Motivated by the application of point-to-point communication networks and biological storage, we investigate new achievability bounds for noisy permutation channels with strictly positive and full-rank square matrices. Our new bounds use$\epsilon$-packing with Kullback-Leibler divergence as distance and are tighter than existing bounds. The key element is our analysis of the union set of error events. Additionally, Gaussian approximations of achievability bounds are derived, and the numerical evaluation shows the precision of the approximation.

Thenoisy permutation channelis a useful abstraction introduced by Makur for point-to-point communication networks and biological storage. While the asymptotic capacity results exist for this model, the characterization of the second-order asymptotics is not available. Therefore, we analyze the converse bounds for the noisy permutation channel in thefinite blocklengthregime. To do this, we present a modified minimax meta-converse for noisy permutation channels by symbol relaxation and construct an auxiliary distribution for this bound usingdivergence covering. We then show the strong converse and refined asymptotic expansions. These two conclusions hold for noisy permutation channels with strictly positive matrices (entry-wise). In addition, we obtain computable bounds for the noisy permutation channel with the binary symmetric channel (BSC), including the original computable converse bound based on the modified minimax meta-converse, the asymptotic expansion, and the$\epsilon $-capacityresult. Finally, numerical results show that the normal approximation shows remarkable precision, and our new converse bound is stronger than existing bounds.

Multi-UAV cooperative reconnaissance for target search, localization, and tracking has attracted much attention in both civil and military applications, where strategies need to be designed for UAVs to finish the reconnaissance task cooperatively and in the time optimal manner. Different from the state-of-the-art of recent research where all the UAVs involved are equipped with homogeneous payloads, this work exploits payload diversity to enhance the time efficiency of the cooperation and proposes a fast multi-UAV cooperative reconnaissance (FMUCR) method. FMUCR groups UAVs in pairs. In each pair, one UAV is equipped with a passive positioning radar, referred to as p-UAV, while another is equipped with an active positioning radar, referred to as a-UAV. FMUCR exploits the large detection range and rough target location detection of passive radar to enable fast search and directional tracking of a target, while the precise target position calculation of active radar to enable accurate tracking of a target. Specifically, the task area is partitioned into subareas according to the number of UAV pairs. Each UAV pair conducts target search, localization, and tracking in one subarea, where the p-UAV leads searching and preliminary tracking of targets, while accurate tracking of targets are taken over by the a-UAV. Algorithms for off-line path planning and on-line path planning are designed, respectively, for target search and target tracking. The comparative simulation demonstrates that, FMUCR can greatly shorten the target discovery time with little loss in target tracking accuracy.

Abstract In this paper, a cooperative orthogonal frequency division multiplexing system with multiple untrusted amplify‐and‐forward relays is considered. Since there exists no direct link between source and user because of the shadowing effect, a positive system secrecy rate cannot be obtained. Addressing this issue, a cooperative user‐aided jamming technique to improve the secrecy performance is employed. With the aim of maximising the system secrecy rate, a joint resource allocation problem of power allocation, relay assignment and subcarrier pairing is formulated. By using the signal‐to‐noise ratio based approach and the Lagrange dual method, this nonconvex problem is solved efficiently. To reduce the calculation complexity, a suboptimal algorithm, which decouples the power allocation with the relay assignment and subcarrier pairing is further proposed. Numerical results are presented to demonstrate the advantage of the two proposed algorithms and the impact of relays' location and number of subcarriers on the secrecy performance. Moreover, it is shown that the secrecy performance of the proposed scheme is decreased with respect to the number of untrusted relays.

Abstract In this study, a non‐orthogonal multiple access based cooperative relay system is investigated, where the source serves multiple destinations via an amplify‐and‐forward relay. Direct links between the source and destinations are considered to form a general framework while the maximum ratio combination (MRC) is applied to obtain the largest decoding signal‐to‐interference‐plus‐noise ratio (SINR). Firstly, the outage performance is analysed with given successive interference cancellation (SIC) order and power allocation. An excellent approximated expression for the exact outage probabilities is derived in closed form. In addition, the asymptotic expression at high signal‐to‐noise ratio is provided to gain further insights. Then, in order to fully exploit the potential of the system, a joint optimisation of SIC order design and power allocation is formulated, with the objective of minimising the maximum outage probability of all destinations. By exploring the inherent property of the system, an optimal solution of SIC order and power allocation is developed. Finally, simulation results are presented to verify the correctness of the analytical results as well as indicate the efficiency of authors' SIC order design and power allocation algorithm.

In this paper, we study the resource allocation for a multi-carrier non-orthogonal multiple access (NOMA) system with coordinated direct and relay transmission, where some user equipments (UEs) are directly served by the base station (BS) and other UEs require the help from the relay nodes. In order to fully exploit the degrees of freedom in the systems design, we consider a general set of design parameters, including subcarrier pairing, subcarrier assignment, relay selection and power allocation, to formulate a joint optimization problem. To solve the combinational non-convex problem, we employ the Lagrange dual method and the Hungarian algorithm to obtain an asymptotically optimal solution. Simulation results demonstrate the asymptotic optimality and show that our algorithm significantly outperforms the conventional orthogonal multiple access (OMA) scheme and other existing resource allocation methods.

This study investigates a problem of resource allocation for an uplink multi‐carrier non‐orthogonal multiple access (NOMA) system. To provide a general resource allocation problem, the order of successive interference cancellation (SIC), subcarrier assignment and power allocation are all considered as the optimisation variables of interest, with the objective of maximising the weighted sum rate under the transmit power constraint of each user equipment (UE). Firstly, a novel optimal SIC order is proposed based on the inherent structure of the uplink NOMA system. Then, with the optimal SIC order, the Lagrange dual method is employed to solve the joint subcarrier assignment and power allocation problem in an asymptotically optimal manner. In addition, an optimal solution of power allocation with a fixed subcarrier assignment is presented. In light of the optimal power allocation, a tight upper bound of the uplink NOMA system is developed by relaxing the number of multiplexed UEs on each subcarrier. Simulation results show that the proposed algorithm outperforms conventional orthogonal multiple access schemes and other static NOMA schemes, and as a benchmark, the upper bound reveals that multiplexing at most two UEs on each subcarrier is sufficient for the performance improvement.

In this paper, we investigate energy-efficient resource management for downlink OFDMA systems with feedback channel capacity constraints. Compared with previous works, quantized channel state information (CSI) is studied by using rate-distortion theory since it can establish an information-theoretic lower bound on the capacity of the feedback channel. Based on the quantized CSI, an effective resource allocation algorithm is proposed to maximize the system energy efficiency employing the generalized fractional programming theory and the Lagrange dual decomposition method. Numerical results show that our proposed algorithm can nearly achieve the optimal solution and imply that the energy efficiency with a limited feedback rate could be close to that with perfect CSI.