In this paper, we propose a novel downlink multiple access system with a multi-antenna transmitter and two single-antenna receivers, inspired by the underlying principles of hierarchical quadrature amplitude modulation (H-QAM) based multiple access (QAMA) and space-division multiple access (SDMA). In the proposed scheme, coded bits from two users are split and assigned to one shared symbol and two private symbols carried by different beams. Based on joint symbol mapping of H-QAM constellations and phase-aligned precoding at the transmitter, each receiver observes a different H-QAM constellation with Gray mapping, a unique parallax feature not shared by existing schemes. In addition to avoiding successive interference cancellation (SIC), each user independently demodulates its own bits on separate I and Q branches with calculations based on closed-form expressions. Hence the receiver complexity is on par with that of orthogonal multiple access (OMA), which is much lower than that in other competing alternatives such as non-orthogonal multiple access (NOMA) and rate-splitting multiple access (RSMA). We carry out system optimization and determine the achievable rate region. Numerical results show that the proposed system achieves superior performance compared with benchmark schemes employing SIC-free receivers, and further demonstrates competitive results across various performance metrics even relative to benchmark schemes relying on SIC-based receivers. The proposed scheme is attractive to 6G and IoT applications where the increased system complexity is at the transmitter while the receivers maintain low complexity.

This paper investigates the energy-efficient hybrid beamforming design for a multi-functional integrated sensing, communications, and powering (ISCAP) system. In this system, a base station (BS) with a hybrid analog-digital (HAD) architecture sends unified wireless signals to communicate with multiple information receivers (IRs), sense multiple point targets, and wirelessly charge multiple energy receivers (ERs) at the same time. To facilitate the energy-efficient design, we present a novel HAD architecture for the BS transmitter, which allows dynamic on-off control of its radio frequency (RF) chains and analog phase shifters (PSs) through a switch network. We also consider a practical and comprehensive power consumption model for the BS, by taking into account the power-dependent non-linear power amplifier (PA) efficiency, and the on-off non-transmission power consumption model of RF chains and PSs. We jointly design the hybrid beamforming and dynamic on-off control at the BS, aiming to minimize its total power consumption, while guaranteeing the performance requirements on communication rates, sensing Cramér-Rao bound (CRB), and harvested power levels. The formulation also takes into consideration the per-antenna transmit power constraint and the constant modulus constraints for the analog beamformer at the BS. The resulting optimization problem for ISCAP is highly non-convex due to the binary on-off non-transmission power consumption of RF chains and PSs, the non-linear PA efficiency, and the coupling between analog and digital beamformers. To tackle this problem, we first approximate the binary on-off non-transmission power consumption into a continuous form, and accordingly propose an iterative algorithm to find a high-quality approximate solution with ensured convergence, by employing techniques from alternating optimization (AO), sequential convex approximation (SCA), and semi-definite relaxation (SDR). Then, based on the optimized beamforming weights, we develop an efficient method to determine the binary on-off control of RF chains and PSs, as well as the associated hybrid beamforming solution. Numerical results show that the proposed design achieves an improved energy efficiency for ISCAP than other benchmark schemes without joint design of hybrid beamforming and dynamic on-off control. This validates the benefit of dynamic on-off control in energy reduction, especially when the multi-functional performance requirements become less stringent.

This paper studies the transmit energy beamforming in a multi-antenna wireless power transfer (WPT) system, in which an access point (AP) equipped with a uniform linear array (ULA) sends radio signals to wirelessly charge multiple single-antenna energy receivers (ERs). Different from conventional energy beamforming designs that require the AP to acquire the channel state information (CSI) via training and feedback, we propose a new training-free energy beamforming approach assisted by wireless radar sensing, which is implemented based on the following two-stage protocol. In the first stage, the AP performs wireless radar sensing to estimate the path gain and angle parameters of the ERs for constructing the corresponding CSI. In the second stage, the AP implements the transmit energy beamforming based on the constructed CSI to efficiently charge these ERs in a fair manner. Under this setup, first, we jointly optimize the sensing beamformers and duration in the first stage to minimize the sensing duration, while ensuring a given accuracy threshold for parameters estimation subject to the maximum transmit power constraint at the AP. Next, we optimize the energy beamformers in the second stage to maximize the minimum harvested energy by all ERs. In this approach, the estimation accuracy threshold for the first stage is properly designed to balance the resource allocation between the two stages for optimizing the ultimate energy harvesting performance. Finally, numerical results show that the proposed training-free energy beamforming design performs close to the performance upper bound with perfect CSI, and outperforms the benchmark schemes without such joint optimization and that with isotropic transmission.

