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This article studies a mobile-edge computing (MEC) system, where an access point (AP) and a relay node serve a user terminal over multicarrier subchannels. In the MEC system, the relay can assist not only task offloading but also task computation. Aiming at minimizing total energy consumption at the user terminal and the relay, the resource allocations, such as subcarrier allocation, power allocation, task partition, and offloading time and computation time allocation, are to be optimized, subject to a given task computation delay constraint. To achieve this goal, a novel cooperative MEC protocol is designed, where multicarrier subchannels are utilized for parallel task offloading by integrating the rateless coding technique. Then, under the newly designed protocol, the resource allocation optimization problem is formulated as a mixed-integer programming (MIP) problem that is challenging to solve. To tackle this MIP problem, continuous relaxation and algebraic transformation techniques are applied to transform it into a convex problem in order to reveal the lower bound of energy consumption performance. After that, by equivalently rewriting the integer subcarrier allocation constraint in the original optimization problem as the intersection of a convex set and a d.c. (difference of two convex sets) set, the problem is solved by the successive convex approximation to achieve a practical and efficient resource allocation scheme. Simulation results show that the proposed jointly cooperative task offloading and computation scheme can significantly reduce the energy consumption as compared to the baseline schemes, where the relay only assists the task offloading or task computation.

This article studies a network where a transmitter communicates with a receiver by hybrid communications that consist of passive information transmission (IT) via backscatter communication (BC) and active IT via wireless-powered communication (WPC). Because the circuit energy consumption in the passive IT of BC is much lower than that in the active IT of WPC, BC usually achieves a higher data transmission rate than WPC. Thus, it was suggested in the literature that the network throughput performance could not be improved by hybrid communications. However, our work in this article demonstrates that the throughput can be enhanced by a newly designed hybrid communication strategy. To demonstrate this, we develop a novel protocol that enables the transmitter to adaptively switch its operation between BC, active IT, and energy harvesting in one time block while scheduling energy consumption flexibly among multiple time blocks. Under the developed protocol, we formulate an optimization problem to jointly optimize the operation mode and resource allocation at the transmitter. The formulated problem is difficult to solve because the energy scheduling at the transmitter is coupled across multiple time blocks, and noncausal channel state information (CSI) is required. To address this problem, we first solve a simplified optimization problem via dynamic programming (DP) and a layered optimization method by assuming that the noncausal CSI is known. Then, we employ an approximate DP approach to solve the original problem with causal CSI. Finally, we verify by simulations that the proposed scheme can achieve superior throughput performance.

This letter studies the optimization of a UAV-aided vehicular communication system where a rotary-wing UAV serves a moving vehicle. Considering the propulsion energy of the UAV, our goal is to maximize the energy efficiency (EE) of the system by optimizing UAV trajectory. Due to the causality of the vehicle location information (VLI), the formulated optimization problem is challenging to solve. To address the VLI causality issue, a vehicle’s trajectory prediction framework is proposed, in which a short-term accurate trajectory prediction model and a long-term coarse trajectory prediction model are integrated. Based on the proposed vehicle’s trajectory prediction framework, a novel receding horizon optimization method is proposed to transform the EE maximization problem into a sequence of optimization problems, in which a time window of the short-term vehicle’s trajectory prediction is successively moved along the vehicle’s trip. By solving the sequence of optimization problems, an energy-efficient UAV’s trajectory optimization algorithm is obtained. Simulation results verify that our proposed scheme can achieve about 20%-40% EE performance gains over existing baseline schemes.

