Retrieval Augmented Generation (RAG) is an essential agent for Large Language Model (LLM) aided Description Language (HDL) tasks, addressing the challenges of limited training data and prohibitively long prompts. However, its performance in handling ambiguous queries and real-world, repository-level HDL projects containing thousands or even tens of thousands of code lines remains limited. Our analysis demonstrates two fundamental mismatches, structural and vocabulary, between conventional semantic similarity-based RAGs and HDL codes. To this end, we propose HDLxGraph, the first framework that integrates the inherent graph characteristics of HDLs with RAGs for LLM-assisted tasks. Specifically, HDLxGraph incorporates Syntax Trees (ASTs) to capture HDLs’ hierarchical structures and Data Flow Graphs (DFGs) to address the vocabulary mismatch. In addition, to overcome the lack of comprehensive HDL search benchmarks, we introduce HDLSearch, an LLMgenerated dataset derived from real-world, repository-level HDL projects. Evaluations show that HDLxGraph improves search, debugging, and completion accuracy by $\mathbf{1 2. 0 4 \%} \boldsymbol{/} \mathbf{1 2. 2 2 \%} \boldsymbol{/} \mathbf{5. 0 4 \%}$ and by $\mathbf{1 1. 5 9 \%} \boldsymbol{/} \mathbf{8. 1 8 \%} \boldsymbol{/} \mathbf{4. 0 7 \%}$ over state-of-the-art similarity-based RAG and software-code Graph RAG baselines, respectively. The code of HDLxGraph and HDLSearch benchmark are available at https://github.com/UMN-ZhaoLab/HDLxGraph.

As program workloads (e.g., AI) increase in size and algorithmic complexity, the primary challenge lies in their high dimensionality, encompassing computing cores, array sizes, and memory hierarchies. To overcome these obstacles, innovative approaches are required. Agile chip design has already benefited from machine learning integration at various stages, including logic synthesis, placement, and routing. With Large Language Models (LLMs) recently demonstrating impressive proficiency in Hardware Description Language (HDL) generation, it is promising to extend their abilities to 2.5D integration, an advanced technique that saves area overhead and development costs. However, LLM-driven chiplet design faces challenges such as flatten design, high validation cost and imprecise parameter optimization, which limit its chiplet design capability. To address this, we propose MAHL, a hierarchical LLM-based chiplet design generation framework that features six agents which collaboratively enable AI algorithm-hardware mapping, including hierarchical description generation, retrieval-augmented code generation, diverseflow-based validation, and multi-granularity design space exploration. These components together enhance the efficient generation of chiplet design with optimized Power, Performance and Area (PPA). Experiments show that MAHL not only significantly improves the generation accuracy of simple RTL design, but also increases the generation accuracy of real-world chiplet design, evaluated by Pass@5, from 0 to 0.72 compared to conventional LLMs under the best-case scenario. Compared to state-of-the-art CLARIE (expert-based), MAHL achieves comparable or even superior PPA results under certain optimization objectives.

Mixture-of-Experts (MoE) architecture offers enhanced efficiency for Large Language Models (LLMs) with modularized computation, yet its inherent sparsity poses significant hardware deployment challenges, including memory locality issues, communication overhead, and inefficient computing resource utilization. Inspired by the modular organization of the human brain, we propose $\texttt{Mozart}$, a novel algorithm-hardware co-design framework tailored for efficient training of MoE-based LLMs on 3.5D wafer-scale chiplet architectures. On the algorithm side, $\texttt{Mozart}$ exploits the inherent modularity of chiplets and introduces: ($1$) an expert allocation strategy that enables efficient on-package all-to-all communication, and ($2$) a fine-grained scheduling mechanism that improves communication-computation overlap through streaming tokens and experts. On the architecture side, $\texttt{Mozart}$ adaptively co-locates heterogeneous modules on specialized chiplets with a 2.5D NoP-Tree topology and hierarchical memory structure. Evaluation across three popular MoE models demonstrates significant efficiency gains, enabling more effective parallelization and resource utilization for large-scale modularized MoE-LLMs.

