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Register a new userPower Allocation for Coexisting Multicarrier Radar and Communication Systems in Cluttered Environments
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Added by
Fangzhou Wang
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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In this paper, power allocation is examined for the coexistence of a radar and a communication system that employ multicarrier waveforms. We propose two designs for the considered spectrum sharing problem by maximizing the output signal-to-interference-plus-noise ratio (SINR) at the radar receiver while maintaining certain communication throughput and power constraints. The first is a joint design where the subchannel powers of both the radar and communication systems are jointly optimized. Since the resulting problem is highly nonconvex, we introduce a reformulation by combining the power variables of both systems into a single stacked variable, which allows us to bypass a conventional computationally intensive alternating optimization procedure. The resulting problem is then solved via a quadratic transform method along with a sequential convex programming (SCP) technique. The second is a unilateral design which optimizes the radar transmission power with fixed communication power. The unilateral design is suitable for cases where the communication system pre-exists while the radar occasionally joins the channel as a secondary user. The problem is solved by a Taylor expansion based iterative SCP procedure. Numerical results are presented to demonstrate the effectiveness of the proposed joint and unilateral designs in comparison with a subcarrier allocation based method.
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In this paper, we study the problem of power and channel allocation for multicarrier non-orthogonal multiple access (NOMA) full duplex (FD) systems. In such a system there are multiple interfering users transmitting over the same channel and the allocation task is a non-convex and extremely challenging problem. The objective of our work is to propose a solution that is close to the theoretic optimum but is of limited complexity. Following a block coordinate descent approach, we propose two algorithms based on the decomposition of the original allocation problem in lower-complexity sub-problems, which can be solved in the Lagrangian dual domain with a great reduction of the computational load. Numerical results show the effectiveness of approach we propose, which outperforms other schemes designed to address NOMA-FD allocation and attains performance similar to the optimal solution with much lower complexity.
In this paper, incremental decode-and-forward (IDF) and incremental selective decode-and-forward (ISDF) relaying are proposed to improve the spectral efficiency of power line communication. Contrary to the traditional decode-and-forward (DF) relaying, IDF and ISDF strategies utilize the relay only if the direct link ceases to attain a certain information rate, thereby improving the spectral efficiency. The path gain through the power line is assumed to be log-normally distributed with high distance-dependent attenuation and the additive noise is from a Bernoulli-Gaussian process. Closed-form expressions for the outage probability, and approximate closed-form expressions for the end-to-end average channel capacity and the average bit error rate for binary phase-shift keying are derived. Furthermore, a closed-form expression for the fraction of times the relay is in use is derived as a measure of the spectral efficiency. Comparative analysis of IDF and ISDF with traditional DF relaying is presented. It is shown that IDF is a specific case of ISDF and can obtain optimal spectral efficiency without compromising the outage performance. By employing power allocation to minimize the outage probability, it is realized that the power should be allocated in accordance with the inter-node distances and channel parameters.
In this paper, robustness of non-contiguous orthogonal frequency division multiplexing (NC-OFDM) transmissions is investigated and contrasted to OFDM transmissions for fending off signal exploitation attacks. In contrast to ODFM transmissions, NC-OFDM transmissions take place over a subset of active subcarriers to either avoid incumbent transmissions or for strategic considerations. A point-to-point communication system is considered in this paper in the presence of an adversary (exploiter) that aims to infer transmission parameters (e.g., the subset of active subcarriers and duration of the signal) using a deep neural network (DNN). This method has been proposed since the existing methods for exploitation, which are based on cyclostationary analysis, have been shown to have limited success in NC-OFDM systems. A good estimation of the transmission parameters allows the adversary to transmit spurious data and attack the legitimate receiver. Simulation results show that the DNN can infer the transmit parameters of OFDM signals with very good accuracy. However, NC-OFDM with fully random selection of active subcarriers makes it difficult for the adversary to exploit the waveform and thus for the receiver to be affected by the spurious data. Moreover, the more structured the set of active subcarriers selected by the transmitter is, the easier it is for the adversary to infer the transmission parameters and attack the receiver using a DNN.
Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the smooth approximation and alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority.
Narrowband and broadband indoor radar images significantly deteriorate in the presence of target dependent and independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating wall clutter in indoor radar images. The algorithm relies on the availability of clean images and corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband RF imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Further, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal to noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of -10dB and label mismatch error of 50%.
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