High altitude platform stations (HAPS) offer a promising solution for achieving ubiquitous connectivity in next-generation wireless networks (xG). Integrating HAPS with terrestrial networks, creating HAPS-empowered vertical heterogeneous networks (vHetNets), significantly improves coverage and capacity and supports emerging novel use cases. In HAPS-empowered vHetNets, HAPS and terrestrial network tiers can share the same spectrum, forming harmonized spectrum vHetNets that enhance spectral efficiency (SE). However, harmonized spectrum vHetNets face major challenges, including severe co-channel interference and scalability in large-scale deployments. To address the first challenge, we adopt a cell-free multiple-input multiple-output (MIMO) network architecture in which users are simultaneously served by multiple base stations using beamforming. However, beamforming weight design leads to a nonconvex, high-dimensional optimization problem, highlighting the scalability challenge. To address this second challenge, we develop a two-level distributed proportional fairness beamforming weight design (PFBWD) algorithm. This algorithm combines the augmented Lagrangian method (ALM) with a three-block ADMM framework. Simulation results demonstrate the performance improvements achieved by integrating HAPS with standalone terrestrial networks, as well as the reduced complexity and signaling overhead of the distributed algorithm compared to centralized algorithms.

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problem is non-convex; thus, we propose an iterative algorithm to solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.

In this paper, we study the energy efficiency (EE) performance of a three-node underwater full-duplex relay network, where the relay is an energy harvesting node. Since the arrival of harvested energy is intermittent from the ambient environment, energy-efficient data transmission can prolong the lifespan of the network. By exploiting the causal system information, we aim to maximize the long-term end-to-end EE of the network through adaptive power control at the relay node. The system is described through a Markov decision process, and the reinforcement learning framework is applied to obtain the energy-efficient transmission policy. Simulation results demonstrate the long-term average EE performance of the obtained transmission policy, which outperforms two benchmark approaches.

In this paper, we propose a novel non-orthogonal multiple access (NOMA)-assisted cooperative overlay spectrum-sharing network, in which a full- duplex (FD) secondary transmitter (ST) is selected for relaying the primary transmitter's information and transmitting its own to the primary receiver and secondary receivers, respectively, using NOMA approach. In order to encourage the ST to perform secondary cooperative transmission and maintain its operation, simultaneous wireless information and power transfer (SWIPT) technique is exploited to scavenge energy from the primary signal. To assess the performance of the proposed system, the outage probabilities for the primary and secondary networks are derived in tight closed-form approximations. Numerical results show that the combination of FD, SWIPT, and NOMA technologies greatly benefits the cooperative overlay spectrum- sharing network in terms of outage probability, when compared with that of half-duplex NOMA and conventional orthogonal multiple access networks.

In this paper, energy efficient resource allocation is considered for an uplink hybrid system, where non-orthogonal multiple access is integrated into orthogonal multiple access (OMA). To ensure the quality of service for the users, a minimum rate requirement is predefined for each user. An energy efficiency (EE) maximization problem is formulated by jointly optimizing the user clustering, channel assignment, and power allocation (PA). To address this problem, a many-to-one bipartite graph is first constructed considering the users and resource blocks (RBs) as the two sets of nodes. Based on swap matching, a joint user-RB association and PA scheme is proposed, which converges within a limited number of iterations. Moreover, for the PA under a given user-RB association, a feasibility condition is first derived. If feasible, a low-complexity algorithm is proposed, which obtains optimal EE for any successive interference cancellation (SIC) order and an arbitrary number of users. In addition, for the special case of two users per cluster, analytical solutions are provided for the two orders in which SIC can be implemented. These solutions shed light on how the power is allocated for each user to maximize the EE. Numerical results are presented, which show that the proposed joint user-RB association and PA algorithm outperforms other hybrid multiple-access-based and OMA-based schemes.