In this paper, an attempt to exploit the benefits of data-driven methods in solving joint relay selection and beamforming for non-regenerative relay networks has been made. The common relay selection and beamforming optimization problem aiming to maximize the receiver’s achievable rate under the constraint of relay transmit power is intrinsically hard since the mixed discrete and continuous variables. The direct map from channel state information to select a relay with optimized beamforming weights via data-driven methods often fails to yield good results. To overcome this difficulty, we propose a two-stage algorithm based on data-driven method. Firstly, we convert relay selection to a multi-class classification problem, and a support vector machine (SVM) based data-driven scheme is proposed to determine the best relay. After the relay is selected, we utilize a closed-form solution to obtain the corresponding relay beamforming weights. Since the number of relays is often more than two, the sample imbalance problem exists in the classification problem, considered in our proposed data-driven scheme. The core idea of SVM based classification method is to train the optimal parameters of the SVM classifier through a large number of offline sample data. In this way, the computation of relay selection can be transferred to offline SVM training. Simulation results demonstrate that the performance of the proposed method is close to that of the global optimal relay selection scheme. Moreover, our proposed scheme has much lower complexities than the global optimal relay selection scheme, especially when the number of relays is large.

This paper considers the distributed beamforming design for a simultaneous wireless information and power transfer (SWIPT) in two-way relay network, which consists of two sources, K relay nodes and one energy harvesting (EH) node. For such a network, assuming perfect channel state information (CSI) is available, and we study two different beamforming design schemes. As the first scheme, we design the beamformer through minimization of the average mean squared error (MSE) subject to the total transmit power constraint at the relays and the energy harvesting constraint at the EH receiver. Due to the intractable expression of the objective function, an upper bound of MSE is derived via the approximation of the signal-to-noise ratio (SNR). Based on the minimization of this upper bound, this problem can be turned into a convex feasibility semidefinite programming (SDP) and, therefore, can be efficiently solved using interior point method. To reduce the computational complexity, a suboptimal beamforming scheme is proposed in the second scheme, for which the optimization problem could be recast to the form of the Rayleigh–Ritz ratio and a closed-form solution is obtained. Numerical results are provided and analyzed to demonstrate the efficiency of our proposed beamforming schemes.

This paper studies power-splitting relaying (PSR) in an orthogonal-frequency-division-multiplexing (OFDM) amplify-and-forward (AF) two-way relay network (TWRN) which implements simultaneous wireless information and power transfer (SWIPT), with the goal of maximizing information exchanging rates of two terminals in the network. To achieve this, we investigate the joint optimization of power-splitting (PS) ratio of the relay and power allocation over subcarriers. The formulated optimization problem not only bears a non-convex energy-harvesting constraint of the relay but also tightly couples the PS ratio and power allocation variables in the non-convex objective function. To tackle this challenging problem, we show that it can be transformed into a problem which fits well in the d.c.( d ifference of two c onvex functions) programming framework by two different approaches, and thus can be effectively solved by two constrained concave-convex procedure based algorithms, respectively. Simulation results show that the performance of the PSR-based OFDM AF TWRN employing our proposed schemes is not only better than that of the existing time-switching based relaying OFDM AF TWRN, but also superior over that of the PSR-based OFDM AF TWRN employing the simple scheme with fixed PS ratio.

This paper studies a new design of a wireless-powered relay network to improve its throughput performance by opportunistic energy harvesting (EH) and information relaying (IR) at the relay node. To enable the relay to implement opportunistic EH and IR, a new protocol, namely, the adaptive harvest-store-forward (AHSF) protocol, is proposed. In the AHSF protocol, the relay is allowed to adaptively switch between an EH mode and a power-splitting (PS)-based IR mode at the beginning of each time frame; moreover, the relay can adaptively determine the amount of energy for IR based on the wireless channel conditions over one finite time horizon, which consists of multiple consecutive time frames. Then, under the proposed protocol, a throughput maximization problem is formulated to optimize the relay operation mode (i.e., EH mode or IR mode) and resource allocation (i.e., time slot, PS ratio, and power allocation) in the IR mode in a finite time horizon. The formulated problem is coupled over time and, thus, is intractable. To address the intractability, dynamic programming is employed to transform the problem into a series of Bellman equations. Based on the Bellman equations, the optimal decision criteria for the relay operation mode is derived, and a time-coupled optimization problem for the resource allocation in the IR mode is addressed by decoupling into two subproblems for two adjacent time frames. By solving the subproblems via convex programming, an optimization policy with causal channel-state information (CSI), which can be implemented online in real time, and an optimization policy with full CSI, which is used to reveal the upper bound of the achieved throughput, are achieved. The simulation results verify that the proposed design can yield significant throughput gains over the existing wireless-powered relaying schemes.