In this paper, we investigate the physical layer security of an untrusted relay assisted over-the-air computation (AirComp) network, where each node is equipped with multiple antennas and the relay is operated in an amplify-and-forward mode. The relay receives the data from each sensor and sends them to the access point (AP) in the first and second time slot, respectively. The AP applies artificial noise (AN) to protect the aggregation of sensors’ data from being wiretapped by the untrusted relay in the first time slot. In particular, we are interested in minimizing the computation distortion measured by the mean-squared error (MSE) via jointly optimizing beamforming matrices at all nodes, subject to the MSE constraint at the relay and individual power constraints at the AP, the relay and each sensor. In the case of the perfect channel state information (CSI), we convert the nonconvex MSE minimization problem into a difference-of-convex (DC) form and propose a constrained concave-convex procedure that can obtain a local minimum to solve the DC problem. We also generalize the framework to an imperfect CSI case where the additional interference term due to incomplete interference cancellation is considered, and the nonconvex robust MSE minimization problem is solved by a proposed inexact block coordinate descent algorithm. Numerical results are presented to show the effectiveness of our proposed schemes.

Future ultrareliable low-latency tasks in the Internet of Things require finite blocklength transmissions. The spectral efficiency of finite blocklength transmissions, by incorporating nonorthogonal multiple access (NOMA), can be significantly improved. In conventional NOMA systems, the successive interference cancelation (SIC) is employed for signal decoding, which is optimal for sufficient long blocklength transmissions. However, for finite blocklength transmissions, the joint decoding instead of SIC is optimal. Considering the joint decoding, we study the decoding error probability and power allocation factor optimization problem, which aims at maximizing the effective throughput at the central user under the minimum-required effective throughput constraint at the cell-edge user. We put forward a 2-D search method to find the globally optimal solution and a low-complexity alternating optimization method to find the locally optimal solution. It is illustrated that our proposed joint decoding scheme has the higher effective throughput than the conventional SIC scheme.

This paper investigates the physical layer security issue in an amplify-and-forward (AF) multi-input multi-output (MIMO) two-way relay assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) network, where the simultaneous wireless information and power transfer (SWIPT) technology is employed to improve network energy efficiency. We consider the scenario that a pair of primary users and two pairs of secondary users (SUs) exchange information via a MIMO two-way relay, where the edge SU of each SU pair is untrusted and tries to wiretap the central SU’s information. For ensuring security, we aim to maximize the sum achievable secrecy rate (SASR) by jointly optimizing the power allocation at all users, power splitting factor and relay beamforming subject to the quality of service (QoS), energy harvesting and transmit power constraints. The formulated optimization problem is highly nonconvex due to coupling variables, thus it is challenging to solve. An effective path-following (PF)-based algorithm is proposed, which is proven to converge to a stationary point. Theoretical and simulation results show that the proposed PF-based algorithm has lower complexity than the state-of-art algorithm. To further reduce complexity, we proposed a zero-forcing (ZF)-based scheme. Numerical simulations show that the proposed PF-based algorithm achieves the same SASR as the state-of-art algorithm with moderate complexity, while the proposed ZF-based scheme strikes a good balance between performance and complexity.

Orthogonal time frequency space (OTFS) modulations are robust to time-varying frequency-selective fading channels. OTFS modulations operate in the delay-Doppler domain whereas two-dimensional (2D) orthogonal frequency division multiplexing (OFDM) modulations operate in the time-frequency domain. For 2D OFDM modulations in time-varying frequency-selective fading channels, we investigate the pilot pattern design problem, which minimizes the mean square error (MSE) of channel estimation. The MSE lower bound (LB) is theoretically derived to provide the design criterion. Based on the criterion, we show that the LB achieving design can be found by exhaustive 2D pilot location search. Exhaustive 2D search has high computational complexity. To reduce the complexity, sufficient conditions on the existence of LB achieving design are provided to decouple the 2D problem into two one-dimensional (1D) problems. For the 1D problem, we propose the LB achieving design when the number of possible pilot locations is divisible by the number of pilots. Simulation results illustrate that our proposed LB achieving design is superior to the random pilot pattern design and the bilinear interpolation channel estimation method.

In this paper, we investigate the secure beamforming design in multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) en-abled Internet of Things (IoT) networks, where a controller transmits confidential messages to multiple actuators and a cooperative controller acts as a jammer to prevent a potential eavesdropper from wiretapping the information. Our goal is to maximize achievable secrecy sum rate subject to the successful successive interference cancellation constraint and transmit power constraints of the controller and jammer. When channel state information (CSI) is perfect, we design the secure beamforming by developing an iterative optimization algorithm based on minimum mean square error (MMSE) method. When perfect CSI is not available, we model the channel errors as deterministically-bounded, and propose a robust secure beamforming design algorithm based on weighted MMSE and cutting-set method. The effectiveness of the proposed algorithms is verified by the simulation results.

