This paper considers a multiuser multiple-input single-output (MISO) downlink system with simultaneous wireless information and power transfer. In particular, we focus on secure communication in the presence of passive eavesdroppers and potential eavesdroppers (idle legitimate receivers). We study the design of a resource allocation algorithm minimizing the total transmit power for the case when the legitimate receivers are able to harvest energy from radio frequency signals. Our design advocates the dual use of both artificial noise and energy signals in providing secure communication and facilitating efficient wireless energy transfer. The algorithm design is formulated as a non-convex optimization problem. The problem formulation takes into account artificial noise and energy signal generation for protecting the transmitted information against both considered types of eavesdroppers when imperfect channel state information (CSI) of the potential eavesdroppers and no CSI of the passive eavesdroppers are available at the transmitter. Besides, the problem formulation also takes into account different quality of service (QoS) requirements: a minimum required signal-to-interference-plus-noise ratio (SINR) at the desired receiver; maximum tolerable SINRs at the potential eavesdroppers; a minimum required outage probability at the passive eavesdroppers; and minimum required heterogeneous amounts of power transferred to the idle legitimate receivers. In light of the intractability of the problem, we reformulate the considered problem by replacing a non-convex probabilistic constraint with a convex deterministic constraint. Then, a semi-definite programming (SDP) relaxation approach is adopted to obtain the optimal solution for the reformulated problem. Furthermore, we propose a suboptimal resource allocation scheme with low computational complexity for providing communication secrecy and facilitating efficient energy transfer. Simulation results demonstrate the close-to-optimal performance of the proposed schemes and significant transmit power savings by optimization of the artificial noise and energy signal generation.

This paper considers an orthogonal frequency division multiplexing (OFDM) downlink point-to-point system with simultaneous wireless information and power transfer. It is assumed that the receiver is able to harvest energy from noise, interference, and the desired signals. We study the design of power allocation algorithms maximizing the energy efficiency of data transmission (bit/Joule delivered to the receiver). In particular, the algorithm design is formulated as a high-dimensional non-convex optimization problem which takes into account the circuit power consumption, the minimum required data rate, and a constraint on the minimum power delivered to the receiver. Subsequently, by exploiting the properties of nonlinear fractional programming, the considered non-convex optimization problem, whose objective function is in fractional form, is transformed into an equivalent optimization problem having an objective function in subtractive form, which enables the derivation of an efficient iterative power allocation algorithm. In each iteration, the optimal power allocation solution is derived based on dual decomposition and a one-dimensional search. Simulation results illustrate that the proposed iterative power allocation algorithm converges to the optimal solution, and unveil the trade-off between energy efficiency, system capacity, and wireless power transfer: (1) In the low transmit power regime, maximizing the system capacity may maximize the energy efficiency. (2) Wireless power transfer can enhance the energy efficiency, especially in the interference limited regime.

In this paper, we study the resource allocation algorithm design for multiuser orthogonal frequency division multiplexing (OFDM) downlink systems with simultaneous wireless information and power transfer. The algorithm design is formulated as a non-convex optimization problem for maximizing the energy efficiency of data transmission (bit/Joule delivered to the users). In particular, the problem formulation takes into account the minimum required system data rate, heterogeneous minimum required power transfers to the users, and the circuit power consumption. Subsequently, by exploiting the method of timesharing and the properties of nonlinear fractional programming, the considered non-convex optimization problem is solved using an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation and user selection solution are derived based on Lagrange dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm achieves the maximum energy efficiency of the system and reveal how energy efficiency, system capacity, and wireless power transfer benefit from the presence of multiple users in the system.