In this paper, energy efficient power allocation is considered for a multiuser uplink non-orthogonal multiple access (NOMA) system under quality of service (QoS) constraints. Due to the QoS requirements, the considered energy-efficiency (EE) maximization problem may be infeasible and it is first required to determine the feasibility conditions. Then in a feasible region, the considered problem is shown to be pseudo-concave and can be solved by employing Dinkelbach's algorithm. Moreover, for the two extreme cases with very low and high maximum transmit power constraints, analytical results are provided, which shed light on how power is allocated to each user to maximize the EE. Simulation results are presented, which show that with a low maximum transmit power constraint, NOMA may perform worse than the conventional orthogonal multiple access (OMA) in terms of EE. However, under a high maximum transmit power constraint, NOMA always outperforms OMA.

This letter studies the potential of harvesting energy from the self-interference of a full-duplex base station. The base station is equipped with a self-interference cancellation switch, which is turned off for a fraction of the transmission period in order to harvest the energy from the self-interference that arises due to the downlink transmission. For the remaining transmission period, the switch is on such that the uplink transmission takes place simultaneously with the downlink transmission. A novel energy-efficiency maximization problem is formulated for the joint design of downlink beamformers, uplink power allocations, and the transmission time-splitting factor. The optimization problem is nonconvex, and hence, a rapidly converging iterative algorithm is proposed by employing the successive convex approximation approach. Numerical simulation results show significant improvement in the energy-efficiency by allowing self-energy recycling.

In this paper, we consider the power allocation (PA) problem in cognitive radio networks (CRNs) employing nonorthogonal multiple access (NOMA) technique. Specifically, we aim to maximize the number of admitted secondary users (SUs) and their throughput, without violating the interference tolerance threshold of the primary users (PUs). This problem is divided into a two-phase PA process: a) maximizing the number of admitted SUs; b) maximizing the minimum throughput among the admitted SUs. To address the first phase, we apply a sequential and iterative PA algorithm, which fully exploits the characteristics of the NOMA-based system. Following this, the second phase is shown to be quasiconvex and is optimally solved via the bisection method. Furthermore, we prove the existence of a unique solution for the second phase and propose another PA algorithm, which is also optimal and significantly reduces the complexity in contrast with the bisection method. Simulation results verify the effectiveness of the proposed two-phase PA scheme.

We consider a multi-carrier and densely deployed small cell network, where small cells are powered by renewable energy source and operate in a full-duplex mode. We formulate an energy and traffic aware resource allocation optimization problem, where a joint design of the beamformers, power and sub-carrier allocation, and users scheduling is proposed. The problem minimizes the sum data buffer lengths of each user in the network by using the harvested energy. A practical uplink user rate-dependent decoding energy consumption is included in the total energy consumption at the small cell base stations. Hence, harvested energy is shared with both downlink and uplink users. Owing to the non-convexity of the problem, a faster convergence sub-optimal algorithm based on successive parametric convex approximation framework is proposed. The algorithm is implemented in a distributed fashion, by using the alternating direction method of multipliers, which offers not only the limited information exchange between the base stations, but also fast convergence. Numerical results advocate the redesigning of the resource allocation strategy when the energy at the base station is shared among the downlink and uplink transmissions.

In this paper, the performance of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is investigated, when multiple users are grouped into a cluster. The superiority of MIMO-NOMA over MIMO-OMA in terms of both sum channel capacity and ergodic sum capacity is proved analytically. Furthermore, it is demonstrated that the more users are admitted to a cluster, the lower is the achieved sum rate, which illustrates the tradeoff between the sum rate and maximum number of admitted users. On this basis, a user admission scheme is proposed, which is optimal in terms of both sum rate and the number of admitted users when the signal-to-interference-plus-noise ratio thresholds of the users are equal. When these thresholds are different, the proposed scheme still achieves good performance in balancing both criteria. Moreover, under certain conditions, it maximizes the number of admitted users. In addition, the complexity of the proposed scheme is linear in the number of users per cluster. Simulation results verify the superiority of MIMO-NOMA over MIMO-OMA in terms of both sum rate and user fairness, as well as the effectiveness of the proposed user admission scheme.