Considering reconfigurable intelligent surfaces (RISs), we study a multi-cluster multiple-input-single-output (MISO) non-orthogonal multiple access (NOMA) downlink communication network. In the network, RISs assist the communication from the base station (BS) to all users by passive beamforming. Our goal is to minimize the total transmit power by jointly optimizing the active beamforming matrices at the BS and the reflection coefficient vector at the RISs. Because of the constraints on the RIS reflection amplitudes and phase shifts, the formulated quadratically constrained quadratic problem is highly non-convex. For the aforementioned problem, the conventional semidefinite programming (SDP) based algorithm has prohibitively high computational complexity and deteriorating performance. Here, we propose an effective second-order cone programming (SOCP)-alternating direction method of multipliers (ADMM) based algorithm to obtain the locally optimal solution. To reduce the computational complexity, we also propose a low-complexity zero-forcing based suboptimal algorithm. It is shown through simulation results that our proposed SOCP-ADMM based algorithm achieves significant performance gain over the conventional SDP based algorithm. Furthermore, when the target transmission rates of central and cell-edge users are 0.5 bps/Hz, our proposed NOMA RIS-aided system with 32 RIS elements has about 2.5 dB performance gain over the conventional massive multiple-input-multiple-output system with 64 transmit antennas.

Incorporating short-packet communications with non-orthogonal multiple access (NOMA) networks is able to achieve both low communication delay and high spectral efficiency. In this article, a wireless NOMA downlink short-packet communication system is studied. The system includes a base station, an entrusted central user, and an untrusted cell-edge user. The untrusted cell-edge user may eavesdrop the signals from the base station to the central user. We theoretically derive the average secure block error rate (BLER) of the central user in flat Rayleigh fading channels by utilizing the linear approximations on BLER and secure BLER. We also present the asymptotic average secure BLER at high signal-to-noise ratio (SNR). From numerical results, it is shown that our derived analytical average secure BLER matches the simulated one. Numerical results also illustrate that at high SNR, error floors occur. Furthermore, at high SNR, it is found that when the average secure BLER is not one, the power allocation factors for the central and cell-edge users have almost no impact on the average secure BLER.

Incorporating wireless caching in non-orthogonal multiple access (NOMA) networks is a promising technique to reduce the delivery latency and improve the quality of service. In this paper, we study a cache content placement optimization problem in a cellular NOMA downlink wireless caching network. Our goal is to minimize the average transmit power under the cache capacity constraints. The optimization problem is a non-linear integer programming, which is non-deterministic polynomial-time hard. To efficiently solve the problem, an alternating upper plane method based on quadratic knapsack problem (QKP) is proposed. To deal with the general situation that the sizes of files and cache capacities are not integers, an alternating method based on semidefinite relaxation is also proposed. Finally, a constrained concave-convex procedure-based iterative method is proposed to further reduce the computational complexity. Simulation results show that our proposed methods are superior to the schemes which cache the most popular files until the cache is full.

In this article, we study the secure beamforming design for a two-way cognitive radio (CR) Internet of Things (IoT) network aided with the simultaneous wireless information and power transfer (SWIPT). Located at the center of secondary network, the IoT controller helps to provide relay assistance and cooperative physical layer security (PLS) for two primary users (PUs) against an eavesdropper, while transmitting information and power to the other IoT devices (IoDs) with primary spectrum. To enhance the information security, we aim to maximize the secrecy sum rate (SSR) for PUs by jointly designing the beamforming matrix and vectors at the central controller. To efficiently solve the nonconvex problem, we first propose the branch-reduce-and-bound (BRB)-based algorithm to obtain an upper bound for the SSR and offer a feasible solution by Gaussian randomization, which demands two-level iteration and thus has high complexity. To strike a balance between the complexity and the performance, we then propose iterative algorithm based on constrained-convex-concave programming (CCCP) and a zero forcing (ZF)-based noniterative algorithm, the latter of which with lowest complexity is suitable for the central controller with limited-power supply. The simulation results are provided to demonstrate the effectiveness of our proposed optimization algorithms in comparison to the traditional schemes.