Energy harvesting (EH) is a promising technique for cognitive relaying transmission (CRT) where secondary users (SUs) and relaying nodes do not have a constant power supply each. Unlike conventional CRT where the end-to-end data rate is usually maximised without taking into account the energy consumption at the source and relay, in this study, the energy consumption is characterised by means of energy efficiency, defined as the achievable data rate per Joule. In particular, the energy states at each node (either at a SU or a relay) is modelled as a finite-state Markov chain and the transmit power at a node is optimally allocated by jointly accounting for the interference threshold prescribed by primary users (PUs), the maximum allowable transmit power and the harvested energy at the node. To maximise the energy efficiency, a best relay selection criterion is proposed and the subsequent optimal transmit power allocation is initially formulated as a non-linear fractional programming problem and, then, equivalently transformed into a parametric programming problem and, finally, solved analytically by using the classic Karush–Kuhn–Tucker conditions. With extensive Monte-Carlo simulation results, the effectiveness of the proposed relay selection algorithm and corresponding optimal power allocation strategy are corroborated.

Energy harvesting (EH) is a promising strategy to prolong the operation of energy-constrained wireless systems. Simultaneous wireless information and energy transfer (SWIET) is a potential EH technique which has recently drawn significant attention. By employing SWIET at relay nodes in wireless relay systems, the relay nodes can harvest energy and receive information from their source nodes simultaneously as radio signals can carry energy as well as information at the same time, which solves the energy scarcity problem for wireless relay nodes. In this paper, we study SWIET for nonregenerative orthogonal-frequency-division multiplexing (OFDM) amplify-and-forward systems in order to maximize the end-to-end achievable rate by optimizing resource allocation. Firstly, we propose an optimal energy-transfer power allocation policy which utilizes the diversity provided by OFDM modulation. We then validate that the ordered-signal-to-noise ratio (SNR) subcarrier pairing (SP) is the optimal SP scheme. After that, we investigate the information-transfer power allocation (IPA) and EH time optimization problem which is formulated as a non-convex optimization problem. By making the approximation at high SNR regime, we convert this non-convex optimization problem into a quasi-convex programming problem, where an algorithm is derived to jointly optimize the IPA and EH time. By analytical analysis, we validate that the proposed resource allocation scheme has much lower computational complexity than peer studies in the literature. Finally, simulation results verify the optimality of our proposed resource allocation scheme.

Energy harvesting (EH) is promising to solve energy scarcity problem in energy-constrained wireless networks. In this letter, we investigate simultaneous power and information transfer in two-way amplify-and-forward orthogonal-frequency-division-multiplexing relay networks, where an energy-constrained relay node equipped EH device harvests energy from two terminals and uses the energy to forward information in a time-switching (TS) manner. By jointly designing TS ratios of EH and information processing at the relay, as well as power allocation over all the subcarriers at two terminals and relay, our objective is to maximize end-to-end achievable rate of two-way relay networks subject to transmit power constraint at two terminals and EH constraint at relay. The formulated problem is hard to tackle because the EH constraint is non-convex and the objective rate function has a highly non-convex structure. We propose to solve this non-convex problem by transforming it into a nonlinear fractional problem, which can be solved by successive convex approximation and Dinkelbach’s procedure, and thus propose an efficient iterative algorithm that achieves superior performance with fast convergence speed.

