This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.

Wireless technology improvements have been continually increasing, resulting in greater needs for system design and implementation to accommodate all newly emerging standards. As a result, developing a system that ensures compatibility with numerous wireless systems has sparked interest. As a result of their flexibility and scalability over alternative wireless design options, software-defined radios (SDRs) are highly motivated for wireless device modelling. This research paper delves into the difficulties of designing a reconfigurable multi modulation baseband modulator for SDR systems that can handle a variety of wireless protocols. This research paper has proposed an area-efficient Reconfigurable Baseband Modulator (RBM) model to accomplish multi modulation scheme and resolve the adaptability and flexibility issues with the wide range of wireless standards. This also presents the feasibility of using a multi modulation baseband modulator to maximize adaptability with the least possible computational complexity overhead in the SDR system for next-generation wireless communication systems and provides parameterization. Finally, the re-configurability is evaluated concerning the appropriate symbols generations and analyzed its performance metrics through hardware synthesize results.

This work investigates the effect of channel estimation error on the average secrecy outage capacity of dual selection in the presence of multiple eavesdroppers. The dual selection selects a transmit antenna of Alice and Bob (i.e., user terminal) which provide the best received signal to noise ratio (SNR) using channel state information from every user terminals. Using Gaussian approximation, this paper obtains the tight analytical expression of the dual selection for the average secrecy outage capacity over channel estimation error and multiple eavesdroppers. Using asymptotic analysis, this work quantifies the high SNR power offset and the high SNR slope for the average secrecy outage capacity at high SNR.

This paper investigates the wireless information surveillance in a suspicious millimeter wave (mmWave) wireless communication system via the spoofing relay based proactive eavesdropping approach. Specifically, the legitimate monitor in the system acts as a relay to simultaneously eavesdrop and send spoofing signals to vary the source transmission rate. To maximize the effective eavesdropping rate, an optimization problem for both hybrid precoding design and power distribution is formulated. Since the problem is fractional and non-convex, we resort to the Dinkelbach method to equivalently reduce the original problem into a series of non-fractional problems, which is still coupling. Afterwards, based on the BCD-type method, the non-fractional problem is reduced to three subproblems with two introduced parameters. Then the GS-PDD-based algorithm is proposed to obtain the optimal solution by alternately optimizing the three subproblems and simultaneously updating the introduced parameters. Numerical results verify the effectiveness and superiority of our proposed scheme.

In this letter, we consider the harvested-energy fairness problem in cognitive multicast systems with simultaneous wireless information and power transfer. In the cognitive multicast system, a cognitive transmitter with multi-antenna sends the same information to cognitive users in the presence of licensed users, and cognitive users can decode information and harvest energy with a power-splitting structure. The harvested-energy fairness problem is formulated and solved by using two proposed algorithms, which are based on semidefinite relaxation with majorization-minimization method, and sequential parametric convex approximation with feasible point pursuit technique, respectively. Finally, the performances of the proposed solutions and baseline schemes are verified by simulation results.

This letter studies physical-layer security in a cognitive radio (CR) network, where a primary user (PU) is eavesdropped by multiple collusive eavesdroppers. Under the PU secrecy outage constraint to protect the PU, the secondary users (SUs) are assumed to be allowed to transmit. The problem of joint SU scheduling and power control to maximize the SU ergodic transmission rate is investigated for both the scenarios of perfect and imperfect channel state information (CSI). It is shown that, although collusive eavesdroppers degrade the PU performance compared to non-collusive eavesdroppers, the SU performance is actually improved when the number of eavesdroppers is large. It is also shown that our proposed scheme with imperfect CSI can guarantee that the PU performance is unaffected by imperfect CSI.