We study resource allocation algorithm design for energy-efficient communication in an orthogonal frequency division multiple access (OFDMA) downlink network with hybrid energy harvesting base station (BS). Specifically, an energy harvester and a constant energy source driven by a non-renewable resource are used for supplying the energy required for system operation. We first consider a deterministic offline system setting. In particular, assuming availability of non-causal knowledge about energy arrivals and channel gains, an offline resource allocation problem is formulated as a non-convex optimization problem over a finite horizon taking into account the circuit energy consumption, a finite energy storage capacity, and a minimum required data rate. We transform this non-convex optimization problem into a convex optimization problem by applying time-sharing and exploiting the properties of non-linear fractional programming which results in an efficient asymptotically optimal offline iterative resource allocation algorithm for a sufficiently large number of subcarriers. In each iteration, the transformed problem is solved by using Lagrange dual decomposition. The obtained resource allocation policy maximizes the weighted energy efficiency of data transmission (weighted bit/Joule delivered to the receiver). Subsequently, we focus on online algorithm design. A conventional stochastic dynamic programming approach is employed to obtain the optimal online resource allocation algorithm which entails a prohibitively high complexity. To strike a balance between system performance and computational complexity, we propose a low complexity suboptimal online iterative algorithm which is motivated by the offline algorithm. Simulation results illustrate that the proposed suboptimal online iterative resource allocation algorithm does not only converge in a small number of iterations, but also achieves a close-to-optimal system energy efficiency by utilizing only causal channel state and energy arrival information.

This paper considers orthogonal frequency division multiple access (OFDMA) systems with simultaneous wireless information and power transfer. We study the resource allocation algorithm design for maximization of the energy efficiency of data transmission (bits/Joule delivered to the receivers). In particular, we focus on power splitting hybrid receivers which are able to split the received signals into two power streams for concurrent information decoding and energy harvesting. Two scenarios are investigated considering different power splitting abilities of the receivers. In the first scenario, we assume receivers which can split the received power into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, we examine receivers which can split the received power only into a discrete set of power streams with fixed power splitting ratios. For both scenarios, we formulate the corresponding algorithm design as a non-convex optimization problem which takes into account the circuit power consumption, the minimum data rate requirements of delay constrained services, the minimum required system data rate, and the minimum amount of power that has to be delivered to the receivers. By exploiting fractional programming and dual decomposition, suboptimal iterative resource allocation algorithms are developed to solve the non-convex problems. Simulation results illustrate that the proposed iterative resource allocation algorithms approach the optimal solution within a small number of iterations and unveil the trade-off between energy efficiency, system capacity, and wireless power transfer: (1) wireless power transfer enhances the system energy efficiency by harvesting energy in the radio frequency, especially in the interference limited regime; (2) the presence of multiple receivers is beneficial for the system capacity, but not necessarily for the system energy efficiency.

In this paper, resource allocation for energy efficient communication in space division multiple access (SDMA) downlink networks with large numbers of transmit antennas is studied. The considered problem is modeled as a non-convex optimization problem which takes into account the circuit power consumption and a minimum required data rate. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. The optimal power allocation solution for each iteration is derived based on a low complexity user selection policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations and unveil the trade-off between energy efficiency and the number of antennas.

In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.

In this paper, we formulate a joint optimization problem for resource allocation and scheduling in full-duplex multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) relaying systems with amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. Our problem formulation takes into account heterogeneous data rate requirements for delay sensitive and non-delay sensitive users. We also consider a theoretically optimal hybrid relaying scheme as a performance benchmark, which allows a dynamic selection between AF relaying and DF relaying protocols with full-duplex and half-duplex relays. We show that under some mild conditions the optimal transmitter precoding and receiver post-processing matrices jointly diagonalize the MIMO-OFDMA relay channels for all considered relaying protocols transforming the resource allocation and scheduling problem into a scalar optimization problem. Dual decomposition is employed to solve this optimization problem and a distributed iterative resource allocation and scheduling algorithm with closed-form power and subcarrier allocation is derived. Simulation results not only illustrate that the proposed distributed algorithm converges to the optimal solution in a small number of iterations, but also demonstrate the potential performance gains achievable with full-duplex relaying protocols.

