This paper studies the secure computation offloading for multi-user multi-server mobile edge computing (MEC)-enabled internet of things (IoT). A novel jamming signal scheme is designed to interfere with the decoding process at the Eve, but not impair the uplink task offloading from users to APs. Considering offloading latency and secrecy constraints, this paper studies the joint optimization of communication and computation resource allocation, as well as partial offloading ratio to maximize the total secrecy offloading data (TSOD) during the whole offloading process. The considered problem is nonconvex, and we resort to block coordinate descent (BCD) method to decompose it into three subproblems. An efficient iterative algorithm is proposed to achieve a locally optimal solution to power allocation subproblem. Then the optimal computation resource allocation and offloading ratio are derived in closed forms. Simulation results demonstrate that the proposed algorithm converges fast and achieves higher TSOD than some heuristics.

This paper focuses on the channel estimation in cell-free hybrid massive multiple-input multiple-output (MIMO). Efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all access points (APs), which may enable the CPU to infer the private information of user locations. We therefore develop privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, a joint differentially private noisy matrix completion algorithm based on Frank-Wolfe iteration is presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we characterize the scaling laws of the estimation error in terms of data payload size. Simulation results demonstrate the tradeoff between privacy and channel estimation performance, and show that the estimation error can be mitigated by increasing the payload size while keeping the pilot size fixed.

We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with K users and M access points (APs), each with Naantennas and Nraradio frequency (RF) chains. When Ka, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.

This paper studies the energy-efficient power allocation problem for physical-layer security in multi-user (MU) distributed massive multiple-input multiple-output (MIMO) systems. A new metric called global average secrecy energy efficiency (GASEE) is proposed to measure the MU secrecy energy efficiency (SEE) with a single eavesdropper (Eve). We first derive closed-form expressions for the signal to interference-plus-noise ratios (SINRs) of legitimate users and the Eve with pilot contamination. Under a power consumption model that incorporates transmit power, backhaul power, remote antenna unit (RAU) circuit and signal processing power, and with transmit power constraints as well as SINR constraints for both users and the Eve, the GASEE maximization problem is formulated as a joint optimization of power allocation, RAU clustering, RAU selection and artificial noise (AN) selection. The formulated problem is a mixed integer nonlinear program (MINLP), which is solved by a double-loop procedure. In the outer loop, the denominator of objective is approximated as a linear function. In the inner loop, an efficient algorithm is proposed to find a near-optimal solution to the approximated problem by solving a sequence of sub-problems. Simulation results demonstrate that the proposed algorithm converges fast and achieves a higher GASEE than some heuristics.

Channel coding for the next generation communication system needs to support higher data throughput and transmission rate. Due to the inherent parallel feature, low density parity check (LDPC) codes comply with the target. In this paper, we propose a complete channel coding scheme based on structured LDPC codes. In our design, the correlation among different parity check matrices for different code rates is established, meanwhile the correlation between adjacent rows is established, then matrix design should be restricted by these two type defined correlations in order to make great progress in the support of multiple code rates and layered decoding algorithm. Our scheme includes the definition of the four matrices and five constraint features of these matrices, also the benefits of these features are analyzed. Simulation result shows that the performance of the proposed scheme is the same as or a little better than that of 11ad LDPC codes. The proposed LDPC scheme obviously decreases route complexity and increases decoder throughput. So, this scheme is very suitable for high speed decoder with more than 1Gbps throughput in various future communication systems.

5G sets stringent requirement on delay, which means large packets have to be segmented into shorter ones. However, the gain of turbo coding can be severely degraded due to short code size. In this paper, a novel coding and encoding scheme is proposed to obtain high coding gain under short size. The idea is to encode multiple short code blocks cooperatively to obtain a long code size. In the process of encoding, an XOR operation is conducted on certain parts of previously encoded blocks to introduce correlation. The mechanism of successive interference cancelation (SIC) receiver is applied in the decoding procedure and each code block utilizes the extrinsic information from the correlated parts from other code blocks. The scheme incurs no extra delays at reduced complexity. Simulation results show the scheme can yield equivalent or better performance than using traditional turbo code on long code block.

Video transmission on multiuser wireless channels faces manifold challenges in both source- and channel-coding. We propose a framework to analyze three facets of this idea: video distortion, transmit power, and delay. In the scenario considered here, both the transmitter and end-users are equipped with multi-antenna transceivers. The goal is to broadcast video to all users; however, the video is differentiated in quality to match the spatial and temporal variation of the channel. An emerging coding standard that is well-suited to this scenario is the Scalable Video Coding (SVC) extension of H.264. We employ the medium-grain scalability (MGS) feature of SVC, which allows us to generate quality-scalable layers from a single video sequence. For transport, the transmitter employs the block-diagonal zero-forcing (ZF) precoding technique, a well-known technique which yields good performance. With these assumptions, the idea here is to compute a precoder at fixed intervals. Crucially, the cross-layer framework allocates layers of video to the end-users jointly with precoder computation. The framework also ensures that delay and buffer constraints are met, which is necessary for real-time video. In terms of solution approach, the problem turns out to be a difficult mixed-integer nonlinear optimization problem. However, we show that it is tractable via a primal-dual method. We analyze performance in various configurations, gaining insight into the relationship between the main system parameters.

We propose a framework for multicasting layers of scalable video over a broadcast channel to multiple users simultaneously. The framework ensures that all users receive the content, but at differentiated levels of quality depending on channel conditions. A coding scheme well-suited to this purpose is Scalable Video Coding (SVC). In particular, we employ medium-grain scalability (MGS), which allows us to generate quality-scalable layers from a single video sequence. For transport, we assume that both the transmitter and endusers are equipped with multiple-input multiple-output (MIMO) transceivers. The transmitter employs the zero-forcing (ZF) preceding technique, which yields good performance with low complexity. With these assumptions, the scheme proposed here computes a block-diagonal ZF-precoder at fixed intervals, subject to a power constraint at the transmitter. Crucially, the algorithm assigns quality-scalable layers of video to the end-users jointly with precoder computation. The framework also ensures that delay and buffer constraints are met, which is necessary for realtime video. In terms of solution approach, the problem turns out to be a difficult mixed-integer nonlinear optimization problem.