In this paper, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network, where the UAV with constant power supply first charges all users by transmitting wireless energy to all users simultaneously, and after that all users send their own information to the UAV. Our target is the joint optimization of the time allocation as well as the position of the UAV to make the uplink sum achievable rate for all users as large as possible. To solve this non-convex problem, we first derive the analytic optimal solution of the time allocation, which is expressed as the function of UAV position. The original problem, after substituting the derived optimal time allocation, is reformulated as a new optimization problem whose optimization variable is only UAV position. We propose a sequential unconstrained convex minimization-based algorithm to obtain the globally optimal solution. The simulation results demonstrate that the performance of our proposed algorithm matches with that obtained by two-dimensional exhaustive search. To decrease the complexity, a Dinkelbach-based algorithm to obtain the locally optimal solution is also proposed. The simulation results show that the performances of our proposed two algorithms are superior to the schemes without time allocation optimization and/or position optimization.

In[1], the title of the paper should be: “Joint Position and Time Allocation Optimization of UAV Enabled Wireless Powered Communication Networks.”

Considering that a legitimate full-duplex monitor aims to intercept the suspicious wireless transmission, we study the robust jamming design problem where imperfect channel state information is assumed. We model channel estimation errors as deterministically bounded and define the successful monitoring event as that the signal-to-interference-plus-noise ratio (SINR) at the legitimate monitor is higher than the SINR at the suspicious receiver. To increase the worst-case probability of successful monitoring, we propose a robust proactive monitoring via jamming scheme. From simulation results, our proposed robust scheme is superior to the nonrobust one.

Cooperative non-orthogonal multiple access (NOMA) systems inherit advantages of the NOMA protocol and the cooperative relay. In this paper, we propose cooperative NOMA systems in multiple-input-multiple-output channels. The whole transmission is divided into two phases. In the first phase, the base station broadcasts signals using the NOMA protocol to a central user and a cell-edge user. In the second phase, the central user helps the base station cooperatively relay signals intended for the cell-edge user. Our objective is to maximize achievable rate from the base station to the cell-edge user under transmit power constraints and achievable rate constraint from the base station to the central user. The difficulty of this problem is the joint beamforming of the base station and the central user in the second phase. We propose a constrained convex-concave procedure (CCCP)-based algorithm. To reduce computational complexity, we also propose a closed-form search-based suboptimal algorithm. Simulation results demonstrate that our proposed cooperative NOMA system with CCCP-based algorithm outperforms the conventional NOMA scheme. When achievable rate constraint to the central user is low, our proposed cooperative NOMA system with the closed-form search-based suboptimal algorithm outperforms the NOMA scheme.

To legitimately monitor suspicious wireless communications is an important issue. In this letter, we propose a proactive monitoring via jamming scheme over an amplify-and-forward relay network, which consists of a legitimate monitor, a suspicious source, a suspicious relay, and a suspicious destination. Our objective is to design the optimal jamming power to maximize average monitoring rate at the legitimate monitor. For the optimization problem, we propose to find the true optimal jamming power by a bisection search method. Furthermore, we propose an approximate optimal jamming scheme whose closed-form expression on average monitoring rate is theoretically derived. Simulation results demonstrate that our proposed true optimal jamming scheme outperforms passive monitoring and proactive monitoring via constant-power jamming. Furthermore, our proposed approximate optimal jamming scheme achieves almost the same average monitoring rate as true optimal one.

In this letter, we consider a cellular downlink multiple-input-multiple-output nonorthogonal multiple access (NOMA) secure transmission network, which consists of a base station, a central user, and a cell-edge user. The base station and two users are all equipped with multiple antennas. The central user is an entrusted user and the cell-edge user is a potential eavesdropper. We focus on secure beamforming optimization problem, which maximizes achievable secrecy rate of the central user subject to transmit power constraint at the base station and transmission rate requirement at the cell-edge user. The optimization problem is nonconvex. We employ majorization-minimization method to iteratively optimize a sequence of valid surrogate functions for the nonconvex optimization problem. Furthermore, in each iteration, we derive the semi-closed form solution to optimize the valid surrogate functions. Simulation results demonstrate that our proposed NOMA scheme outperforms the zero-forcing-based NOMA scheme and conventional orthogonal multiple access scheme.