The combination of large-scale antenna arrays and simultaneous wireless information and power transfer (SWIPT), which can provide enormous increase of throughput and energy efficiency is a promising key in next generation wireless system (5G). This paper investigates efficient transceiver design to minimize transmit power, subject to users' required data rates and energy harvesting, in large-scale SWIPT system where the base station utilizes a very large number of antennas for transmitting both data and energy to multiple users equipped with time-switching (TS) or power-splitting (PS) receive structures. We first propose the well-known semidefinite relaxation (SDR) and Gaussian randomization techniques to solve the minimum transmit power problems. However, for these large-scale SWIPT problems, the proposed scheme, which is based on conventional SDR method, is not suitable due to its excessive computation costs, and a consensus alternating direction method of multipliers (ADMM) cannot be directly applied to the case that TS or PS ratios are involved in the optimization problem. Therefore, in the second solution, our first step is to optimize the variables of TS or PS ratios, and to achieve simplified problems. After then, we propose fast algorithms for solving these problems, where the outer loop of sequential parametric convex approximation (SPCA) is combined with the inner loop of ADMM. Numerical simulations show the fast convergence and superiority of the proposed solutions.

This paper studies the secrecy throughput performance of the three-node wireless-powered networks and proposes two secure transmission schemes, namely the half-duplex maximal ratio combining (HD&MRC) scheme and the full-duplex jamming scheme based on time switching simultaneous wireless information and power transfer (FDJ&TS-SWIPT). The closed-form expressions of the secrecy throughput are derived, and intuitive comparison of the two schemes is provided. It is illustrated that the HD&MRC scheme only applies to the low and medium signal-to-noise ratio (SNR) regime. On the contrary, the suitable SNR regime of the FDJ&TS-SWIPT is much wider. It is depicted that FDJ&TS-SWIPT combing with current passive self-interference cancellation (SIC) algorithm outperforms HD&MRC significantly, especially when a medium or high transmit SNR is provided. Numerical simulations are conducted for verifying the validity of the analysis.

This letter considers a legitimate proactive eavesdropping scenario, where a half-duplex legitimate monitor hires a third-party jammer for jamming the suspicious communication to improve the eavesdropping performance. The interaction between the third-party jammer and the monitor is modeled as a Stackelberg game, where the jammer moves first and sets the price for jamming the suspicious communication, and then the legitimate monitor moves subsequently and determines the requested transmit power of the jamming signals. We derive the optimal jamming price and the optimal jamming transmit power. It is shown that the proposed price-based proactive eavesdropping scheme is effective in improving the successful eavesdropping probability compared to the case without jamming. It is also shown that the proposed scheme outperforms the existing full-duplex scheme when the residual self-interference cannot be neglected.

This paper studies a simultaneous wireless information and power transfer (SWIPT) system in which the transmitter not only sends data and energy to many types of wireless users, such as multiple information decoding users, multiple hybrid power-splitting users (i.e., users with a power-splitting structure to receive both information and energy), and multiple energy harvesting users, but also prevents information from being intercepted by a passive eavesdropper. The transmitter is equipped with multiple antennas, whereas all users and the eavesdropper are assumed to be equipped with a single antenna. Since the transmitter does not have any channel state information (CSI) about the eavesdropper, artificial noise (AN) power is maximized to mask information as well as to interfere with the eavesdropper as much as possible. The non-convex optimization problem is formulated to minimize the transmit power satisfying all signal-to-interference-plus-noise (SINR) and harvested energy requirements for all users so that the remaining power for generating AN is maximized. With perfect CSI, a semidefinite relaxation (SDR) technique is applied, and the optimal solution is proven to be tight. With imperfect CSI, SDR and a Gaussian randomization algorithm are proposed to find the suboptimal solution. Finally, numerical performance with respect to the maximum SINR at the eavesdropper is determined by a Monte-Carlo simulation to compare the proposed AN scenario with a no-AN scenario, as well as to compare perfect CSI with imperfect CSI.

This letter investigates the performance of a legitimate surveillance system, where a wireless powered legitimate monitor aims to eavesdrop a suspicious communication link. Power splitting technique is adopted at the monitor for simultaneous information eavesdropping and energy harvesting. In order to maximize the successful eavesdropping probability, the power splitting ratio is optimized under the minimum harvested energy constraint. Assuming that perfect channel state information (CSI) or only the channel distribution information (CDI) is available, the closed-form maximum successful eavesdropping probability is obtained in Rayleigh fading channels. It is shown that the minimum harvested energy constraint has no impact on the eavesdropping performance if the minimum harvested energy constraint is loose. It is also shown that the eavesdropping performance loss due to partial knowledge of CSI is negligible when the eavesdropping link channel condition is much better than that of the suspicious communication link channel.