In this paper, resource allocation for energy-efficient communication in an orthogonal frequency division multiple access (OFDMA) downlink network with a large number of transmit antennas is studied. The considered problem is modeled as a non-convex optimization problem which takes into account the circuit power consumption, imperfect channel state information at the transmitter (CSIT), and different quality of service (QoS) requirements including a minimum required data rate and a maximum tolerable channel outage probability. The power allocation, data rate adaptation, antenna allocation, and subcarrier allocation policies are optimized for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). By exploiting the properties of fractional programming, the resulting non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which leads to an efficient iterative resource allocation algorithm. In each iteration, the objective function is lower bounded by a concave function which can be maximized by using dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations and demonstrate the trade-off between energy efficiency and the number of transmit antennas.

We study resource allocation for energy-efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs). We formulate the resource allocation problem for joint BS zero-forcing beamforming (ZFBF) transmission as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. In each iteration, a low-complexity suboptimal semi-orthogonal user selection policy is computed. Besides, by using the concept of perturbation function, we show that in the considered systems under some general conditions, the duality gap with respect to the power optimization variables is zero despite the non-convexity of the primal problem. Thus, dual decomposition can be used in each iteration to derive an efficient closed-form power allocation solution for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency, network capacity, and backhaul capacity: (1) In the low transmit power regime, an algorithm which achieves the maximum spectral efficiency may also achieve the maximum energy efficiency; (2) a high spectral efficiency does not necessarily result in a high energy efficiency; (3) spectral efficiency is always limited by the backhaul capacity; (4) energy efficiency increases with the backhaul capacity only until the maximum energy efficiency is achieved.

In this paper, we formulate an optimization problem for resource allocation and scheduling in orthogonal frequency division multiple access (OFDMA) networks. Our problem formulation takes into account artificial noise generation to combat a passive multiple antenna eavesdropper and the effects of imperfect channel state information at the transmitter (CSIT) in slow fading. The optimization problem is solved by dual decomposition which results in an iterative resource allocation algorithm with a fast speed of convergence. The packet data rate, secrecy data rate, power, and subcarrier allocation policies are optimized to maximize the average secrecy outage capacity (bit/s/Hz securely and successfully delivered to the users). Simulation results illustrate that our proposed iterative algorithm converges to the optimal solution in a small number of iterations and guarantees a non-zero secrecy data rate for given target secrecy outage and channel outage probability requirements.

Repetition-coded relaying using half-duplex relays offers an easy way of achieving cooperative diversity that works well with apparently any source transmission schemes, but at the same time presents a potentially serious problem of inefficient spectrum utilization. Poor diversity-multiplexing tradeoff (DMT) is often reported. This paper investigates efficient ways of using repetition-type decode-and-forward relaying to improve the DMT of users in a wireless downlink. We deliberately align the relaying operation of one user with the source transmission for another user and show that the arisen inter-user and inter-relay interference could be efficiently managed without the need of transmit-side channel state information thanks to the downlink network topology and simple user scheduling. The effects of different interfering signal flows are analyzed in detail. We show that interference does not necessarily lead to system performance degradation, which is metric-specific, and good DMT is obtained both with and without the presence of the direct path.

A generalized multiuser cooperative concatenated coding framework is theoretically developed for not only cooperative diversity but also enhanced distance spectra. Novel schemes are proposed featuring flexible cooperation group, variable-size messages, and other practical concerns. In particular, users failing to detect their partners messages are allowed to cooperate without incurring error propagation.

Two-way wireless communication has regained significant attention recently over relay channels and network coding techniques have been adopted to improve the spectral efficiency. In the literature, most works assumed knowledge of the channel state information at the transmitter (CSIT) and focused mainly on the three-step network coding (NetC) and two-step superposition coding (SupC) schemes. This work instead targets systems without CSIT and explores in detail the features and limitations of SupC and NetC under both the two-step and three-step frameworks. Equal-rate bi-directional traffic is considered and a comparative study of the schemes is provided. The maximum goodput and robustness to channel knowledge discrepancies are evaluated. Key results include theoretically derived outage regions and performance, and the analysis of the complementary role of NetC and SupC. Special features of the three-step framework and a simple yet efficient protocol, adaptive relay-assisted and direct transmission (ARDT), which smartly exploits the direct link in the absence of CSIT, will also be presented. Optimal power allocation at the relay is also derived for the three-step SupC scheme by exploiting the channel reciprocity (no CSIT required). A significant advantage of ARDT is demonstrated over the recently developed two-step SupC scheme. Numerical results together with extensive discussions are provided.

