive convex approximation approach to solve the PA problem whose results are used to calculate the SA weights for re-optimizing the SA by using the bipartite matching method. We then present the numerical results to demonstrate the improvement of our proposed algorithm in comparison with the greedy FD and half-duplex (HD) resource allocation algorithms.

This paper considers the joint fronthaul resource and rate allocation for the OFDMA uplink cloud radio access networks (C-RANs). This amounts to determine users' transmission rates and quantization bit allocation for I/Q baseband signals, which must be transferred from remote radio heads (RRHs) to the cloud over the capacity-limited fronthaul network. Our design aims at maximizing the system sum rate through optimal allocation of fronthaul capacity and cloud computation resources. Toward this end, we propose a novel two-stage approach to solve the underlying non-linear integer problem. In the first stage, we relax the integer variables to attain a relaxed problem, which is solved by employing a pricing-based method. Interestingly, we show that the pricing-based problem is convex with respect to each optimization variable, which can be, therefore, solved efficiently. In addition, we develop a novel mechanism to iteratively update the pricing parameter which is proved to converge. In the second stage, we propose two different rounding strategies, which are applied to the obtained continuous solution of the relaxed problem to achieve a feasible solution for the original problem. Finally, we present numerical results to demonstrate the significant sum-rate gains of our proposed design with respect to a standard greedy algorithm.

This paper focuses on the resource allocation in a full-duplex (FD) multiuser single cell system consisting of one FD base-station (BS) and multiple FD mobile nodes. In particular, we are interested in jointly optimizing the power allocation (PA) and subcarrier assignment (SA) for uplink (UL) and downlink (DL) transmission of all users to maximize the system sum-rate. First, the joint optimization problem is formulated as nonconvex mixed integer program, a difficult nonconvex problem. We then propose an iterative algorithm to solve this problem. In the proposed algorithm, the PA is obtained by employing the SCALE algorithm, whereas the SA is updated by a gradient method. Finally, we present numerical results to demonstrate the significant gains of our proposed design compared to that due to two fast greedy algorithms.

This paper considers the joint processing design for the cloud radio access network (C-RAN) with limited cloud computation capacity. This amounts to determine the set of remote radio heads (RRHs) serving each user and the corresponding precoding vectors whose corresponding computation effort (CE) is a non-linear function of the number of antennas pooled from all serving RRHs and the modulation bits. Toward this end, we propose a novel three-step approach to solve the underlying mixed non-linear integer program. First, we transform this problem into a group association problem (GAP) with additional association constraints where each user must be associated with exactly one particular group of RRHs. Second, we study the relaxed power minimization problem (PMP) where the group association integer variables are relaxed and the computational constraint functions are approximated by weighted linear functions of transmission powers. We prove that this relaxed PMP can be solved optimally and the obtained optimal solution satisfies all association constraints of the original GAP problem. Third, we develop an iterative procedure to update the weight parameters of the approximated computational constraint functions to drive the achieved solution to an efficient and feasible solution of the original problem. Finally, we present numerical results to demonstrate the significant gains of our proposed design compared to that due to a fast greedy algorithm.

This paper considers a sparse precoding design for sum-rate maximization in a cloud radio access network (Cloud-RAN). Constrained by the fronthaul link capacity and transmit power limit at each remote radio head (RRH), the sparse design amounts to determine the precoders at the RRHs as well as the set of serving RRHs for each mobile user. In this work, we first formulate the fronthaul link constraints as non-convex and discontinuous constraints with sparsity terms. These sparsity terms are then iteratively approximated into linear forms by means of reweighted ℓ1-norm with conjugate functions. Finally, to determine the beamforming vectors, the non-convex sum-rate maximization problem with linear constraints is transformed into an equivalent problem of iterative weighted mean-squared error minimization. Convergence of the proposed iterative algorithm is then proved and verified by the presented numerical results. In addition, numerical results demonstrate the superior performance by the proposed algorithm over a previously proposed one in literature.

In this paper, we consider the joint coordinated beamforming and admission control design for cloud radio access networks (Cloud-RANs). Specifically, the set of multi-antenna remote radio heads (RRHs) serving each single-antenna user and the corresponding beamforming vectors are optimized to minimize the total transmission power subject to constraints on the capacity of fronthaul links, maximum powers of RRHs, and the minimum signal to interference plus noise ratios (SINRs) of users. Since the minimum SINR requirements of all users may not be guaranteed, some users may need to be removed so that all constraints can be satisfied. This NP-hard beamforming and admission control problem can be typically solved via a greedy algorithm. We instead propose a novel convex relaxation approach to formulate the underlying problem to a single-stage semi-definite program (SDP) based on which we develop an iterative algorithm to solve it. We then present numerical results to demonstrate the significant gains of the proposed algorithm compared to the greedy counterpart. Also, the impacts of the target SINR and cluster size on the number of supported users and total transmission power are also studied.

We investigate the cooperative transmission design for the cloud radio access network (C-RAN) considering fronthaul capacity and cloud processing constraints. Specifically, we consider the joint transmission scheme where the baseband signals and precoding vectors are processed and calculated by the cloud, which are delivered over the fronthaul links to the remote radio heads (RRHs) to form the RF signals for being transmitted to the users. We formulate the joint optimization problem for precoding design and allocation of RRHs, fronthaul capacity, and BBU processing resources to minimize the total transmission power subject to QoS constraints of the users. We present both optimal exhaustive search algorithm and two low-complexity algorithms to solve the resource allocation problem where the first one can achieve the Pareto optimality and the second one can determine an efficient solution with pretty low complexity. Numerical results confirm the excellent performance of the proposed low-complexity algorithms.

We consider the joint subchannel allocation and power control problem for OFDMA femtocell networks in this paper. Specifically, we are interested in the fair resource sharing solution for users in each femtocell that maximizes the sum min rate of all femtocells subject to protection constraints for the prioritized macro users. Toward this end, we describe the mathematical formulation for the problem and present an optimal exhaustive search algorithm. Given the exponential complexity of the optimal exhaustive search algorithm, we then develop a distributed and low-complexity algorithm to solve the resource allocation problem. We prove that the proposed algorithm converges. Finally, numerical results are presented to demonstrate the desirable performance of the proposed algorithms.

In this paper, we propose a universal power control (PC) algorithm that can provide QoS support in minimum signal-to-interference-plus-noise ratios (SINRs) for all users while exploiting differentiated channel conditions to enhance the network throughput. In particular, we design the PC algorithm by using non-cooperative game theory and establish sufficient conditions for its convergence. Then, we apply it to design a hybrid access scheme for two-tier macrocell-femtocell networks. Specifically, we devise a distributed load-award association algorithm for macro users, which enables flexible user association to BSs of either tier. In addition, we develop an efficient mechanism based on which users can steer the equilibrium in such a way that they achieve their desirable performance targets. Numerical results are then presented to validate the theoretical results and demonstrate the desirable performance of the proposed algorithms.