This paper studies a secure cell-free integrated sensing and communication (ISAC) system, in which multiple ISAC transmitters collaboratively send confidential information to multiple communication users (CUs) and concurrently conduct target detection. Different from prior works investigating communication security against potential information eavesdropping, we consider the security of both communication and sensing in the presence of information and sensing eavesdroppers that aim to intercept confidential communication information and extract target information, respectively. Towards this end, we optimize the joint information and sensing transmit beamforming at these ISAC transmitters for secure cell-free ISAC. Our objective is to maximize the detection probability over a designated sensing area while ensuring the minimum signal-to-interference-plus-noise-ratio (SINR) requirements at CUs. Our formulation also takes into account the maximum tolerable signal-to-noise ratio (SNR) constraints at information eavesdroppers for ensuring the confidentiality of information transmission, and the maximum detection probability constraints at sensing eavesdroppers for preserving sensing privacy. The formulated secure joint transmit beamforming problem is highly non-convex due to the intricate interplay between the detection probabilities, beamforming vectors, and SINR constraints. Fortunately, through strategic manipulation and via applying the semidefinite relaxation (SDR) technique, we successfully obtain the globally optimal solution to the design problem by rigorously verifying the tightness of SDR. Furthermore, we present two alternative joint beamforming designs based on the sensing SNR maximization over the specific sensing area and the coordinated beamforming, respectively. Numerical results reveal the benefits of our proposed design over these alternative benchmarks.

This paper investigates the performance tradeoff for a multi-antenna integrated sensing and communication (ISAC) system with simultaneous information multicasting and multi-target sensing, in which a multi-antenna base station (BS) sends the common information messages to a set of single-antenna communication users (CUs) and estimates the parameters of multiple sensing targets based on the echo signals concurrently. We consider two target sensing scenarios without and with prior target knowledge at the BS, in which the BS is interested in estimating the complete multi-target response matrix and the target reflection coefficients/angles, respectively. First, we consider the capacity-achieving transmission and characterize the fundamental tradeoff between the achievable rate and the multi-target estimation Cramér-Rao bound (CRB) accordingly. To this end, we design the optimal transmit signal covariance matrix at the BS to minimize the estimation CRB for each of the two scenarios, subject to the minimum multicast rate requirement and the maximum transmit power constraint. It is shown that the optimal covariance matrix consists of two parts for ISAC and dedicated sensing, respectively. Next, we consider the transmit beamforming designs, in which the BS sends one information beam together with multiple a-priori known dedicated sensing beams for effective ISAC and each CU can cancel the interference caused by the sensing signals. By exploiting the successive convex approximation (SCA) technique, we develop efficient algorithms to obtain the joint information and sensing beamforming solutions to the resultant rate-constrained CRB minimization problems. Finally, we provide numerical results to validate the CRB-rate (C-R) tradeoff achieved by our proposed designs, as compared to two benchmark schemes, namely the isotropic transmission and the joint beamforming without sensing interference cancellation. It is shown that the proposed optimal transmit covariance solution achieves much better C-R performance than the benchmark schemes and the proposed joint beamforming with sensing interference cancellation performs close to the optimal transmit covariance solution when the number of CUs is small. We also conduct simulations to show the practical estimation performance achieved by our proposed designs, by considering randomly generated information signals and practical estimators.

This paper studies an uplink rate splitting multiple access (RSMA) enabled integrated sensing and communication (ISAC) system, where a radar-communication (RadCom) base station (BS) receives the signals from communication users (CUs) while simultaneously sensing the target. By utilizing the rate splitting (RS) paradigm to split the CU with better channel condition into two virtual CUs, the inter-user interference is mitigated substantially and a higher sensing performance is achieved with determinate communication performance requirements. Under this consideration, we jointly optimize the transmit and receive beamforming to maximize the radar signal-to-interference-plus-noise ratio (SINR) while ensuring to the CUs’ rate requirements as well as the transmit power constraints of BS and CUs. However, due to the coupled variables and non-convex constraints, the formulated problem is non-convex and difficult to solve. To tackle this problem, we propose an effective alternating optimization (AO) algorithm, specifically, in each iteration, the optimal transmit radar waveform is derived via the rank-1 guaranteed semidefinite relaxation (SDR). Simulation results are provided to validate the performance of our proposed design and it is shown that RSMA is effective and robust for suppressing interference.