This paper studies resource allocation in wireless-powered orthogonal-frequency-division multiplexing (OFDM) amplify-and-forward (AF) or decode-and-forward (DF) relay networks with time-switching (TS) based relaying. Our objective is to maximize end-to-end achievable rates by optimizing TS ratios of energy transfer (ET) and information transmission (IT), power allocation (PA) over all subcarriers for ET and IT as well as subcarrier pairing (SP) for IT. The formulated resource allocation problem is a mixed integer programming (MIP) problem, which is prohibitive and fundamentally difficult to solve. To simplify the MIP problem, we firstly provide an optimal ET policy and an optimal SP scheme, and then obtain a nonlinear programming problem to optimize TS ratios and PA for IT. Nevertheless, the obtained nonlinear programming problem is non-convex and still hard to tackle directly. To make it tractable, we transform the non-convex problem into a fractional programming problem, which is further converted into an equivalent optimization problem in subtractive form. By deriving the optimal solution to the equivalent optimization problem, we propose a globally optimal resource allocation scheme which bears much lower complexity as compared to the suboptimal resource allocation in the literature. Finally, our simulation results verify the optimality of our proposed resource allocation scheme and show that it outperforms the existing scheme in literature.

In this paper, we consider simultaneous wireless information and energy transfer in an orthogonal-frequency-division-multiple-access decode-and-forward relay network, in which an energy-constrained relay node harvests energy from a source node and uses the harvested energy to forward information to multiple destination nodes. Our objective is to maximize the end-to-end sum rate by resource allocation, subject to transmit power constraint at the source and energy-harvesting (EH) constraint at the relay. A non-convex and mixed-integer programming (MIP) problem is formulated to optimize time-switching (TS) ratios of EH and information decoding at the relay, TS ratio of information transmission from relay to destinations, subcarrier allocation as well as power allocation (PA) over all subcarriers at source and relay. We propose to decouple this problem into a convex problem and an MIP problem in fractional form. To solve the MIP problem, we transform it into an equivalent optimization problem in subtractive form which has a tractable solution. As a result, we propose a novel scheme to achieve jointly optimal TS ratios, subcarrier allocation and PA. Simulation results verify the optimality of our proposed resource allocation scheme.

In this paper, we investigate the joint power allocation and secure relay beamforming problem in a no-orthogonal multiple access (NOMA) amplify-and-forward relay network with two users. Our goal is to maximize the rate of the user who has better channel conditions, subject to total power constraints of the two users and a minimum signal-to-interference-ratio constraint of the user who has worse channel conditions. The formulated problem is non-convex and hard to tackle directly. To solve the problem, we firstly propose an algorithm which employing one-dimensional search to obtain the optimal solution. Since one-dimensional search may involve high computational complexity, we then propose to solve the problem with a sequential parametric convex approximation (SPCA) based iterative algorithm, which can achieve a locally optimal solution with low computational complexity. To further improve the network performance, we finally propose to solve the problem with a second-order cone programming (SOCP) based iterative algorithm. Simulation results demonstrate that the SOCP based iterative algorithm, which has lower computational complexity than one-dimensional iterative algorithm, has the performance close one-dimensional iterative algorithm. The SPCA based iterative algorithm has lowest computational complexity among proposed three methods, however, it's performance is worse.

Wireless powered relaying is a promising cooperative communication technique which has drawn much attention recently. In this paper, we investigate a cooperative communication system where a source node (SN) communicates with a destination node (DN) with the help of an energy-constrained relay node (RN) in the presence of interference. We consider a general scenario where a direct link between SN and DN is available, though the quality may be poor due the long distance or obstacles between the two nodes. Based on the concepts of energy diversity (ED) and opportunistic energy harvesting (OEH), we first provide a new ED-OEH protocol, which allows RN harvests energy or processes information on a time-frame basis. Then, under the ED-OEH protocol, we propose a novel wireless power and information transfer algorithm for the cooperative communication system, which can adaptively switch the relay operation mode according to the amount of harvested energy, the interference power and the quality of the system links to efficiently utilize the ED of the direct link and achieve OEH in the investigated system to improve the system throughput. To verify the performance of our proposed algorithm, we conduct comprehensive computer simulations with various system settings. Simulation results show that our proposed scheme achieves significant throughput gains over the existing schemes in literature.