In this paper, we formulate an optimization problem for secure resource allocation and scheduling in orthogonal frequency division multiple access (OFDMA) half-duplex decode-and-forward (DF) relay assisted networks. Our problem formulation takes into account artificial noise generation to combat a passive multiple antenna eavesdropper and the effects of imperfect channel state information at the transmitter (CSIT) in slow fading. The optimization problem is solved by dual decomposition which results in a highly scalable distributed iterative resource allocation algorithm. The packet data rate, secrecy data rate, power, and subcarrier allocation policies are optimized to maximize the average secrecy outage capacity (bit/s/Hz securely and successfully delivered to the users via relays). Simulation results illustrate that our proposed distributed iterative algorithm converges to the optimal solution in a small number of iterations and guarantees a non-zero secrecy data rate for given target secrecy outage and channel outage probability requirements.

Wireless network coding has attracted significant attention recently over relay channels. In this paper, we are interested in two-way wireless communication and explore the comparative advantages of two popular network coding strategies, binary network coding (NetC) and superposition coding (SupC), also named as analog network coding. In the literature, most works focused on NetC under a three-step framework and recently also SupC but under a two-step one. However, there is still little understanding on the truly optimal configuration. In addition, most works assumed channel knowledge at the transmitters while ignoring the presence of the direct link, which may not be valid in a fading environment. In this work, we focus on the outage performance instead and take into consideration also the time-varying nature of the direct link Outage regions of various schemes are theoretically characterized and their optimal operating conditions are identified, from which the best combined strategies are derived in terms of the maximum goodput and robustness to channel knowledge defects. Interesting new findings together with insightful discussions are provided.

Cooperative transmission has been gaining enormous attention where users share their resources for an overall improved performance. In this work, coded cooperation is of interest where a novel strategy is proposed with remarkable performance under various scenarios. In contrast to conventional works that focus on cooperative diversity, the proposed scheme successfully explores further coding benefits even when cooperative diversity is physically unavailable. Signal space diversity is also exploited for better cooperative diversity achievement. Imperfect inter-user channels are considered as well where the detrimental error propagation issue due to cooperative coding on erroneously detected messages is solved. Impressive mutual benefit is achieved for users with or without detection problems on their partners' messages.

Cooperative transmission has been of growing interest recently where users take advantage of each other's resources for better overall performance. In this paper, coded cooperation for wireless networks is considered and a novel cooperative concatenated coding strategy is proposed. In contrast to previous works, this scheme features additional coding benefits other than spatial diversity. Specifically, we show that remarkable error rate reduction can be achieved even when the receive SNR of all cooperating users is maintained at a specific level. The idea of interleaver gain of concatenated codes is exploited and it is shown that the error rate decreases dramatically with the number of cooperating nodes. Multiple cooperating users with unequal message sizes can also be supported easily and a reduced-complexity cooperative encoding structure is provided for good inter-user channels. Code design criteria are also investigated, as well as, the performance under various data-block sizes and number of cooperating users.

User cooperation is an emerging transmission framework where users act as relays of each other to provide extra diversity paths for better overall performance. However, most work in the literature focused only on the physical-layer properties and operations of a fixed pair of source and destination at a particular time instant. In contrast, we consider the downlink throughput optimization for an adaptive cellular network with both user cooperation and scheduling. Firstly, we evaluate several commonly used cooperation strategies under different fairness constraints. We show that the decode-and-forward protocol cannot deliver any capacity gain over its non-cooperative counterpart when users are scheduled for maximizing the overall system throughput. Secondly, the placement of relaying timeslots, which directly affects the cooperation performance, is investigated over existing frame structures so that user cooperation can be enabled easily in conventional networks. It is shown that multiple relays can actually be supported in different timeslots concurrently with other transmissions through scheduling and the corresponding optimal relays selection and power allocation are derived.