This paper studies a downlink secure integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) transmits confidential messages to a single-antenna communication user (CU) while performing sensing on targets that may act as suspicious eavesdroppers. To ensure the quality of target sensing while preventing their potential eavesdropping, the BS combines the transmit confidential information signals with additional dedicated sensing signals, which play a dual role of artificial noise (AN) for degrading the qualities of eavesdropping channels. Under this setup, we jointly design the transmit information and sensing beamforming, with the objective of minimizing the weighted sum of beampattern matching errors and cross-correlation patterns for sensing subject to secure communication constraints. The robust design takes into account the channel state information (CSI) imperfectness of the eavesdroppers in two practical CSI error scenarios. First, we consider the scenario with bounded CSI errors of eavesdroppers, in which the worst-case secrecy rate constraint is adopted to ensure secure communication performance. In this scenario, we present the optimal solution to the worst-case secrecy rate constrained sensing beampattern optimization problem, by adopting the techniques of S-procedure, semi-definite relaxation (SDR), and a one-dimensional (1D) search, for which the tightness of the SDR is rigorously proved. Next, we consider the scenario with Gaussian CSI errors of eavesdroppers, in which the secrecy outage probability constraint is adopted. In this scenario, we present an efficient algorithm to solve the more challenging secrecy outage-constrained sensing beampattern optimization problem, by exploiting the convex restriction technique based on the Bernstein-type inequality, together with the SDR and 1D search. Finally, numerical results show that the proposed designs can properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, so as to achieve desirable sensing transmit beampatterns while ensuring the CU’s secrecy requirements for the two scenarios.

In this paper, we investigate the effect of cooperative double Intelligent reflecting surfaces (IRS) in improving the energy efficiency (EE) of a multi-user multiple-input multiple-output (MIMO) system. Under the general channel setup with the co-existence of both double-IRS and single-IRS reflection links, we aim to maximize the total EE of the system by jointly designing the transmit beamforming at the base station (BS) and the reflecting beamforming at the IRSs. The proposed problem is subject to the transmit power constraint at the BS, the individual minimum signal-to-interference-plus-noise ratio (SINR) requirements of the users and the unit modulus constraints of the IRS phase shifts. Due to the non-convex constraints and the coupling between variables, we decompose the original problem into two subproblems and propose an effective alternating optimization (AO) algorithm based on the techniques of fractional programming (FP) and semi-definite relaxation (SDR) to solve it. Simulation results validate the significant advantages of the proposed algorithm over benchmark schemes, as well as the effectiveness of cooperative double-IRS deployment in improving the EE performance of the system.

This paper studies a secrecy integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) aims to send confidential messages to a single-antenna communication user (CU), and at the same time sense several targets that may be suspicious eavesdroppers. To ensure the sensing quality while preventing the eavesdropping, we consider that the BS sends dedicated sensing signals (in addition to confidential information signals) that play a dual role of artificial noise (AN) for confusing the eavesdropping targets. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS, to minimize the matching error between the transmit beampattern and a desired beampattern for sensing, subject to the minimum secrecy rate requirement at the CU and the transmit power constraint at the BS. Although the formulated problem is non-convex, we propose an algorithm to obtain the globally optimal solution by using the semidefinite relaxation (SDR) together with a one-dimensional (1D) search. Next, to avoid the high complexity induced by the 1D search, we also present two sub-optimal solutions based on zero-forcing and separate beamforming designs, respectively. Numerical results show that the proposed designs properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, thus achieving desirable transmit beampattern for sensing while ensuring the CU’s secrecy rate.

In this paper, we study a downlink unmanned aerial vehicle (UAV)-enabled simultaneous wireless information and power transfer (SWIPT) system with the aid of an intelligent reflecting surface (IRS). Considering the time switching (TS) scheme, the UAV is leveraged to charge a single battery limited smart device (SD) and for information transmission simultaneously. We aim to maximize the average harvested energy of the SD over a finite mission/communication period on the conditions of average data transmission requirement and other practical constraints, by jointly optimizing the UAV trajectory, TS factors, and the phase shifts of IRS. In existing literatures, the block coordinate descent and successive convex approximation (BCD-SCA) method is usually applied to obtain a local optimal solution, however, the solution is susceptible to the initial parameter settings. Therefore, in this paper, we obtain a more advantageous solution by using the Lagrange dual method. Numerical results show that under the same constraints, the proposed design scheme improves the average harvested energy compared with the BCD-SCA method, and complexity analysis indicates that our algorithm has lower computation complexity.