In this paper, a generalization of the satisfaction equilibrium (SE) for games in satisfaction form (SF) is presented. This new solution concept is referred to as the generalized satisfaction equilibrium (GSE). In games in SF, players choose their actions to satisfy an individual constraint that depends on the actions of all the others. At a GSE, players that are unsatisfied are unable to unilaterally deviate to be satisfied. The concept of GSE generalizes the SE in the sense that it allows mixed-strategy equilibria in which there exist players who are unable to satisfy their individual constraints. The pure-strategy GSE problem is closely related to the constraint satisfaction problem and finding a pure-strategy GSE is proven to be NP-hard. The existence of at least one GSE in mixed strategies is proven for the class of games in which the constraints are defined by a lower limit on the expected utility. A dynamics referred to as the satisfaction response is shown to converge to a GSE in certain classes of games. Finally, Bayesian games in SF and the corresponding Bayesian GSE are introduced. These results provide a theoretical framework for studying service-level provisioning problems in communications networks as shown by several examples.

This paper studies the joint design of downlink transmit beamforming and power allocation in two-tier wireless backhaul small cell heterogeneous networks. We consider reverse time division duplexing combined with equal spectrum splitting between two tiers for interference mitigation. We formulate a constrained optimization problem with the objective of maximizing the proposed access energy efficiency, defined by the ratio of the sum achievable rate at the users to the overall consumed power, where the power consumption model includes the adaptive decoding power. The formulated problem is non-convex and generally NP-hard. To solve it, we first apply the high-complexity branch-and-bound algorithm to find the global optimal solution. Then, we develop a lower complexity algorithm which iteratively solves the convex approximated problem until convergence. Compared with the conventional methods, this algorithm converges faster to a solution that is very close to the global optimal solution achieved by the branch-and-bound approach. Finally, we exploit the framework of the alternating direction method of multipliers on the convex approximated problem to develop a distributed algorithm. Numerical results are obtained to show the improvement of our proposed model with much better power conservation compared with the different design of fixed circuit power assignment.

This paper considers the problem of maximizing energy efficiency on the downlink of two-tier wireless backhaul small cell heterogeneous networks, where an interference mitigation strategy that combines reverse time division duplexing and equally orthogonal spectrum splitting is proposed. By enabling the small cell access points with the capability of switching ON/OFF, we develop a joint design of transmit beamforming, power and small cell selection that maximizes the proposed weighted access energy efficiency metric. To better convey the total power consumption model, we assume the adaptive decoding power model at each small cell access point. The formulated problem is combinatorial and non-convex, which is NP-hard in general. Hence, to find a more realistic close-to-optimal feasible solution, we iteratively approximate the non-convex constraints in the formulated problem as second order cone ones based on the first order Taylor convex approximation and error-controlled second order cone approximation. The problem arrived at each iteration is a mixed integer second order cone programming, which can be solved optimally and efficiently by available dedicated solver to achieve the final result at convergence. Numerical results are studied to show the improvement of our proposed model compared to previous works.

We consider a model of two-tier heterogeneous cellular networks (HetNets), with wireless backhaul communication (WBC), consisting of a macrocell and a small cell tiers. A joint design of transmit beamforming, power allocation and bandwidth partitioning for both uplink (UL) and downlink (DL) transmissions is considered in this work. By proposing a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors, we formulate a constrained optimization problem with the objective of maximizing the total energy efficiency of the small cells considering both UL and DL. For this non-convex problem, we leverage the sequential parametric convex approximation (SPCA) method to develop an efficient iterative algorithm to find the local optimal solution. Numerical simulations corroborate the convergence of our proposed algorithm and their performance gains compared to the previous work.