Nonorthogonal multiple access (NOMA) is expected to be a promising technique for future wireless networks. In this letter, we investigate the secrecy sum rate optimization problem for a downlink multiple-input-multiple-output NOMA system that consists of a base station, multiple legitimate users, and an eavesdropper. Our objective is to maximize achievable secrecy sum rate subject to successful successive interference cancellation constraints and transmit power constraint. The formulated optimization problem is nonconvex. Motivated by the relationship between mutual information rate and minimum mean square error, we propose to transform the secrecy sum rate optimization problem into a biconvex problem. The biconvex problem is solved by alternating optimization method where in each iteration, we solve a second-order cone programming. Simulation results demonstrate that our proposed NOMA scheme outperforms conventional orthogonal multiple access scheme.

Non-orthogonal multiple access (NOMA) is a promising technology in future communication systems. In this paper, we propose a buffer-aided NOMA relaying system, which consists of a source, a relay, and two destinations. In the relaying system, the relay helps the source transmit packets to two destinations simultaneously using NOMA scheme. We theoretically derive outage probabilities of source-to-relay link and relay-to-destinations links considering two scenarios that the relay does and does not know the channel state information (CSI) from itself to two destinations. When the relay knows CSI, the obtained outage probability of relay-to-destinations links involves integration operation. Thus, we derive an upper bound and two lower bounds. Simulation results demonstrate that two lower bounds approach exact outage probability at low and high signal-to-noise ratios, respectively. We also propose a relay decision scheme for the buffer-aided NOMA relaying system. Based on the obtained system outage probability, we theoretically derive the diversity order. It is found that no matter whether the relay knows CSI or not, the diversity order of 2 can be achieved when the buffer size is larger than or equal to 3.

Non-orthogonal multiple access (NOMA) is a promising technology in future mobile communications. In this letter, we study the channel estimation and power allocation problem for the two-user NOMA downlink system with one strong user and one weak user. Firstly, we introduce a new type of linear estimator that aims at maximizing the average effective signal-to-interference-and-noise ratio (SINR) of the strong user with bounded average effective SINR guaranteed for the weak user. We propose a constrained concave convex procedure (CCCP)-based iterative algorithm to solve the estimation problem. Secondly, we also derive the maximum average effective SINR of the strong user under the traditional maximum-likelihood (ML)-based estimator and linear minimum-mean-square-error (LMMSE)-based estimator, respectively. Simulation results have shown that the proposed estimator outperforms the traditional ML and LMMSE estimators, indicating a new way of channel estimation and power allocation for the NOMA downlink systems.

Transceiver with Tomlinson-Harashima (TH) precoding outperforms the linear minimum mean-square-error (MSE) architecture in terms of minimum achievable MSE. In this paper, we investigate transceiver design optimization problem for nonregenerative multiple-input multiple-output cognitive relay networks (CRNs) with TH precoding. In the CRN, a secondary user (SU) source, an SU relay and an SU destination employ a TH precoder, a relay precoder, and a linear equalizer, respectively. For scenario in which SUs know perfect channel state information (CSI) from SUs to primary users, we propose an alternating optimization (AO)-based suboptimal algorithm. Given TH precoder and relay precoder, we derive a closed-form optimal solution of linear equalizer. Given relay precoder, TH precoder can be found by convex optimization. Given TH precoder, we transform nonconvex relay precoder design problem into a difference of convex programming and propose a constrained concave convex procedure-based iterative algorithm to find its local optimum. For scenario in which SUs know imperfect CSI, the channel uncertainties are modeled by worst case model. We derive equivalent worst case interference power constraints and extend the proposed AO-based suboptimal algorithm to cope with the worst case interference power constraints. Simulation results demonstrate that the proposed transceiver design with TH precoding outperforms linear transceiver designs.

In this paper, we investigate information and energy cooperation in cognitive non-regenerative two-way relay networks, where the multi-antenna secondary user (SU) transmitter harvests energy from primary users' (PUs) signals by the power splitting (PS) scheme to forward PUs' signals and transmit its own signals. Our objective is to design beamforming matrix and vector at the SU transmitter and PS factor to maximize achievable rate at SUs while maintaining achievable rate requirements at PUs subject to transmit power constraint at SU transmitter. We consider two scenarios that the SU transmitter knows perfect and imperfect channel state information of all links. For the former, we propose an optimal solution based on Charnes-Cooper transformation and 1-D search. We also propose a low-complexity suboptimal solution based on the zero-forcing (ZF) scheme and the algebraic norm-maximizing scheme. For the latter, channel uncertainties are modeled by a worst-case model. We propose to employ a ZF scheme and transform the problem into a semidefinite programming with the help of S-procedure and rank-one relaxation. The rank-one feasible solution is proposed to be recovered by the Gaussian randomization method. Simulation results demonstrate that our proposed designs outperform the beamforming scheme without energy cooperation.