In this letter, we consider a cognitive radio network where multiple secondary users (SUs) share the spectrum bands with multiple primary users (PUs) who are facing security threats from multiple eavesdroppers. By adopting the PU secrecy outage constraint to protect the PUs, we optimize the joint user and power allocation for the SUs to maximize the SU ergodic transmission rate. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that the proposed algorithm outperforms the existing scheme, especially for a large number of PUs and a small number of SUs. It is also shown that the number of eavesdroppers has negligible impact on the performance improvement of the proposed algorithm compared to the existing scheme. In addition, it is shown that increasing the number of eavesdroppers has insignificant impact on the SU performance if the number of eavesdroppers is already large.

The sub-blocking algorithm has been known as a core component in implementing a turbo decoder using a Graphic Processing Unit (GPU) to use as many cores in the GPU as possible for parallel processing. However, even though the sub-blocking algorithm allows a large number of threads in a given GPU to be adopted for processing a large number of sub-blocks in parallel, each thread must access the global memory with strided addresses, which results in uncoalesced memory access. Because uncoalesced memory access causes a lot of unnecessary memory transactions, the memory bandwidth efficiency drops significantly, possibly as low as 1/8 in the case of an Long Term Evolution (LTE) turbo decoder, depending upon the compute capability of a GPU. In this paper, we present a novel method for converting uncoalesced memory access into coalesced access in a way that completely recovers the memory bandwidth efficiency to 100% without additional overhead. Our experimental tests, performed with NVIDIA's Geforce GTX 780 Ti GPU, show that the proposed method can enhance the throughput by nearly 30% compared with a conventional turbo decoder that suffers from uncoalesced memory access. Throughput provided by the proposed method has been observed to be 51.4Mbps when the number of iterations and that of sub-blocks are set to 6 and 32, respectively, in our experimental tests, which far exceeds the performance of previous works implemented the Max-Log-MAP algorithm.

In heterogeneous networks, network selection is an important task for reconfigurable mobile devices (MDs). In the reconfigurable MD architecture that has been standardized by the European Telecommunications Standards Institute (ETSI), the network selection functionality is handled by a software component called Mobility Policy Manager (MPM). In this paper, we present an implementation of the MPM whereby a reconfigurable MD conforming to the ETSI standard can select the most appropriate radio access network (RAN) to use. We implemented a reconfigurable MD test-bed compliant with the ETSI standard, and show that the network selection driven by the MPM enhances the throughput of the receiving MD by about 26% compared to the arbitrary network selection provided by a conventional reconfigurable MD without the functionality of MPM, verifying the functionality of the MPM.

In this paper, we present a modified image rejection method that uses imbalance compensation techniques for phase and gain in low-intermediate frequency (IF) software-defined radio (SDR) receivers. In low-IF receivers, the image frequency signal interferes with the desired signal owing to the phase and gain imbalances caused by analog devices. Thus, it is difficult to achieve the required image rejection ratio (IRR) of over 60dB without compensation. To solve this problem, we present modified blind compensation techniques based on digital signal processing using a feedback control loop with a practical computation process. The modified method can reduce the complexity when a hardware logic circuit is used, like an FPGA. The simulation and experimental results verify that the modified method achieves an IRR greater than 50-60dB for both the carrier and the modulated waves.

Viterbi decoding is commonly used for several protocols, but computational cost is quite high and thus it is necessary to implement it effectively. This paper describes GPU implementation of Viterbi decoder utilizing three-point Viterbi decoding algorithm (TVDA), in which the received bits are divided into multiple chunks and several chunks are decoded simultaneously. Coalesced access and Warp Shuffle, which is new instruction introduced are also utilized in order to improve decoder performance. In addition, iterative execution of parallel chunks decoding reduces the latency of proposed Viterbi decoder in order to utilize the decoder as a part of GPU-based SDR transceiver. As the result, the throughput of proposed Viterbi decoder is improved by 23.1%.