We consider a multiantenna small-cell network where access-points serving multiple users on the downlink and uplink channels. The access point is powered by both renewable and non-renewable energy sources. Its objective is to jointly maximize the throughput of the downlink users and minimize the amount of energy drawn from the non-renewable energy source while guaranteeing the quality of service to both downlink and uplink users. The quality of service of uplink user is achieved if all the received packets are decoded in a given time. Further, we assume that the energy used for sampling and decoding the received packets is not negligible due to the access point small range of operation. With this perspective, the optimal transmit power allocation and received packet decoding policy are investigated in offline and online settings. The joint optimization problem is solved using the dual decomposition method to find the optimal policy. Numerical simulations are conducted to evaluate the performance of the proposed offline and online algorithms. Simulation results shown significant gains compared to the greedy method.

In this paper, we consider a multiantenna small-cell access-point serving multiple users on the downlink and uplink sides using the frequency division duplex scheme. The access point is powered by both renewable and non-renewable energy sources. The objective of this paper is to process the frequency division duplex frame by drawing the minimum amount of energy from the non-renewable energy source while guaranteeing the quality of service of downlink transmission and decoding all the received uplink packets. Assuming that the energy used for sampling and decoding the received packets is not negligible, the optimal transmit power allocation and received packet decoding policy is investigated first in an offline setting and then in an online setting. An iterative offline algorithm based on dual decomposition method is proposed to find the optimal policy. In the online setting, an optimal high-complexity solution using dynamic programming approach is developed. Then, a reduced complexity suboptimal online algorithm using dual decomposition is proposed. Finally, two more suboptimal and low-complexity online algorithms for maximizing the ratio of throughput and non-renewable energy, and fairness metric are further addressed. Numerical simulations evaluate the performance of the proposed offline and online algorithms and show the efficiency of our proposed algorithms.

This paper studies two-tier heterogeneous cellular networks, with wireless backhaul communication, consisting of macrocell and small cell tiers. A joint design of transmit beamforming, power allocation, and bandwidth partitioning for both uplink and downlink transmissions is considered. By assuming the reverse time division duplexing system, we propose a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors. Under the proposed strategy, we formulate a constrained optimization problem with the objective of maximizing the sum rate of small cell uplink and downlink. For this non-convex problem, we leverage the sequential parametric convex approximation method to find the stationary point of the problem. In this method, a convex approximation of the problem is solved at each iteration. Furthermore, with appropriate transformations, we approximate the problem as second-order cone programming (SOCP) and propose a fast converging algorithm to attain the solution. We also evaluate the impact of imperfect channel state information by reformulating the optimization problem and applying the proposed algorithm to solve it. We conduct numerical simulations to show that the joint design of transmit beamforming, power allocation, and bandwidth partitioning leads to a better resource utilization and high spectral efficiency. Moreover, our results show that the proposed SOCP-based algorithm converges fast to a solution, which is shown to be closer to the global optimal solution achieved by the branch-and-bound algorithm compared with other works.

Abstract This paper considers uplink channel access in a zone of closed‐access small‐cells that is deployed in a macrocell service area. All small‐cell user equipments (SUEs) have access to a common orthogonal set of channels, leading to intercell interference. In addition, each channel forms a separate collision domain in each cell, thus can be successfully used only by one SUE of that cell. This paper proposes two non‐cooperative Bayesian games, G1 and G2, that are played among the SUEs. G1 assumes the availability of channel state information at the transmitters, while G2 assumes the availability of only the distribution of the channel state information. Each SUE can choose to transmit over one of the channels or not to transmit. The emphasis of the paper is on the set of symmetric threshold strategies where the Nash equilibrium is fully determined by a single parameter. The existence and uniqueness of pure Bayesian–Nash symmetric equilibrium of G1 in threshold strategies and mixed Bayesian–Nash symmetric equilibrium of G2 in uniformly distributed threshold strategies are proven. Numerical results corroborate the theoretical findings and benchmark against another decentralized scheme. Copyright © 2015 John Wiley & Sons, Ltd.