In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled secure communication system, where ground nodes send confidential information to a legitimate UAV by time division multiple access in the presence of a smart UAV eavesdropper. It is a practical scenario that the UAV eavesdropper will make full use of its mobility for more effective eavesdropping and its trajectory can not be obtained in advance. Firstly, the problem of maximizing the sum secrecy rate by jointly optimizing the legitimate UAV trajectory, transmit power control and node scheduling is formulated from perspective of legitimate UAV. Next, due to the presence of smart eavesdropper and time-varying environment caused by uncontrollable mobility of UAV eavesdropper, we reformulated the original problem as a two-player zero-sum stochastic game (TZSG) problem. In order to solve the TZSG problem, considering competitive scenario and the mixed action space of the TZSG, we propose an algorithm based on multi-agent deep reinforcement learning to obtain a policy of legitimate communication link nodes and simulation results verify the proposed algorithm has superior performance than benchmark algorithm.

Massive multiple-input multiple-output (MIMO) enables reliability in low latency communication systems. This paper proposes a channel estimation scheme in orthogonal time frequency space (OTFS) massive MIMO systems, where the inherent channel cluster structure in Doppler-angle domain is considered. In our proposed scheme, a local Beta process (LBP) is utilized to characterize a two-dimensional (2-D) structure in Doppler-angle domain. The uplink channel estimation problem is modelled as a sparse channel reconstruction problem, and it is solved by estimating channel parameters (i.e., Doppler, angle and delay) with sparse Bayesian learning (SBL). Thus, downlink channel estimation can be performed with the help of these parameters. Finally, simulation results verify that the proposed algorithm can not only achieve ideal performance of channel estimation, but also have robust adaptation to variable Doppler.

In this paper, we investigate a secure unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system, where multiple ground users and a UAV collaborate to complete computing tasks in the presence of multiple eavesdroppers on the ground with exact locations. The UAV acts as a computing server and users can offload part of computing tasks to the air in order to relieve calculation pressure. The total energy consumption of the UAV is minimized by jointly optimizing the task allocation, the transmit power of each user equipment, and the trajectory of UAV, subject to the secrecy offloading rate constrains, the transmit power and UAV's trajectory constrains. To solve the difficult nonconvex optimization problem, an alternating algorithm based on the successive convex approximation is proposed to optimize the parameters iteratively. The simulation results demonstrate that the developed algorithm is effective and can decrease the energy consumption compared with other benchmark methods.

In this paper, we propose a novel deep learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. The channel response with known pilot positions can be treated as a low-resolution image. Then, we explore a generative adversarial network (GAN) for channel super-resolution (SR) to estimate the whole channel state information (CSI). For previous deep learning-based channel estimators recovered by a single model, high-frequency details are missing and they fail to match the fidelity expected at the higher resolution. The scheme we proposed is more consistent with the real channel by adding a discriminator to recover more details of the channel. The simulation results show that our scheme is superior to other SR-based channel estimation methods and close to the linear minimum mean square error (LMMSE) performance.

This paper considers an unmanned aerial vehicle (UAV)-enabled uplink non-orthogonal multiple-access (NOMA) system, where multiple users on the ground send independent messages to a UAV via NOMA transmission. We aim to design the UAV's dynamic maneuver in real time for maximizing the sum-rate throughput of all ground users over a finite time horizon. Different from conventional offline designs considering static user locations under deterministic or stochastic channel models, we consider a more challenging scenario with mobile users and segmented channel models, where the UAV only causally knows the users' (moving) locations and channel state information (CSI). Under this setup, we first propose a new approach for UAV dynamic maneuver design based on reinforcement learning (RL) via Q-learning. Next, in order to further speed up the convergence and increase the throughput, we present an enhanced RL-based approach by additionally exploiting expert knowledge of well-established wireless channel models to initialize the Q-table values. Numerical results show that our proposed RL-based and enhanced RL-based approaches significantly improve the sum-rate throughput, and the enhanced RL-based approach considerably speeds up the learning process owing to the proposed Q-table initialization.

