Full-duplex Distributed Massive MIMO System with Optimal Beamforming and Resource Allocation for WPCN

In this paper, we study the uplink (UL) and downlink (DL) ergodic capacity in the bidirectional dynamic network (BDN). By dynamically allocating the numbers of UL and DL remote radio heads (RRHs), BDN facilitates simultaneous UL and DL communications to accommodate the data traffic asymmetry. Assuming that the number of antennas at each RRH (M) is large, we derive the closed-form expressions for the ergodic achievable rates in BDN systems, and the theoretical results are shown to be accurate compared with the Monte Carlo simulation results. Furthermore, we investigate the performance of the limiting UL and DL rates when M goes to infinity, and it indicates that we can scale down the UL and DL power per user proportionally to 1/M to guarantee a certain UL or DL quality of service in BDN. Numerical results indicate that there exists an optimal ratio between UL and DL RRH numbers for the maximal spectral efficiency in BDN. Thanks to our proposed RRH layout which guarantees the uniformity and symmetry in the whole network, we can find the optimal ratio between UL and DL RRH numbers more easily.

This paper considers a single-antenna wireless- powered communication network (WPCN) over a flat- fading channel. We show that, by using our probabilistic harvest-and-transmit (PHAT) strategy, which requires the knowledge of instantaneous full channel state information (CSI) and fading probability distribution, the ergodic throughput of this system may be greatly increased relative to that achieved by the harvest-then- transmit (HTT) protocol. To do so, instead of dividing every frame to the uplink (UL) and downlink (DL), the channel is allocated to the UL wireless information transmission (WIT) and DL wireless power transfer (WPT) based on the estimated channel power gain. In other words, based on the fading probability distribution, we will derive some thresholds that determine the association of a frame to the DL WPT or UL WIT. More specifically, if the channel gain falls below or goes over these thresholds, the channel will be allocated to WPT or WIT. Simulation results verify the performance of our proposed scheme.

In this paper, we study a wireless powered communication network (WPCN) subject to the co-channel interference. In the down-link (DL), the hybrid access point (H-AP) broadcasts the wireless energy offered by the radio frequency (RF) signal to the user and relay. In the up-link (UL), the user and relay nodes transmit information to the H-AP through time-division-multiple-access (TDMA). In contrast to conventional WPCN, the interference can be considered as additional energy, not just as a threat for the data transmissions. Through the manner of curve fitting, we obtain the closed-form expression for the system outage probability and the throughput. Furthermore, the optimal time allocation to maximize the average system throughput is obtained. Finally it is shown that the theoretical results fit the simulation results closely and the proposed optimal scheme achieves the best performances.

In traditional half-duplex (HD) communication systems, the HD base station (BS) can transmit artificial noise (AN) to jam the eavesdroppers for securing the downlink (DL) communication. However, guaranteeing uplink (UL) transmission is not possible with an HD BS because HD BSs cannot jam the eavesdroppers during UL transmission. In this chapter, we investigate the resource allocation algorithm design for secure multiuser systems employing a full-duplex (FD) BS for serving multiple HD DL and UL users simultaneously. In particular, the FD BS transmits AN to guarantee the concurrent DL and UL communication security. We propose a multi-objective optimization framework to study two conflicting yet desirable design objectives, namely total DL transmit power minimization and total UL transmit power minimization. To this end, the weighted Tchebycheff method is adopted to formulate the resource allocation algorithm design as a multi-objective optimization problem (MOOP). The considered MOOP takes into account the quality-of-service (QoS) requirements of all legitimate users for guaranteeing secure DL and UL transmission in the presence of potential eavesdroppers. Thereby, secure UL transmission is enabled by the FD BS, which would not be possible with an HD BS. Although the considered MOOP is non-convex, we solve it optimally by semidefinite programming (SDP) relaxation. Simulation results not only unveil the trade-off between the total DL transmit power and the total UL transmit power, but also confirm that the proposed secure FD system can guarantee concurrent secure DL and UL transmission and provide substantial power savings over a baseline system.

In this paper, we consider a dynamic time division duplex system based on a cloud radio access network (RAN) to support the ultimate goal of efficient resource utilization. To optimize the performance of both remote radio heads (RRHs) and user equipments (UEs) which have multiple antennas, we propose an iterative scheme consisting of four types of beamforming, namely downlink (DL) transmit beamforming, DL receive beamforming, uplink (UL) transmit beamforming, and UL receive beamforming, in an attempt to minimize mean squared error (MSE). The proposed beamforming scheme makes it possible to allow DL and UL transmission be supported simultaneously by managing interference between RRHs and UEs. Our simulation results show that the MSE and the bit error rate (BER) are greatly improved under the proposed beamforming algorithm compared to conventional schemes. From intensive system level simulations, the proposed algorithm points out more than 3 dB performance improvement in terms of the required signal-to-noise-ratio (SNR) to achieve the BER less than $10^{-3}$ compared to conventional schemes.