We consider a point-to-point wireless link with automatic repeat request (ARQ) based packet transmission where both the transmitter and receiver nodes are energy harvesting (EHNs). Transmitter EHN has access to low-grade channel state information (CSI) as it is implicitly obtained from ARQ feedback. Furthermore, signal processing tasks such as sampling and decoding at the receiver EHN can be interrupted if there is insufficient energy in the battery that cause loss of packet and wastage of harvested energy both at the transmitter and receiver EHNs. We propose selective sampling (SS) scheme where only part of the transmitted packet is sampled and stored depending on the receiver nodes stored energy. SS information (SSI) is then fed back to the transmitter. Packet decoding is not performed until full packet is constructed. Hence, we modify the conventional ARQ messages, i.e., ACK/NAK by adding few more bits to carry additional SSI as well. Another objective is to find the optimal power allocation policy to adapt to the low-grade CSI and SSI available at the transmitter such that harvested energy can be utilized efficiently especially at the receiver. Furthermore, using a decision-theoretic framework, we propose greedy power allocation scheme to evaluate the performance of the proposed retransmission scheme. In numerical examples, we illustrate that our proposed scheme has lower average packet transmission time and packet drop probability (PDP) compared to the equal power allocation and greedy power allocation with conventional retransmission scheme.

We consider the single-user multiple-input multiple-output (MIMO) precoder design problem for the doubly spatially correlated partially coherent Rayleigh fading channels with discrete inputs. The objective is to design a linear precoder to adapt to the degradation caused by the imperfect channel estimation at the receiver and the transmit-receive antenna correlation. The system is partially coherent so that the MIMO channel coefficients are estimated at the receiver and its error covariance matrix is fed back to the transmitter. We utilized the cutoff rate (CR) expression, an alternative to the mutual information (MI), and propose to use it as a design criterion to design the linear precoders. A linear precoder is obtained by numerically maximizing the CR with respect to the precoder matrix with a given average power constraint. Furthermore, the precoder matrix is decomposed using singular value decomposition (SVD) into the input shaping matrix, power loading matrix, and beamforming matrix. The beamforming matrix is found to coincide with the eigenvectors of the transmit correlation matrix. The power loading and input shaping matrices are solved numerically using the difference of convex (d.c.) functions programming algorithm and optimization under the unitary constraint, respectively. A 2-block alternating optimization (AO) algorithm is proposed to solve the input shaping matrix and power loading matrix iteratively. Precoders are designed to be used in conjunction with two MIMO transmission schemes: the spatial multiplexing (SM) and space-time (ST) block transmission modes. The frame error rate (FER) and average MI are used as the performance metrics to validate the performance of the newly designed CR-precoders in comparison with the conventional no-precoding case and cutoff rate optimized partially coherent constellations (PCCs). Numerical examples show that the performance gains of the designed precoders are significant compared to the CR-PCCs and conventional codewords.

The space-time (ST) constellation design problem and the related bit mapping schemes for multiple-input multipleoutput (MIMO) systems in spatially correlated Rayleigh fading channels with imperfect channel estimation at the receiver are considered. We assume that the channel estimation error covariance matrix, as well as transmit and receive correlation matrices are perfectly known both at the transmitter and at the receiver. We derive the cutoff rate (CR) and an upper bound (UB) on the average bit error probability (ABEP) expression for spatially correlated channels and propose them as the constellation design criteria subject to a given average transmit power constraint. Additionally, to use the resulting constellations together with forward error correction (FEC) codes requires efficient bit mapping schemes. Because these constellations lack geometrical symmetry in general, the Gray mapping is not always possible in the majority of the constellations obtained. Moreover, different mapping schemes may lead to highly different bit error rate (BER) performances. Thus, an efficient bit mapping scheme called the modified binary switching algorithm (MBSA) is proposed. It minimizes an upper bound on the ABEP (UB-ABEP) and is used to find the bit mapping schemes. Numerical examples show that the designed constellation and its optimized bit mapping together with turbo codes outperform the conventional constellations with respect to the frame error rate (FER).