With the rapidly increasing computing demand, data centers become more and more power-hungry, which incurs substantial electricity cost. Meanwhile, due to the time-dependent demand preference, power grid is suffering high load variations, which results in a large profit loss. In this paper, we consider a cost-efficient workload scheduling with a coordination between a cloud service provider operating multiple geo-distributed data centers and smart grids. The aim is to explore the flexibility of data center power demands to reduce the cost of the cloud service provider and smooth the load variations of smart grids simultaneously. We first present the penalty model of the computation workload scheduling at each data center, and introduce the cost model of smart grids, including power generation cost and the cost due to the power load variations. To jointly minimize the cost of smart grids and penalty of the cloud service provider resulted from workload scheduling, we formulate the objective function as a weighted sum of the cost and the penalty to study the tradeoffs, and obtain the optimal offline solution by the dual decomposition technique. In order to make the coordination implemented in an online fashion, we propose a Receding Horizon Control (RHC) based online algorithm to obtain the suboptimal workload management based on the predicted information, including the future amounts of interactive workload, batch workload, and power load, in the prediction horizon. The simulation results show that with the coordination between the cloud service provider and smart grids, the cost of smart grids can be significantly reduced, by up to 20 percent, and the load variations of smart grids can be well smoothed simultaneously.

In this letter, we propose a new channel estimation scheme for downlink channels in massive multiple-input multiple-output systems, where orthogonal frequency-division multiplexing is adopted. To estimate the downlink channels in the multi-subcarrier scenario, the common sparsity and cluster structure is exploited, which is unknown to the user. The common sparsity property is described and a local beta process is assumed on each of the common local clusters in a new constructed Bayesian framework. Then, we propose a common structure based multi-subcarrier Bayesian compressive sensing approach for the downlink channel estimation. Simulation results verify the effectiveness of the proposed algorithm.

This paper investigates a new scenario of spectrum sharing between unmanned aerial vehicle (UAV) and terrestrial wireless communication, in which a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV’s mobility in three-dimensional (3D) space to improve its cognitive communication performance while controlling the co-channel interference at the primary receivers (PRs), such that the received interference power at each PR is below a prescribed threshold termed as interference temperature (IT). First, we consider the quasi-stationary UAV scenario, where the UAV is placed at a static location during each communication period of interest. In this case, we jointly optimize the UAV’s 3D placement and power control to maximize the SR’s achievable rate, subject to the UAV’s altitude and transmit power constraints, as well as a set of IT constraints at the PRs to protect their communications. Second, we consider the mobile UAV scenario, in which the UAV is dispatched to fly from an initial location to a final location within a given task period. We propose an efficient algorithm to maximize the SR’s average achievable rate over this period by jointly optimizing the UAV’s 3D trajectory and power control, subject to the additional constraints on UAV’s maximum flying speed and initial/final locations. Finally, numerical results are provided to evaluate the performance of the proposed designs for different scenarios, as compared to various benchmark schemes. It is shown that in the quasi-stationary scenario the UAV should be placed at its minimum altitude while in the mobile scenario the UAV should adjust its altitude along with horizontal trajectory, so as to maximize the SR’s achievable rate in both scenarios.

Massive multiple-input multiple-output (MIMO) systems with a large number of base station (BS) antennas can provide significant spectral and energy efficiency. However, phase noise introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. In this paper, we study the effect of phase noise on uplink MIMO systems with imperfect channel state information. We consider a setup that the BS employs different numbers of free- running oscillators. The asymptotic equivalent (AE) expressions of signal-to-interference-plus-noise ratio (SINR) for matched filter (MF) and minimum mean squared error (MMSE) receivers are derived by using random matrix theory, respectively. Based on the AE expressions, we show that the system performance degrades as the number of oscillators increases. Then, we demonstrates a phase noise impact comparison between the MF and MMSE receivers. The MF receiver shows greater robustness but a lower sum rate performance than the MMSE receiver under the effect of phase noise. Moreover, we investigate the relationship between the sum rate gain and the data transmission interval. Due to the phase noise, the length of the data transmission interval is limited. The derived AE expressions provide valuable insights on the effect of various parameters on the system performance and the optimum choice of the data transmission interval length.