The parallel interference cancellation (PIC) multiple input multiple output (MIMO) detection algorithm has bit error ratio (BER) performance comparable to the maximum likelihood (ML) algorithm but with complexity close to the simple linear detection algorithm such as zero forcing (ZF), minimum mean squared error (MMSE), and successive interference cancellation (SIC), etc. However, the throughput of PIC MIMO detector on central processing unit (CPU) cannot meet the requirement of wireless protocols. In order to reach the throughput required by the standards, the graphics processing unit (GPU) is exploited in this paper as the modem processor to accelerate the processing procedure of PIC MIMO detector. The parallelism of PIC algorithm is analyzed and the two-stage PIC detection is carefully developed to efficiently match the multi-core architecture. Several optimization methods are employed to enhance the throughput, such as the memory optimization and asynchronous data transfer. The experiment shows that our MIMO detector has excellent BER performance and the peak throughput is 337.84 Mega bits per second (Mbps), about 7x to 16x faster than that of CPU implementation with SSE2 optimization methods. The implemented MIMO detector has better computing throughput than recent GPU-based implementations.

In this paper, we propose a fully parallel Turbo decoder for Software-Defined Radio (SDR) on the Graphics Processing Unit (GPU) platform. Soft Output Viterbi algorithm (SOVA) is chosen for its low complexity and high throughput. The parallelism of SOVA is fully analyzed and the whole codeword is divided into multiple sub-codewords, where the turbo-pass decoding procedures are performed in parallel by independent sub-decoders. In each sub-decoder, an efficient initialization method is exploited to assure the bit error ratio (BER) performance. The sub-decoders are mapped to numerous blocks on the GPU. Several optimization methods are employed to enhance the throughput, such as the memory optimization, codeword packing scheme, and asynchronous data transfer. The experiment shows that our decoder has BER performance close to Max-Log-MAP and the peak throughput is 127.84Mbps, which is about two orders of magnitude faster than that of central processing unit (CPU) implementation, which is comparable to application-specific integrated circuit (ASIC) solutions. The presented decoder can achieve higher throughput than that of the existing fastest GPU-based implementation.

In the arsenal of resources for improving computer memory system performance, predictors have gained an increasing role in the past few years. They can suppress the latencies when accessing cache or main memory. In our previous work we proposed predictors that not only close the opened DRAM row but also predict the next row to be opened, hence the name ‘Complete Predictor’. It requires less than 10kB of SRAM for a 2GB SDRAM system. In this paper we evaluate how much additional hardware is needed and whether the activations of the predictors will slow down the DRAM controller.

The effective bandwidth of the dynamic random-access memory (DRAM) for the alternate row-wise/column-wise matrix access (AR/CMA) mode, which is a basic characteristic in scientific and engineering applications, is very low. Therefore, we propose the window memory layout scheme (WMLS), which is a matrix layout scheme that does not require transposition, for AR/CMA applications. This scheme maps one row of a logical matrix into a rectangular memory window of the DRAM to balance the bandwidth of the row- and column-wise matrix access and to increase the DRAM IO bandwidth. The optimal window configuration is theoretically analyzed to minimize the total number of no-data-visit operations of the DRAM. Different WMLS implementationsare presented according to the memory structure of field-programmable gata array (FPGA), CPU, and GPU platforms. Experimental results show that the proposed WMLS can significantly improve DRAM bandwidth for AR/CMA applications. achieved speedup factors of 1.6× and 2.0× are achieved for the general-purpose CPU and GPU platforms, respectively. For the FPGA platform, the WMLS DRAM controller is custom. The maximum bandwidth for the AR/CMA mode reaches 5.94 GB/s, which is a 73.6% improvement compared with that of the traditional row-wise access mode. Finally, we apply WMLS scheme for Chirp Scaling SAR application, comparing with the traditional access approach, the maximum speedup factors of 4.73X, 1.33X and 1.56X can be achieved for FPGA, CPU and GPU platform, respectively.