Space-time (ST) constellation design problem and bit mapping schemes for multiple-input multiple-output (MIMO) systems in correlated Rayleigh fading channels with imperfect channel estimation at the receiver are considered. The channel coefficients are assumed to be correlated in space and uncorrelated in time from one coherence interval to another after which they change to another independent realization according to the spatial correlation model. We further assume the channel error estimation variance, as well as transmit and receive correlation matrices are perfectly known both at the transmitter and at the receiver. We derive the cutoff rate (CR) and an upper bound on the average bit error probability (BEP) expression for spatially correlated channels and propose them as the constellation design criteria subject to average power constraint. Additionally, to use the resulting constellations together with forward error correction (FEC) codes requires efficient bit mapping schemes. Because these constellations lack geometrical symmetry in general the Gray mapping is not always possible. Moreover, different mapping schemes may lead to different error rate performances. Thus, an efficient bit mapping scheme called binary switching algorithm (BSA) is proposed to use in order to find the optimal bit mappings. The Monte Carlo simulation results show that the designed constellation and its optimized bit mapping together with turbo codes outperform the earlier ones and the conventional constellations.

Space-time (ST) constellations for multiple-input multiple-output (MIMO) systems have usually been designed assuming either perfect or no channel state information (CSI) at the receiver. Practical systems are often partially coherent, i.e., have imperfect receiver CSI. In this paper, we consider the partially coherent ST code and constellation design for MIMO systems in Rayleigh fading channels while keeping the communication resource (i.e., power and rate) allocation to the training symbols minimum. As a result receiver acquires an imperfect CSI estimate, which is Gaussian random variable with zero mean and a non-zero variance, while the transmitter has perfect knowledge of the CSI estimation variance available via feedback. We propose to use the cutoff rate (CR) with respect to signal points as the design criterion. The CR expression is first derived. The CR maximization problem is formulated and numerical optimization algorithms are proposed. Constraints on the peak-to-average power ratio (PAPR) are also introduced. Numerical examples show that the designed constellations outperform earlier ones with respect to the PAPR, mutual information between the channel input and output as well as symbol error rate (SER) especially at low or medium signal-to-noise ratio (SNR) regime.

Relays can be used between the source and the destination to improve network coverage or reliability. In the relay based distributed space-time block-coded (DSTBC) single-carrier (SC) transmission scheme, asynchronism in time and frequency causes degradation in system performance because of the interblock interference, non-coherent combining and multiuser interference. The simultaneous presence of timing offset and carrier frequency offset also destroy the orthogonal structure of DSTBC. In this paper, we study the combined effect of timing offset and carrier frequency offset and propose a two stage equalization technique for this system at the destination terminal. Technique is based on interference cancellation and cyclic prefix reconstruction. The proposed equalization technique maintains the orthogonal structure of DSTBC and allows the use of low complexity one-tap frequency-domain equalizer. The technique significantly alleviates the effect of time offsets and frequency offsets at the destination without increasing the complexity of the receiver or disturbing the 3rd generation partnership project-long term evolution (3GPP LTE) uplink frame structure or the data rate over frequency selective channels.

Relays are used between the source and the destination to improve network coverage and reliability in cooperative relay system. Imperfect time delay synchronization at the receiver arises because of time difference in propagation of the signals from the source and the relay to the receiver. The performance degrades due to interference from the previous block and the defective channel matrix. In this paper, we study the effect of time delay asynchronism and propose to use interblock interference cancellation and cyclic prefix reconstruction techniques on amplify and forward relay based distributed space-time block coding for single carrier-frequency domain equalization system, which removes the unwanted interference part and reconstructs the defective channel matrix to circulant one. The proposed method significantly reduces the effect of time delay asynchronism at the receiver without disturbing the 3rd generation partnership project-long term evolution (3GPP LTE) uplink frame structure, data rate and increasing the complexity over frequency selective channels.