This paper studies an unmanned aerial vehicle (UAV)-enabled multicast channel, in which a UAV serves as a mobile transmitter to deliver common information to a set of ground users. We aim to characterize the capacity of this channel over a finite UAV mission/communication period, subject to its maximum speed constraint and an average transmit power constraint. To achieve the capacity, the UAV should use a sufficiently long code that spans over its whole mission/communication period. Accordingly, the multicast channel capacity is achieved via maximizing the minimum achievable time-averaged rates of the users, by jointly optimizing the UAV's trajectory and transmit power allocation over time. However, this problem is non-convex and difficult to be solved optimally. To tackle this problem, we first consider a relaxed problem by ignoring the maximum UAV speed constraint, and obtain its globally optimal solution via the Lagrange dual method. The optimal solution reveals that the UAV should hover above a finite number of ground locations, with the optimal hovering duration and transmit power at each location. Next, based on such a multi-location-hovering solution, we present a successive hover-and-fly trajectory design and obtain the corresponding optimal transmit power allocation for the case with the maximum UAV speed constraint. Numerical results show that our proposed joint UAV trajectory and transmit power optimization significantly improves the achievable rate of the UAV-enabled multicast channel, and also greatly outperforms the conventional multicast channel with a fixed-location transmitter.

In this paper, a partial channel state information (CSI) based robust Tomlinson-Harashima precoding (THP) transceiver is proposed in downlink time division duplexing (TDD) multi-user multiple-input multiple-output (MU-MIMO) systems. Partial CSI is obtained only based on the observations of uplink pilot training. Our robust THP transceiver includes THP matrices and a Demodulation Reference Signal (DMRS) precoding matrix. By minimizing the mean square error of the received signal at the user equipment (UE) side, robust THP matrices can be optimized at the BS. The optimized robust THP receiving matrices are transmitted to the UEs by DMRS. Based on the optimized THP matrices, DMRS precoder is also designed with partial CSI to make sure that the UEs can recover the receiving matrices as precise as possible. Simulation results show that our proposed THP transceiver is robust to both the partial CSI and the imperfect receiving matrices.

In this paper, we propose a new channel estimation scheme for downlink channels in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems where orthogonal frequency division multiplexing (OFDM) is adopted. In this scenario, the channel sparsity level is assumed unknown to the base station (BS). To the best of our knowledge, multitask bayesian compressive sensing (MBCS) has not been used in channel estimation of FDD massive MIMO systems. By exploiting the spatially common sparsity within the system bandwidth, the MBCS can learn the common sparsity characteristics of the user channels, which guarantees the performance of sparse channel recovery. Based on MBCS, we propose a pilot adapted MBCS (PAMBCS) scheme to further exploit the sparsity feature, where the pilot sequences are designed by minimizing the differential entropy of estimated channel vectors to reduce the estimation uncertainties. Simulation results have shown that the MBCS has a good capability to reduce pilot overhead, even though when a few number of subcarriers can be used for pilot transmission. Moreover, the performance of PAMBCS is much better than random pilots based MBCS.

In this paper, we propose a throughput-maximum resource provision scheme in the OFDMA-based Wireless Virtual Network (WVN). The proposed scheme takes the dynamics of both traffic arrivals and wireless channels into consideration. Furthermore, we consider a more flexible service contract where average resource provision is guaranteed for each slice. As it is practically impossible to know future traffic arrival and channel information, we employ the Lyapunov Optimization to develop an online resource provision algorithm. The proposed online algorithm performs joint subcarrier and power allocation to each slice based on the traffic information, wireless channel state as well as historical resource provision. Theoretic analysis implies that the proposed algorithm can arbitrarily get close to the theoretic optimal throughput with degradations in delay, and the two performances can be balanced on demand. Extensive simulation results show that our proposed scheme can significantly improve the average throughput, while reducing the average delay and achieving an excellent isolation performance.

Sparse code multiple access (SCMA) represents a paradigm shift from conventional multiple access concept and is considered as a promising candidate multiple access technique for the fifth generation (5G) wireless communication systems. In this paper, we investigates the downlink resource allocation problem in SCMA systems. Taking user fairness into account, we formulate a problem to maximize the sum of logarithmic user data rate by jointly optimizing the subcarrier assignment and power allocation. The formulated problem is combinatorial and non-convex. To tackle this problem, we first convert the power allocation problem into a standard convex problem by assuming the subcarrier assignment is fixed. Based on this, then the algorithm ``remove and reallocate'' for joint power allocation and subcarrier assignment is proposed with low complexity. Simulation results demonstrate that our proposal is near-optimal and can achieve good performance over both traditional OFDM systems and random subcarrier assignment in SCMA systems.