For conventional subcarrier pairing scheme in cooperative orthogonal frequency division multiplexing decode‐and‐forward multi‐relay networks, to avoid interference, each subcarrier pair (SP) is assigned to a single relay. Over a specific subcarrier, the destination receives signals transmitted from the relay. In this study, to better exploit the degrees of spatial freedom, the authors propose to assign each SP to multiple relays. Thus, over a specific subcarrier, the destination receives signals transmitted from multiple relays. Under the total network power constraint, to maximise the sum transmission rate, they propose a joint resource allocation scheme, in which they jointly optimised the four types of resources: assisting relays selection, transmission mode selection, subcarrier pairing and power allocation. They further propose a suboptimal algorithm which can significantly reduce the computational complexity of the aforementioned optimal allocation scheme with sacrificing little on the performance. It is shown from simulation results that the author's proposed schemes have significant performance improvement over the resource allocation schemes in the literature.

Cognitive radio is a potential technique to solve the spectrum shortage problem in wireless communications. Integrating wireless relaying into the cognitive radio networks can further improve the spectrum efficiency. In this study, a multiple‐input multiple‐output cognitive relay network is considered, where the primary network (PN) consists of one transmitter‐receiver pair and the secondary network consists of multiple active source‐destination pairs and non‐regenerative relays. All nodes in the networks are deployed with multiple antennas. How to avoid interference to the PN is an important task in the secondary network design. The precoders of the secondary sources and the receivers of the secondary destinations are designed in a well‐known zero‐forcing way. The secondary relays forward signals for the secondary source‐destination pairs by beamforming. With interference cancellation constraints and individual transmit power constraints, a relay beamforming scheme is proposed to maximise the total signal‐to‐interference‐plus‐noise ratio (SINR) at the secondary destinations. Then a maximising minimum SINR relay beamforming scheme is further proposed to provide max–min fairness among the secondary source‐destination pairs. The beamforming problems are formulated into the quadratically constrained quadratic fractional programming problems, and are solved by the semi‐definite relaxation technique. The performances of these beamforming schemes have been verified by computer simulations.

For wireless networks which aim at high speed communication and computation, we propose a parallel analog function computation scheme via wireless multiple-access multiple-input-multiple-output channels. We consider that each sensor node only has imperfect channel state information from itself to the fusion center. Modeling the channel uncertainties by the worst-case model, the robust transceiver design problem for parallel analog function computation is formulated as a non-convex optimization problem which minimizes the worst-case mean-square-error subject to individual transmit power constraints. We then propose an alternating optimization algorithm to solve the problem. Simulation results demonstrate that the proposed robust scheme outperforms the non-robust one.

Employing energy harvesting (EH) at the relay with simultaneous wireless information and power transfer is promising to prolong lifetime of energy-constrained relay networks. In this letter, considering multicarrier decode-and-forward (DF) relay network with time-switching (TS) based relaying, we investigate the optimization problem which jointly designs TS ratios of EH and information-decoding at the relay, TS ratio of signal forwarding from relay to destination as well as power allocation (PA) over all subcarriers at source and relay. Our objective is to maximize end-to-end achievable rate of DF relay networks subject to transmit power constraint at source and EH constraint at relay. We propose to decouple the optimization problem into a convex problem and a quasi-convex problem. For the quasi-convex problem, we propose to solve it by bisection search. Simulation results demonstrate that our proposed joint TS and PA optimization scheme outperforms the scheme with fixed TS ratio of EH.

In this letter, considering the simultaneous wireless information and power transfer scheme, we study the robust artificial noise (AN)-aided secure transmission design in multiple-input-single-output channels where the channel uncertainties are modeled by worst-case model. Our objective is to maximize the worst-case secrecy rate with respect to both the worst-case channel uncertainties and the worst-case eavesdropper among multiple eavesdroppers, under the transmit power constraint and the worst-case energy harvesting constraint. The optimal solution to the problem can be found by two-dimensional (2-D) search. Since the 2-D search algorithm has high computational complexity, we propose to neglect the correlation of the channel uncertainties from the transmitter to the information-decoding receiver and reformulate the problem as a sequence of convex semidefinite programming (SDP) which is solved efficiently by SDP based one-dimensional line search method. It is shown through computer simulations that the proposed robust AN-aided secure transmission schemes have significant performance gain over the non-robust AN-aided secure transmission scheme and the robust secure transmission scheme without the aid of AN.