Orthogonal frequency division multiple access (OFDMA) and multicarrier code division multiple access (MC-CDMA) have recently drawn much attention for being potential candidates of future generation cellular systems. In single-cell scenario, while MC-CDMA is good at achieving frequency diversity when there is no channel state information available at the transmit side (CSIT), OFDMA achieves higher capacity than MC-CDMA due to its finer resolution in exploiting multiuser diversity with CSIT. Whether multiple-input-multiple-output (MIMO) MC-CDMA or OFDMA is a better option in multi-cell system remains unjustified in the literature. In this paper, we study the ergodic capacity and the goodput of MIMO-MC-CDMA and MIMO-OFDMA downlink systems with CSIT in multi-cell scenario assuming that the base station has the knowledge of the average inter-cell interference level only. Several types of users modeling different interference patterns are considered: (I) high data rate delay-insensitive users, (II) high data rate delay-sensitive .users, and (III) voice users (low data rate and bursty). Optimal resource allocation algorithms are used to compute the capacities of the systems, while a simple heuristic is used to obtain the achievable goodputs for both systems. The effects of path loss, number of antennas and different user types are studied and insightful results are obtained. We find that OFDMA has a higher system goodput for both Type I and Type II high data rate users, while MC-CDMA has a higher goodput for Type III users. Compared to MC-CDMA, the goodput of an OFDMA system is more sensitive to the activity factor of the voice users and suffers from noticeable loss. This demonstrates the superiority of the two systems in different practical situations.

In this paper, we examine and compare the potential maximum sum capacity of downlink multiple-input-multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) and multiple-input-multiple-output multicarrier code division multiple access (MIMO-MC-CDMA) in a single-cell multiuser environment with channel side information at the transmitter, with and without a fairness constraint. The resource allocation is formulated as a cross-layer optimization framework and optimal power allocation and user selection algorithms are proposed for both scenarios. We find that for delay-sensitive applications, where fairness is imposed, the performance gain of OFDMA over MC-CDMA is quite large at moderate path loss exponents and number of antennas. However, for delay-insensitive applications, the benefits of OFDMA over MC-CDMA are significantly reduced when the path loss exponent or the number of antennas is large

User/node cooperation has recently been receiving much attention especially in wireless sensor networks. It is shown that spatial diversity can be achieved even in a single-antenna system by exploiting the underlying virtual antenna array structure of multiple users/nodes. So far, much work has been devoted to single-antenna networks with only the channel state information at the receivers (CSIR). Motivated by the increasing importance of time division duplex (TDD) operation for future generation systems where channel state information is also available at the transmitter (CSIT), we consider CSIT-assisted user cooperation. Nevertheless, a constraint of limited capacity for control messages exchange among cooperating nodes is imposed, which is particularly true for a large cooperating group. In this paper, we further relax the constraint of a single antenna to an arbitrary number for all nodes. A simple and efficient cooperation protocol is proposed and analyzed for enhancing the transmission link quality

Orthogonal frequency division multiplexing (OFDM) is robust to inter-symbol interference and offers delay diversity that avoids large time-domain interleavers which in turn induce long encoding and decoding latency. We consider a high code-rate differentially modulated OFDM system for broadband wireless communications. By treating the differential modulation scheme as a rate-1 recursive convolutional code, turbo decoding can be applied and numerical results show very good performance, compared to that of the traditional coded coherent modulation schemes. With an extra differential code, large coding gains are achieved, without bandwidth expansion, for both the bit error rate (BER) and frame error rate (FER) in the AWGN channel while significant FER gain can be achieved in broadband frequency-selective radio channels with only a few iterations.