Nowadays, data centers consume substantial power, which takes up a considerable portion of local power supply (e.g., smart grid). In this paper, we leverage data center workload scheduling for the coordination between data centers and the smart grid, aiming to reduce the electricity cost of data centers and smooth the load variation of the smart grid simultaneously. We first build cost models of workload scheduling at data centers and the power generation and variation at the smart grid. We formulate the objective function as a weighted sum of the cost of the smart grid and the penalty caused by workload scheduling. Using the dual decomposition method, we then derive the optimal offline solution. To facilitate online implementation, we finally propose a Receding Horizon Control (RHC) based algorithm to obtain the suboptimal solution using limited predicted information. Extensive simulation results show that our proposed scheme can significantly reduce the cost of the smart grid, by up to 20%, while smoothing the load variation simultaneously.

Virtual Network Embedding (VNE) is a key step towards network virtualization. In this paper, we first introduce a new link interference metric for each link to quantify the interference caused by its bandwidth scarcity to accept VN requests, and then an Interference-based VNE (I-VNE) algorithm is proposed. Benefited from the new metric, I-VNE can jointly consider the temporal and spatial topology information of networks, and tries to embed each virtual network request with low interference to avoid rejecting the future requests. Our simulations show that, I-VNE can significantly improve performance in terms of time-average revenue, acceptance ratio and average node utilization with more unbalanced average link utilization, compared with the three existing VNE algorithms with only the global resource information in the spatial dimension.

We obtain the expression and a lower-bound for area spectral efficiency (ASE) of single-tier Poisson distributed networks considering multiuser MIMO (MU-MIMO) transmission. With the help of the lower-bound, we observe some interesting results. These results are validated via numerical results for the original expression. We find that ASE can be viewed as a concave function with respect to the number of antennas and active users. For the purpose of maximizing ASE, we demonstrate that the optimal number of active users is a fixed portion of the number of antennas. With the optimal number of active users, we observe that ASE increases linearly with the number of antennas. Another contribution of this paper is joint optimization of the base station (BS) density, the number of antennas, and active users to minimize the network energy consumption. We demonstrate that the optimal combination of the number of antennas and active users is the solution that maximizes the energy-efficiency. Besides the optimal algorithm, we propose a suboptimal algorithm to reduce the computational complexity, which can achieve near-optimal performance.

Developing green radio networks is desirable to improve energy efficiency under quality of service (QoS) constraints. In this paper, we propose a QoS aware energy efficient resource allocation scheme in multiuser high speed downlink packet access (HSDPA) systems. First, we derive a new metric, namely effective energy efficiency (EEE), to represent the delivered service bits at the media access control (MAC) layer per joule subject to given QoS constraints. Then, by using this new metric, a EEE optimization problem in the mixed traffic scenario is formulated, where we can exploit the multi-traffic diversity. With the help of primal decomposition technique, we solve the formulated problem and propose a cross-layer resource allocation scheme, in which determines the EEE optimal transmit power level for each user and schedules the set of EEE near-optimal users. Numerical results are presented to quantify the superiority of our proposed scheme over the conventional resource allocation schemes.

In this paper, we investigate the optimal energy efficient coordinated beamforming in multi-cell multiple-input single-output (MISO) systems with K multiple-antenna base stations (BS) and K single-antenna mobile stations (MS), where each BS sends information to its own intended MS with cooperatively designed transmit beamforming. We assume single user detection at the MS by treating the interference as noise. By taking into account a realistic power model at the BS, we characterize the Pareto boundary of the achievable energy efficiency (EE) region of the K links, where the EE of each link is defined as the achievable data rate at the MS divided by the total power consumption at the BS. Since the EE of each link is non-cancave (which is a non-concave function over an affine function), characterizing this boundary is difficult. To meet this challenge, we relate this multi-cell MISO system to cognitive radio (CR) MISO channels by applying the concept of interference temperature (IT), and accordingly transform the EE boundary characterization problem into a set of fractional concave programming problems. Then, we apply the fractional concave programming technique to solve these fractional concave problems, and correspondingly give a parametrization for the EE boundary in terms of IT levels. Based on this characterization, we further present a decentralized algorithm to implement the multi-cell coordinated beamforming, which is shown by simulations to achieve the EE Pareto boundary.