In OFDM-based spectrum sharing networks, due to inefficient coordination or imperfect spectrum sensing, the signals from femtocells or secondary users appear as interference in a subset of subcarriers of the primary systems. Together with the inter-carrier interference (ICI) introduced by high mobility, equalizing one subcarrier now depends not only on whether interference exists, but also the neighboring subcarrier data. In this letter, we propose a novel approach to iteratively learn the statistics of noise plus interference across different subcarriers, and refine the soft data estimates of each subcarrier based on the variational inference. Simulation results show that the proposed method avoids the error floor effect, which is exhibited by existing algorithms without considering interference mitigation, and performs close to the ideal case with perfect ICI cancelation and knowledge of noise plus interference powers for optimal maximum a posteriori probability (MAP) equalizer.

Considering simultaneous wireless information and power transfer (SWIPT), we investigate cooperative-jamming (CJ) aided robust secure transmission design in multiple-input-single-output channels, where a cooperative jammer introduces jamming interferences and assists a source to supply wireless power for both an energy receiver and a legitimate destination. The destination employs a power splitting (PS) scheme to split the received signals for both information decoding and energy harvesting (EH). Compared with conventional transmission without SWIPT, the transmission with SWIPT should satisfy additional worst-case EH constraints. Furthermore, the PS scheme introduces an additional multiplicative optimization variable, i.e., the PS factor. Our objective is to maximize worst-case secrecy rate under transmit power constraints and worst-case EH constraints. We propose to decouple the problem into three optimization problems and employ alternating optimization algorithm to obtain the locally optimal solution. For the optimization of transmit covariance matrices and PS factor, we propose to employ the S-procedure and its extension to reformulate it as a convex semidefinite programming. It is shown through the simulation results that our proposed CJ aided robust secure transmission scheme outperforms the robust direct transmission scheme without CJ and the CJ aided non-robust scheme.

This paper studies optimal precoder design for non-regenerative multiple-input multiple-output MIMO cognitive relay systems, where the secondary user SU and relay station RS share the same spectrum with the primary user PU. We aim to maximize the system capacity subject to the transmit power constraints at the SU transmitter SU-Tx and RS, and the interference power constraint at the PU. We jointly optimize precoders for the SU-Tx and RS with perfect and imperfect channel state information CSI between the SU-Tx/RS and PU, where our design approach is based on the alternate optimization method. With perfect CSI, we derive the optimal structures of the RS and SU-Tx precoding matrices and develop the gradient projection algorithm to find numerical solution of the RS precoder. Under imperfect CSI, we derive equivalent conditions for the interference power constraints and convert the robust SU-Tx precoder optimization into the form of semi-definite programming. For the robust RS precoder optimization, we relax the interference power constraint related with the RS precoder to be convex by using an upper bound and apply the gradient projection algorithm to deal with it. Simulation results demonstrate the effectiveness of the proposed schemes. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, equalization of multihop relaying orthogonal frequency division multiplexing (OFDM) signal is investigated under time-varying channel with unknown noise powers, channel orders and Doppler frequencies. An iterative algorithm is developed under variational expectation maximization (EM) framework. The proposed algorithm iteratively estimates the channel, learns the channel and noise statistical information, and recovers the unknown data, using only limited number of pilot subcarrier in one OFDM symbol. Simulation results show that, without any statistical information, the performance of the proposed algorithm is very close to that of the optimal channel estimation and data detection algorithm, which requires specific information on system structure, channel tap positions, channel lengths, Doppler shifts as well as noise powers.

For amplify-and-forward multi-antenna relay networks with energy harvesting (EH) constraint, we study the optimal relay beamforming problem which maximizes the achievable rate from source to information-decoding receiver subject to the transmit power constraint at relay and the EH constraint at EH receiver. Because of the EH constraint, the beamforming problem is not convex. We propose the optimal beamforming scheme by converting the beamforming problem into a convex semidefinite programming with the rank-one relaxation and Charnes-Cooper transformation. We also propose a suboptimal closed-form beamforming scheme. It is shown from simulations that when the maximum allowable relay transmit power to noise power ratio is high, the performance of proposed suboptimal scheme approaches that of the optimal scheme.