The newly emerging wireless powered communication networks (WPCNs) have recently drawn significant attention, where radio signals are used to power wireless terminals for information transmission. In this paper, we study a WPCN where one multi-antenna access point (AP) coordinates energy transfer and information transfer to/from a set of single-antenna users. A harvest-then-transmit protocol is assumed where the AP first broadcasts wireless power to all users via energy beamforming in the downlink (DL), and then the users send their independent information to the AP simultaneously in the uplink (UL) using their harvested energy. To optimize the users' throughput and yet guarantee their rate fairness, we maximize the minimum throughput among all users by a joint design of the DL-UL time allocation, the DL energy beamforming, and the UL transmit power allocation plus receive beamforming. We solve this non-convex problem optimally by two steps. First, we fix the DL-UL time allocation and obtain the optimal DL energy beamforming, UL power allocation and receive beamforming to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users. This problem is shown to be in general non-convex; however, we convert it equivalently to a spectral radius minimization problem, which can be solved efficiently by applying the alternating optimization based on the non-negative matrix theory. Then, the optimal time allocation is found by a one-dimension search to maximize the minimum rate of all users. Furthermore, two suboptimal designs of lower complexity are proposed, and their throughput performance is compared against that of the optimal solution.

In Internet of Things (IoT) systems, a number of sensor devices monitor the physical system states and exchange information with each other. The main limitation is that the IoT devices are generally energy constrained since those are powered with batteries. To address this energy problem, we consider a cooperative wireless-powered communication network (WPCN), which consists of three phases: 1) downlink (DL) energy transfer from a multi-antenna access point (AP); 2) data sharing among IoT devices; and 3) uplink (UL) information transfer from single-antenna IoT devices. Based on the shared data and the harvested energy, the single-antenna IoT devices in the neighborhood cooperate to form a virtual antenna array in order to transmit their information simultaneously to the multi-antenna AP using a multiple-input multiple-output (MIMO) technique in the UL information transfer phase. In this study, the transmit covariance matrices (i.e., beamforming vectors and the corresponding transmit power allocation) used for both DL energy transfer and UL information transfer are jointly designed to maximize the UL capacity based on the Lagrangian method. Furthermore, the time allocation for each phase is optimized based on a stochastic gradient method. In the numerical results, it is shown that our proposed beamforming scheme and the stochastic time allocation can achieve near-optimal performance.

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

In this paper, we consider a two-user wireless powered communication network (WPCN) with user cooperation, which consists of a hybrid access point (H-AP) and two users with energy harvesting circuit. The H-AP transfers energy to users during downlink (DL) and the users transmit information during uplink (UL) utilizing the harvested energy. To enhance user fairness, a user cooperation protocol is employed in which a user near from the H-AP relays the message of the other user by partially using its allocated time and energy resources. We maximize the sum-rate in the considered network subject to a set of quality of service (QoS) requirements by jointly optimizing the time allocation for DL energy transfer and UL information transmission. A novel closed-form solution is derived for the optimization problem using convex optimization techniques.

In this paper, we consider a wireless-powered communication networks (WPCNs), in which a multi-antenna two-way amplify-and-forward (AF) relay transfers power to multipair of single antenna users. A harvest-then-transmit protocol is adopted here, where the relay first broadcasts energy signals to all users during a power transfer phase. Multiple pairs of users, then use the harvested energy to exchange information signals through the relay over two phases, up-link (UL) and down-link (DL) phases. In the former phase, all the users simultaneously transmit their information signals to the relay, then in the latter phase the relay amplifies and forwards the received signals to their intended users. In addition, in order to mitigate the interference, zero-forcing reception and transmission is applied at the relay. In light of this, analytical expressions for the ergodic spectral and energy efficiencies are derived and Monte Carlo simulations are provided throughout to validate our analysis. The impacts of some important system parameters such as energy harvesting (EH) time, number of user-pairs and relay antennas, on the system performance are investigated. The results show that, good selection of the EH time is the key to achieve optimal system performance and increasing the number of relay antennas can reduce this factor.

This paper studies an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN), in which a UAV is dispatched as a mobile access point (AP) to serve a set of ground users periodically. The UAV employs the radio frequency (RF) wireless power transfer (WPT) to charge the users in the downlink, and the users use the harvested RF energy to send independent information to the UAV in the uplink. Unlike the conventional WPCN with fixed APs, the UAV-enabled WPCN can exploit the mobility of the UAV via trajectory design, jointly with the wireless resource allocation optimization, to maximize the system throughput. In particular, we aim to maximize the uplink common (minimum) throughput among all ground users over a finite UAV's flight period, subject to its maximum speed constraint and the users' energy neutrality constraints. The resulted problem is nonconvex and thus difficult to be solved optimally. To tackle this challenge, we first consider an ideal case without the UAV's maximum speed constraint, and obtain the optimal solution to the relaxed problem. The optimal solution shows that the UAV should successively hover above a finite number of ground locations for downlink WPT, as well as above each of the ground users for uplink communication. Next, we consider the general problem with the UAV's maximum speed constraint. Based on the above multilocation-hovering solution, we first propose an efficient successive hover-and-fly trajectory design, jointly with the downlink and uplink wireless resource allocation, and then propose a locally optimal solution by applying the techniques of alternating optimization and successive convex programming (SCP). Numerical results show that the proposed UAV-enabled WPCN achieves significant throughput gains over the conventional WPCN with fixed-location AP.