Wireless power transfer is an effective way to prolong the operation time of energy-constrained wireless networks. This paper addresses the achievable rate optimization of a large-scale multiple input single output (MISO) orthogonal frequency division multiplexing (OFDM) system with a wireless powered decode-and-forward (DF) relay. The source is equipped with a large numbers of antennas, and maximum ratio transmission beamforming is used for both power transfer and information transmission from the source to the relay. The power allocation (PA) and time-switching (TS) ratios of power transfer and information transmission from the source to the relay as well as information transmission from the relay to the destination are jointly optimized to maximize end-to-end transmission rate, and thus a jointly optimal TS and PA scheme is proposed. Computer simulation results have validated the optimality of the proposed scheme.

By integrating the concept of effective capacity, the quality-of-service (QoS)-driven power and bandwidth allocation for heterogeneous wireless networks are investigated. The objective is to improve the power efficiency while providing delay QoS guarantees for the multi-mode mobile terminal users. First, the joint power and bandwidth allocation problem is formulated as a convex problem. Then, by solving the problem in the dual domain, an iterative algorithm to achieve the jointly optimal power and bandwidth allocation is proposed. The analytical results show that the optimal power and bandwidth allocation depends not only on the channel quality, but also on the delay QoS constraints. The numerical results verify that the proposed scheme can achieve the best power efficiency compared to the existing schemes.

In this paper, we investigate the quality-of-service QoS driven subcarrier pairing and power allocation for two-hop amplify-and-forward OFDM relay systems. By integrating the concept of effective capacity, our goal is to maximize the system throughput subject to a given delay QoS constraint. We propose a jointly optimal subcarrier pairing and power allocation scheme, which can be implemented with two separate steps. First, pair the subcarriers over the source-relay channel and relay-destination channel by the descending order of the subcarriers' channel gains. Second, by making use of the derived equivalent end-to-end channel gains of the subcarrier pairs, optimally allocate power over the subcarrier pairs, and then optimally partition the power of the subcarrier pairs between the source and the relay. The simulation results show that our proposed scheme can efficiently provide different levels of delay QoS guarantees, even if under stringent delay QoS constraints. The simulation results also verify that our proposed scheme shows significant superiorities over the other existing schemes. Copyright © 2013 John Wiley & Sons, Ltd.

Simultaneous wireless information and energy transfer (SWIET) is a promising energy harvesting (EH) technique to power energy-constrained wireless nodes in wireless communications. In wireless relay networks, employing SWIET at the source-to-relay link, an energy-constrained relay node can harvest energy from the radio-frequency (RF) signals transmitted by a source node while assisting information relaying, and thus its lifetime can be prolonged. This paper investigate the resource allocation in a SWIET-based two-hop amply-and-forward or decode-and-forward relay network with a power-splitting EH receiver at the relay. The goal is to maximize the end-to-end achievable rate. Firstly, this paper formulate the resource allocation problem as a non-convex optimization problem. Then, this paper transform the non-convex problem into a convex problem by algebraic transformations. By solving the convex problem, this paper obtain the optimal resource allocation policy for the AF or DF relay network. Simulation results verify the optimality of the proposed resource allocation policy in this paper.

