Motivated by the teleoperation and local map sharing vehicular communication use cases, we investigate whether uplink throughput can be predicted by different machine learning approaches. First, we perform measurements of the vehicle to infrastructure (V2I) uplink throughput in Munich, Germany. Then, we use the collected measurements to evaluate whether linear regression (LR), deep neural network (DNN), and random forest (RF) can predict the uplink throughput. Our results show that, while very easy to train, LR is overly simple in describing the relationship of the input features and the predicted uplink throughput. On the other hand, DNN and RF can provide a very good prediction of uplink throughput (below 0.5 Mbps mean absolute error for a 40 Mbps uplink connection), while requiring longer training. Irrespective of the employed model, our results show that the best indicator of uplink throughput is signal to interference and noise ratio (SINR). When location information is added to SINR, the prediction error can be further reduced, albeit slightly. In the absence of SINR, location information is the second best in predicting uplink throughput. However, it can be employed only for locations that were available in the training dataset. On the other hand, SINR allows for generalization to locations different to those observed in the training dataset.

Wireless sensor networks (WSNs) act as the backbone of Internet of Things (IoT) technology. In WSN, field sensing and fusion are the most commonly seen problems, which involve collecting and processing of a huge volume of spatial samples in an unknown field to reconstruct the field or extract its features. One of the major concerns is how to reduce the communication overhead and data redundancy with prescribed fusion accuracy. In this paper, an integrated communication and computation framework based on meta-learning is proposed to enable adaptive field sensing and reconstruction. It consists of a stochastic-gradient-descent (SGD)-based base-learner used for the field model prediction aiming to minimize the average prediction error, and a reinforcement meta-learner aiming to optimize the sensing decision by simultaneously rewarding the error reduction with samples obtained so far and penalizing the corresponding communication cost. An adaptive sensing algorithm based on the above two-layer meta-learning framework is presented. It actively determines the next most informative sensing location, and thus considerably reduces the spatial samples and yields superior performance and robustness compared with conventional schemes. The convergence behavior of the proposed algorithm is also comprehensively analyzed and simulated. The results reveal that the proposed field sensing algorithm significantly improves the convergence rate.

In existing communication systems, the channel state information of each user equipment (UE) should be repeatedly estimated when it moves to a new position or when another UE takes its place. The underlying ambient information, including the specific layout of potential reflectors, which provides more detailed information about all UEs' channel structures, has not been fully explored and exploited. In this paper, we rethink the mm-wave channel estimation problem in a new and indirect way, i.e., instead of estimating the resultant composite channel response at each time, and for any specific location, we first conduct the ambient perception exploiting the fascinating radar capability of a mm-wave antenna array and then accomplish the location-based sparse channel reconstruction. In this way, the sparse channel for a quasi-static UE arriving at a specific location can be rapidly synthesized based on the perceived ambient information, thus greatly reducing the signaling overhead and online computational complexity. Based on the reconstructed mm-wave channel, single-beam mm-wave communication is designed and evaluated which shows an excellent performance. Such an approach, in fact, integrates the radar with communication, which may possibly open a new paradigm for future communication system design.

As a promising candidate for future 5G and beyond, millimeter-wave (mmWave) has received considerable attention. However, mmWave channel estimation remains to be a challenging problem. To tackle this issue, this paper proposes a mmWave joint radar and communication system for indoor scenarios. By cooperating with mmWave radar, the propagation environment is perceived. Then, high-accuracy channel estimates are obtained, which helps in designing more accurate precoders and combiners. Compared with the conventional channel estimation methods, the proposed scheme significantly reduces the spectrum overhead and computational complexity, especially when massive antennas and multiple users are involved. Our results may shed some lights on the system design for future mmWave communications.

With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and recurrent neural network (RNN), are proposed with the same parameter magnitude. The performance of these deep neural networks are evaluated through extensive simulation. Numerical results show that RNN has the best decoding performance, yet at the price of the highest computational overhead. Moreover, we find there exists a saturation length for each type of neural network, which is caused by their restricted learning abilities.

Reed-Muller (RM) sequences have been widely used in compressed sensing (CS) to construct deterministic measurement matrices and extract attributes of sparse signals. However, if the signal is not sparse enough, the existing reconstruction algorithms encounter serious performance degradation. In this paper, invoking the elegant nested structure of second-order RM sequences, a soft-decision reconstruction algorithm is proposed. With the soft- information passing through the nested structure, the proposed reconstruction algorithm outperforms the existing ones. Notably, the performance can be further improved based on shuffling operations. Numerical results verify the good performance of the proposed algorithm and show that it preserves quite low computational complexity.