In cognitive relay networks, the interferences from secondary users (SUs) and relays to primary users are constrained to be lower than a threshold. The interference constraints are difficult to satisfy when the channel state information (CSI) is imperfect. In this paper, we propose a robust beamforming scheme for the multi-antenna non-regenerative cognitive relay network where the multi-antenna relay with imperfect CSIs helps the communication of single-antenna SU. Our objective is to design a robust beamforming scheme which maximizes the system capacity subject to transmit power constraint and interference constraints. The bounded channel uncertainties are modeled using the worst-case model. The robust beamforming problem, neglecting the correlation of channel uncertainties, is reformulated as a convex semidefinite programming (SDP) by rank-one relaxation. This convex SDP is related with the worst-case relay transmit power minimization problem, which is further reformulated as a convex SDP, whose rank-one solution is proved to exist. Thus, we propose the suboptimal solution to the robust beamforming problem which is found effectively by solving two convex SDPs. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

Simultaneous wireless information and power transfer (SWIPT) is able to prolong the lifetime of energy constrained wireless networks. In this paper, we consider the relay beamforming design problem for SWIPT scheme in a non-regenerative two-way multi-antenna relay network. Our objective is to maximize the sum rate of two-way relay network under the transmit power constraint at relay and the energy harvesting (EH) constraint at EH receiver. For the non-convex EH-constrained relay beamforming optimization problem, we propose an iterative algorithm to find the global optimal solution based on semidefinite programming and rank-one decomposition theorem. To reduce computational complexity of global optimal solution, we transform the EH-constrained optimization problem to a difference of convex programming and propose a constrained concave convex procedure based iterative algorithm to find a local optimum. To further reduce the complexity, we propose a suboptimal solution based on the generalized eigenvectors method. When the case of multi-antenna sources is considered, we propose the alternating optimization based iterative algorithms. It is shown from simulations that considering the EH constraint, our proposed schemes outperform conventional relay beamforming schemes in the literature.

The antenna subarray formation (ASF) is a promising technique for multiple-input multiple-output (MIMO) receiver. For MIMO cognitive radio systems, we propose a joint beamforming and ASF scheme in this letter which maximizes the cognitive achievable capacity subject to the peak transmit power constraint at the secondary transmitter, peak interference power constraint at the primary receiver, and the limited number of nonzero elements in the ASF matrix. To solve the joint optimization problem, we propose a relax-and-recover scheme. Simulation results have shown that the proposed scheme outperforms the conventional antenna selection scheme.

The antenna subset selection technique balances the performance and hardware cost in the multiple-input multiple-output (MIMO) systems, and the problems on the antenna selection in MIMO relay systems have not been fully solved. This paper considers antenna selection on amplify-and-forward (AF) and decode-and-forward (DF) MIMO relay systems to maximise capacity. Since the optimal antenna selection algorithm has high complexity, two fast algorithms are proposed. The selection criterion of the algorithm for AF relay is to maximise a lower bound of the capacity, but not the exact capacity. This criterion reduces algorithmic complexity. The algorithm for DF relay is an extension of an existing antenna subset selection algorithm for one-hop MIMO systems. The authors show the derivations of the algorithms in detail, and analyse their complexity in terms of numbers of complex multiplications. Simulation results show that the proposed algorithms for both cases achieve comparable performance to the optimal algorithm under various conditions, and have decreased complexity.

Under the signal-strength-based power control scheme, the effect of various microcell cluster locations on the uplink average signal-to-interference ratio (also called carrier-to-interference ratio, or CIR) is studied for the hierarchical code-division multiple-access cellular system with attenuators applied to each microcell base station. On the basis of the interference statistics analysis, the influence of power control errors on the uplink average bit error rate (BER) is also studied under the multipath fading and shadowing. Results show that both central macrocell and microcell uplink CIR are almost unaffected by the variations of microcell cluster locations, and that the uplink average BER is evidently affected by the imperfection of power control and the shadowing standard deviations.

In this letter, we present a comprehensive performance analysis of orthogonal space-time block codes (STBCs) with transmit antenna selection under uncorrelated Rayleigh fading channels. Two best transmit antennas that maximize the instantaneous received signal-to-noise (SNR) are selected. Using the well-known moment generating function-based analysis approach, we derive the exact average symbol error rate (SER) for M-ary signals. Furthermore, we provide tight upper bounds on the SER for any number of transmit antennas and receive antennas. The tightness is verified by simulation results. It is shown that the diversity order, with antenna selection, is maintained as that of the full complexity systems