Simultaneous Wireless Information and Energy Transfer (SWIET) is a promising energy harvesting technique to power energy-constrained wireless nodes in wireless communications. In wireless relay networks, employing SWIET at the source-to-relay link, an energy-constrained relay node can harvest energy from the radio-frequency (RF) signals transmitted by a source node while assisting information relaying, and thus its lifetime can be prolonged. In this paper, we investigate an orthogonal-frequency-division multiplexing (OFDM) amplify-and-forward (AF) relay network with SWIET. The goal is to maximize the end-to-end achievable rate. Unlike the work in literature where only peak transmit power constraint at source is considered, we consider both peak and average transmit power constraints at source, by which the operation time of the source's batteries can be extended. The formulated problem is non-convex because the average transmit power constraint at source and the EH constraint at relay are non-convex. To tackle this non-convex problem, we transform it into two fractional programming problems. By solving the two fractional programming problems with Dinkelbach's procedure, we propose two algorithms to achieve optimal resource allocations. Simulation results verify the optimality of our proposed resource allocation scheme and show that the average power constraint at source has much impact on the end-to-end achievable rate of the SWIET-based OFDM AF relay network.

In this paper, we investigate the quality-of-service (QoS) driven subcarrier pairing and power allocation for two-hop decode-and-forward (DF) OFDM relay systems. By integrating the concept of effective capacity, our goal is to maximize the system throughput subject to a given delay-QoS constraint. Based on whether the destination can receive the signal transmitted by the source, we consider two scenarios, i.e. OFDM DF relay systems without diversity and OFDM DF relay systems with diversity, respectively. For OFDM DF relay systems without diversity, we demonstrate that the jointly optimal subcarrier pairing and power allocation can be implemented with two separate steps. For OFDM DF relay systems with diversity, we propose an iterative algorithm to achieve jointly optimal subcarrier pairing and power allocation. Furthermore, we find that the analytical results show different conclusions for the two types of OFDM relay systems. For OFDM relay systems without diversity, the optimal power allocation depend on not only the channel quality of subcarriers but also the delay QoS constraints, while the optimal subcarrier pairing just depends on the channel quality of subcarriers. For OFDM relay systems with diversity, both the optimal subcarrier pairing and power allocation depend on the channel quality of subcarriers and the delay QoS constraints. Simulation results show that our proposed scheme offers a superior performance over the existing schemes.

By integrating the concept of effective capacity, we propose the resource allocation schemes for subcarrier-pair based OFDM decode-and-forward and amplify-and-forward relay systems for quality-of-service QoS guarantees. The objective is to maximize the system throughput subject to a given statistical delay QoS constraint. First, we pair the subcarriers over the source-relay channel and the relay-destination channel by the descending order of the subcarriers' channel gains. Second, by making use of the derived equivalent end-to-end channel gains of the subcarrier pairs, we apply joint water-filling power allocation over the subcarrier pairs and then partition the power of the subcarrier pairs between the source and the relay. We prove that as the equivalent end-to-end channel gains of the subcarrier pairs are given, the combination of sorted subcarrier pairing and joint water-filling power allocation is jointly optimal to maximize the effective capacity. The simulation results show that our proposed schemes achieve the highest effective capacity for OFDM decode-and-forward and amplify-and-forward relay systems as compared with other existing schemes. The results also verify that our proposed schemes can efficiently provide different levels of delay QoS guarantees, even if under the stringent delay QoS constraints. Copyright © 2012 John Wiley & Sons, Ltd.

Employing energy harvesting (EH) at a relay node with simultaneous wireless information and energy transfer (SWIET) is promising to prolong the lifetime of energy-constrained relay systems. In this paper, we investigate the optimal resource allocation in an orthogonal-frequency-division multiplexing (OFDM) decode-and-forward (DF) relay system that implements SWIET at the source-to-relay link in order to maximize the end-to-end achievable rate. We first propose an optimal energy-transfer power allocation (EPA) policy which can utilize the frequency diversity provided by OFDM modulation. We then validate that the ordered-SNR subcarrier pairing is globally optimum. To optimize EH time and information-transmission power allocation (IPA), we formulate the involved problem as a non-convex programming problem, and then transform it into a quasi-convex optimization problem. By solving this quasi-convex optimization problem with the bisection search method, we propose an algorithm which can jointly optimize the EH time and IPA. By analytical analysis, we validate that our proposed resource allocation scheme has much lower computational complexity than the peer studies in the literature. Finally, our simulations demonstrate the optimality of our proposed resource allocation scheme.