Hybrid automatic repeat request transmission scheme with Chase combining (HARQ-CC) has been widely used to improve the throughput in wireless communication systems. However, for punctured polar codes, HARQ-CC cannot make up for the vacancies of the Log Likelihood Ratios (LLRs) of the punctured bits which are never transmitted at all, thus degrading the system efficiency. To address this issue, this paper proposes a new HARQ scheme based on the equivalent puncturing patterns (EPPs) of punctured polar codes. It successfully complements the LLRs of punctured bits, and therefore improves the system throughput. Moreover, we show that the proposed scheme can be implemented efficiently with the same complexity as HARQ-CC. Numerical results indicate that, for punctured polar codes with 21.875% puncturing proportion, the proposed HARQ achieves 7.5% improvement on the normalized system throughput compared with HARQ-CC.

Massive Multiple-Input Multiple-Output (MIMO), small cell, and full-duplex are promising techniques for future 5G communication systems, where interference has become the most challenging issue to be coped with. In this paper, we provide an interference coordination framework for a two-tier heterogeneous network (HetNet) that consists of a massive-MIMO enabled macro-cell base station (MBS) and a number of full-duplex small-cell base stations (SBSs). To suppress the interferences and maximize the throughput, the full-duplex mode of each SBS, i.e., in-band or out-of-band, which has different impact on the interference pattern, should be carefully selected. To address this problem, a distributed graph color algorithm (DGCA) based on price is proposed. Numerical results demonstrate that DGCA significantly improves the system throughput.

With the fast development of vehicular technologies, the number of mobile terminals as well as their moving speeds has increased conspicuously, making it meaningful to re-examine the impact of mobility on communication performance of the existing cellular systems. In this paper, the uplink sum rate of a multiple-input multiple-output orthogonal frequency-division multiple access (MIMO-OFDMA) cellular system with nodes moving randomly at different speeds is studied. In such a multi-user environment, mobility not only causes inter sub-carrier interferences (ICI) among different users, but also brings the so-called channel aging which together with the above interferences incur outdated and inaccurate channel estimation and thus entail additional pilot overhead. Here, we first derive the relationship between the users' mobility and the ICI power, characterize the channel aging, and derive the overall multi-user interference caused by the outdated and inaccurate channel state information as a function of mobility. Then, by capitalizing on the above results, the sum rate of the MIMO-OFDMA uplink is theoretically analyzed. Moreover, an adaptive transmission scheme in which the users adjust the pilot percentage according to mobility statistics is proposed to maximize the sum rate. Finally, numerical results are provided to validate our analyses.

In future Internet of Things (IoT) networks, the explosive growth of mobile devices compel us to reconsider the effectiveness of the current frequency-division multiple access (FDMA) schemes. Devices' differentiated mobility features and diversified scattering environments make it more complicated to characterize the multi-user interference. In this paper, we thoroughly analyze the impacts of devices' mobility on the inter-sub-carrier interference (ICI) in an IoT system based on the 3GPP narrow-band orthogonal frequency-division multiple access (NB-OFDMA) protocol, and obtain the relationship between the system sum-rate and devices' mobility. Our results may shed some lights on the system design under the mobile scenarios.

In high mobility wireless communication scenarios, the most challenging issue is to cope with the extremely fast fading channel. Compared with its static counterpart, channel estimation in high mobility scenarios consumes excessive energy and spectrum to achieve similar performance. To address this issue, we exploit delay correlation with sequential antenna-hopping (AH) to convert the rapid fading channels to a virtual slow-fading channel. As a result, robust communication can be achieved with much less channel estimation overhead even under high mobility. Finally, two important performance metrics, namely, channel estimation mean square error (MSE) and transmission symbol error rate (SER), are re-examined for this virtual slow-fading channel. Numerical results verify the good performance of the proposed scheme and elucidate its effectiveness in high mobility communication scenarios.

Provisioning reliable wireless services for railway passengers is becoming an increasingly critical problem to be addressed with the fast development of high speed trains (HST). In this paper, exploiting the linear mobility inherent to the HST communication scenario, we discover a new type of spatial-temporal correlation between the base station and moving antenna array on the roof top of the train. Capitalizing on the new spatial-temporal correlation structure and properties, an improved differential space-time modulation (DSTM) scheme is proposed. Analytical expressions are obtained for the pairwise error probability of the system. It is demonstrated that the proposed approach achieves superior error performance compared with the conventional DSTM scheme. In addition, an adaptive method, which dynamically adjusts the transmission block length is proposed to further enhance the system performance. Numerical results are provided to verify the performance of the proposed schemes.