This paper studies the spectrum efficiency (SE) maximization problem for cooperative multi-antenna power beacon (PB)-enabled wireless powered communication networks (WPCN), where each transmitter harvests energy from surrounding PBs and then transmits data to the corresponding receiver within its allocated duration. The considered problem is formulated as jointly optimizing the energy beamforming vectors of PBs, the transmission duration, and the transmit power of users to maximize the total SE. In order to derive an efficient algorithm, the SE maximization problem is decomposed into two subproblems: the SE maximization problem for data transmission and the energy consumption minimization problem for energy transfer.We prove that the optimal SE is concave with the harvested energy of each user, and based on this concavity, an efficient algorithm is proposed to achieve the optimal solution. Finally, simulation results validate the optimality of the proposed algorithm, and verify the superiority of the proposed scheme-more than 150% performance gain is obtained, compared with the scheme of single PB with single antenna.

The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance.

The work reports the developed planning algorithms for optimum resource allocation over a dynamic 5G infrastructure, addressing all involved types of system resources, from the radio access domain to the MEC domain through the Optical Distribution Network (ODN). A modular modelling design is adopted considering these three domains. In that manner, allocation is composed of three optimization phases, which are executed sequentially and ensure optimal end-to-end allocation of resources. The first allocation phase focuses on the over-the-air resources, which are directly assigned to the 5G mobile users. The key entities for radio access are the Remote Radio Heads (RRHs), which are placed at the edge of the network fronthaul and provide wireless communication slots to the User Equipment (UE). Beamforming capabilities are also assumed allowing each RRH to support simultaneous and independent transmission of multiple directed beams to provide high quality targeted coverage. The resource allocation scheme is responsible for assigning subchannels within specific beams to address the bandwidth requirements of the requested services. The translation from the requested data-rate and the allocated bandwidth is based on the characteristics of 3GPP New Radio (NR) standard. Furthermore, the radio resource allocation process is responsible for assigning different bands for optimal frequency reuse within the available spectrum and avoids interference. The formulated ILP optimization problem, which is solvable in close to real-time due to its low computational complexity, allows dynamic adaptation to requests and resources, with a target to minimize energy consumption. The latter is achievable through the aggregation of wireless resources into individual beams and RRHs. In order to further enhance this process, an RRH prioritization algorithm is introduced for weighting the candidate allocations. With the completion of the first allocation phase, the optical resource allocation phase is initiated. The considered architecture follows that of the EU funded blueSPACE project and consists of a fully flexible ODN exploiting the cutting-edge capabilities of Spectrally Spatially Flexible Optical Networks (SS-FONs). In this context, the allocator considers the spectral and/or spatial multiplexing features of the optical nodes for performing elastic switching. The aim is to establish complete optical routes (lightpaths) from the radio access domain to the Central Office (CO). In particular, the output of this allocation phase is utilized to determine end-to-end fiber-based connections between BBUs and beams. The formulated optimization problem (a low complexity ILP) is able to model any type of network topology and provide optimal paths to dynamic demands according to the available optical domain resources. Power saving is achieved by minimizing the number of the optical elements used for building the required lightpaths. The last allocation phase assigns MEC resources within the blueSPACE virtual Content Delivery Network (vCDN) and Evolved Packet Core (vEPC). The services request MEC resources to offload computationally demanding tasks (such as real-time multimedia processing) and the allocator is responsible to make optimal decisions for the hosting of the required VM instances. The formulated ILP problem provides such a fast decision-making mechanism, while averting the scattering of allocated resources to numerous physical servers in order to conserve energy. With the termination of this third phase, all required network and computing resources are fully allocated for serving dedicated enhanced Mobile Broadband (eMBB) slices. The aforementioned resource allocation algorithms are implemented and simulated in MATLAB. For the evaluation process, two real-world simulation scenarios are considered to assess the allocation effectiveness and energy conservation capabilities for changing traffic requests. One scenario simulates a 5G deployment over a football stadium and the second scenario a deployment over a city park. Due to their coverage requirements the two scenarios lead to different RRH location and connectivity properties. Results show that in the “Stadium” scenario we achieve 50% savings in the number of activated RRHs and beams, while in the “City Park” scenario this metric reaches 67%. With respect to the allocation of optical networking resources, our optimization approach achieves conservation of up to 11% of optical ports and 14% of optical cores for the stadium scenario, while the corresponding results for the “City Park” scenario are 9% and 16%, respectively. Lastly, regarding the MEC resource allocation, the conducted simulations in the context of the “Stadium” scenario have shown 22% average reduction of the activated physical MEC servers (reaching up to 38%), while in the “City Park” scenario these savings average even higher to 68% (reaching up to 91%).

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.