The transceiver design in a cognitive radio networks is studied, where all nodes are deployed with multiple antennas. A primary point-to-point MIMO link is authorized to utilize the spectrum. Ignoring the secondary users, the primary transmitter and receiver are designed based on the traditional singular value decomposition method. The secondary users consist of multiple source-destination pairs, assisted by multiple two-hop non-regenerative relays. The transceivers of the secondary users are supposed to cancel the interferences from the secondary users to the primary receiver, the interferences from the primary transmitter to the secondary users, and the inter-secondary-user interferences, in order to guarantee the communication quality of both the primary and secondary users. Based on the zero-forcing method, the transceivers are designed to minimize the noise power amplified by the relays. It is shown that the proposed transceiver design algorithm outperform the existed algorithm.

In this paper, a decode-and-forward multiple-input multiple-output relay system with relay selection under unequal power co-channel interferences is considered. In the system, transmit beamforming is applied at each transmitter, and maximal ratio combining is applied at each receiver. The closed- form exact outage probability expression of the system over Rayleigh fading channels has been derived. The result is valid for arbitrary antenna number and interferer number. Numerical investigation shows that the theoretical result makes an exact match with the computer simulations.

In this paper, a decode-and-forward multiple-antenna relay system with opportunistic relaying is considered. The relays apply maximum ratio combining for reception and maximum ratio transmission for transmission. Practically, the system will face co-channel interferences. In order to evaluate the effect of interferences to the system, a closed-form exact outage probability expression of the system over Rayleigh fading channels is derived. Also, Numerical investigation shows that the theoretical result makes an exact match with computer simulations. The derived result can be used to quickly evaluate the exact outage performances of the system in practical scenarios.

To efficiently utilize the advantages of orthogonal-frequency-division-multiplexing (OFDM) and wireless relay techniques, we propose the resource allocation schemes with statistical quality-of-service (QoS) guarantees for the wireless OFDM amplify-and-forward (AF) relay system in this paper. Firstly, we formulate the resource allocation problems for the wireless OFDM AF relay system with QoS guarantees as a nonconvex problem. Then by defining proper amplifier gain for the OFDM AF relay system, we transform the nonconvex problem into a convex problem, and thereby propose the suboptimal resource allocation scheme. Although the proposed resource allocation scheme is suboptimal, the numerical results show that the OFDM relay system applied with our scheme can achieve the significant effective capacity gain over the single-hop OFDM wireless system and the single-carrier wireless relay system with optimal resource allocation schemes. On the other hand, the numerical results also show that our proposed scheme outperforms other resource allocation schemes for the OFDM AF relay system.

The robustness differences of multiuser maximum ratio combining (MRC), MIMO-MRC and optimum combining (OC) systems with imperfect channel state information are analyzed. Using the multiuser average capacity difference CDR with channel estimation error, the robustness of the three systems are illustrated in a flat Rayleigh fading channel with multiple interferers. Performance results show that OC system always outperforms the MIMO-MRC and MRC systems when there are no co-channel interferences, whereas MIMO-MRC and MRC outperform the OC system when there are multiple co-channel interferences.

In this paper, considering the channel estimation error and the presence of co-channel interferences (CCIs), we analyze the performance of multiuser multiple-input multiple-output wireless systems employing transmit beamforming and maximum ratio combining (MIMO MRC). Exact closed-form cumulative distribution function expression of the signal-to-noise-plus-interference ratio (SINR) is derived. Second, we present the average capacity of MIMO MRC systems with multiuser diversity. Numerical results demonstrate the accuracy of our analytical expressions.