Characterizing the fundamental energy efficiency (EE) limits of MIMO broadcast channels (BC) is significant for the development of green wireless communications. We address the EE optimization problem for MIMO-BC in this paper and consider a practical power model, i.e., taking into account a transmit independent power which is related to the number of active transmit antennas. Under this setup, we propose a new optimization approach, in which the transmit covariance is optimized under fixed active transmit antenna sets, and then active transmit antenna selection (ATAS) is utilized. During the transmit covariance optimization, we propose a globally optimal energy efficient iterative water-filling scheme through solving a series of concave-convex fractional programs based on the block-coordinate ascent algorithm. After that, ATAS is employed to determine the active transmit antenna set. Since activating more transmit antennas can achieve higher sum-rate but at the cost of larger transmit independent power consumption, there exists a tradeoff between the sum-rate gain and the power consumption. Here ATAS can explore the optimal tradeoff curve and thus further improve the EE. Optimal exhaustive search and low-complexity norm based ATAS schemes are developed. Through simulations, we discuss the effect of different parameters on the EE of the MIMO-BC.

In this paper, we consider the robust retransmission precoder design for amplify-and-forward (AF) multi-input multi-output (MIMO) relay systems with channel estimation errors. With the objective of minimizing the average mean-squared-error of symbol estimations, we propose a novel progressive retransmission precoder by employing matrix diagonalization and channel pairing. As a consequence, we formulate the precoder design as a joint source/relay power allocation (PA) and channel pairing optimization problem. First, we propose the globally optimal solution, in which exhaustive search is employed for the channel pairing, while with each given channel pairing, an optimal PA algorithm is proposed to solve the nonconvex PA problem by utilizing the necessary conditions for optimality. However, the optimal channel pairing and PA solution is of very high complexity. In order to reduce the complexity, we then propose a suboptimal PA algorithm by iteratively optimizing the PA at the source and the relay, as well as a simplified channel pairing searching method based on the asymptotic optimal solution. It is shown that our proposed robust retransmission precoder improves the system performance significantly, meanwhile, the performance degradation of the low complexity PA and channel pairing algorithm is slight as compared to the optimal PA algorithm and the optimal channel pairing method.

Optimizing energy efficiency (EE) for the MIMO broadcasting channels (BC) is considered in this paper, where a practical power model is taken into account. Although the EE of the MIMO BC is non-concave, we reformulate it as a quasiconcave function based on the uplink-downlink duality. After that, an energy efficient iterative waterfilling scheme is proposed based on the block-coordinate ascent algorithm to obtain the optimal transmission policy efficiently, and the solution is proved to be convergent. Through simulations, we validate the efficiency of the proposed scheme and discuss the system parameters' effect on the EE.

TDD multiuser downlink MIMO channels with delayed channel state information at the transmitter (CSIT) are considered. Block diagonalization is applied at the base station (BS). In order to cope with the inter-user interference caused by the delayed CSIT, we develop a novel capacity estimation method at first and then propose robust delayed CSIT aware multimode selection schemes based on the estimation. The robust schemes can choose the mode with appropriate number of data streams and corresponding user and receive antenna set to compromise the multiuser spatial multiplexing gain and the rate loss caused by the inter-user interference. The proposed schemes are applicable to the heterogeneous case in which the moving speed of each user is different. Through simulation, the proposed schemes outperform the previous naive schemes and are promising in the practical system.

Network coded multiuser uplink MIMO channels have been discussed in this paper. A three slots transmission is considered here and spatial division multiple access (SDMA) has been employed to serve multiuser simultaneously. Minimum mean-square-error successive interference cancelation (MMSE-SIC) is applied in the first two slots and singular value decomposition (SVD) is applied for the third slots. In order to get the optimal performance, user scheduling, decoding ordering and user pairing need to be considered. However, the optimal exhaust searching algorithm is too complex to implement. We propose a low complexity scheme to solve this problem and the simulation results show the performance gain. The proposed scheme is promising in the real systems.

This paper considers the effect of channel estimation error in the multiuser downlink MIMO relay channels. We introduce a modified upper bound and a modified power allocation strategy for the previous asymptotic optimal singular value decomposition zero-forcing beamforming (SVD-ZFBF) [2] at first. And then the effect of channel estimation error to SVDZFBF would be discussed. Similar with the full channel side information (CSI) case, SVD-ZFBF can approach the modified upper bound in the large user number case, no matter how large the channel estimation error is. And in the limited user number case, increasing relay station (RS) power would not make SVD-ZFBF approach the upper bound, which is different from the full CSI case. There is a capacity gap, which is mainly related to the channel estimation error from RS to users, between SVD-ZFBF and the upper bound.