Channel knowledge map (CKM) has been envisioned as a promising technology to achieve environment-aware communications for future sixth-generation (6G) wireless networks. The angle of departure (AoD) and the angle of arrival (AoA) are crucial parameters of CKM widely used for location-specific applications. Conventional stochastic methods of char-acterization fail to correlate the AoD and AoA with location-specific transmission environments. In this paper, we propose to utilize the CKM technology to characterize the location-specific AoD and AoA, by constructing the AoD and AoA map based on sparse measurement data. We further leverage the data-driven deep machine learning (DML) technique to obtain the AoD and AoA for locations without measurements. Simulation results show that the proposed method can construct both the AoD and the AoA maps with high fidelity to the true map. Moreover, Method II which predicts AoDs and AoAs sequentially achieves better performance than the independent prediction in Method I.

Radio environment map (REM) is a database repository widely adopted for applications such as spectrum sensing, interference management and network planning for wireless networks. However, conventional REM construction requires measurement-capable devices (MCDs) to upload location-based measurements, which exposes the privacy of users. In this paper, we propose a new method to construct the REM utilizing federated learning (FL) to preserve user privacy. In this method, multiple mobile MCDs collect the signal strength measurements in parallel and store the measurements locally. FL is then used to train a shared deep machine learning (DML) model for multiple MCDs collectively. In addition, we pre-process the location information and apply power adjustment to the measurements, either to increase the dynamic range of the input data to the FL or decrease the dynamic range of the measurements. The performance of the proposed FL-based method is evaluated in terms of the colormaps, heatmaps and cumulative distribution function (CDF) of estimation errors. The colormaps show that the power adjustment greatly improves the performance of the FL-based REM. The heatmaps show that smaller estimation errors can be achieved with more participating MCDs. The simulation results also show that the proposed FL-based method achieves better performance than the distance-based method and the nearest-neighbor-based (NN-based) method, in terms of estimation errors.

Timely identification of outliers occurring in key performance indicators (KPIs) of mobile cellular networks is crucial for prompt action to unexpected events. The KPIs of cellular networks typically exhibit seasonality and trend effects and these make the detection of outliers challenging. In this paper, an online unsupervised deep machine learning (DML) algorithm for outlier detection for time series with seasonality and trend is proposed. The proposed algorithm utilizes a neural network based on a temporal convolution network (TCN) and an autoencoder (AE) to reconstruct a time series that captures the normal behavior of the input data and then the reconstruction errors between the input and reconstructed time series at the output of the TCN-AE network are used for outlier detection. To train the TCN-AE network to learn the normal behavior of the input time series, we propose a two-step training process to overcome the presence of outliers in the training data. In addition, a novel loss function specially designed to address the seasonality and trend effects of the time series is proposed. Furthermore, a pre-processing technique is used to combat the adverse trend effect which might cause false alarms in outlier detection. The performance of the proposed TCN-AE-based outlier detection algorithm is evaluated using synthetic time series, Yahoo Webscope dataset and real time series of KPIs of mobile cellular networks. The results show that the proposed TCN-AE-based outlier detection algorithm achieves better detection accuracy in terms of F-score than other DML-based algorithms such as long short-term memory (LSTM)-AE-based algorithm and the convolutional neural network (CNN) based algorithm.

Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.

In this paper we investigate the physical layer security for downlink MU-MIMO systems where the transmitter and the eavesdropper are equipped with multiple antennas, while the legitimate users have single antenna. This is a more challenging topic because the eavesdropper appears to be more powerful than the legitimate users. We propose a transmission scheme able to enhance secure transmission for legitimate users, by intentionally applying different beamforming matrices to pilot signals and data signals. The beamforming matrix for data signals is constructed in a way that legitimate users can derive the channel matrix experienced by data signals based on pilot signals, whereas the eavesdropper fails to obtain the channel matrix experienced by data signals even with the aid of pilot signals. Therefore coherent detection at the eavesdropper is impossible and the signal-plus-interference-to-noise (SINR) of the eavesdropper is significantly degraded. In addition, the proposed beamforming design is formulated as an optimization problem to maximize the minimum SINR. We theoretically prove that with this formulation, the resultant optimal SINR is not affected by the proposed format of beamforming matrix for data signals. Numerical results show that, the proposed beamforming design outperforms the conventional beamforming design in terms of ergodic secrecy sum rate. Furthermore, with suitable beamforming designs, the ergodic secrecy sum rate achieved will not be noticeably affected by the number of antennas at the eavesdropper, although it is significantly degraded with conventional beamforming design.

In this paper, we propose an indoor localization system employing ordered sequence of access points (APs) based on received signal strength (RSS). Unlike existing indoor localization systems, our approach does not require any time-consuming and laborious site survey phase to characterize the radio signals in the environment. To be precise, we construct the fingerprint map by cutting the layouts of the interested area into regions with only the knowledge of positions of APs. This can be done offline within a second and has a potential for practical use. The localization is then achieved by matching the ordered AP-sequence to the ones in the fingerprint map. Different from traditional fingerprinting that employing all APs information, we use only selected APs to perform localization, due to the fact that, without site survey, the possibility in obtaining the correct AP sequence is lower if it involves more APs. Experimental results show that, the proposed system achieves localization accuracy < 5m with an accumulative density function (CDF) of 50% to 60% depending on the density of APs. Furthermore, we observe that, using all APs for localization might not achieve the best localization accuracy, e.g. in our case, 4 APs out of total 7 APs achieves the best performance. In practice, the number of APs used to perform localization should be a design parameter based on the placement of APs.

In this paper, a spectrum sensing algorithm to detect the presence of OFDM based system (DVB-T) with pilot signal insertion is presented. The proposed algorithm makes use of time domain auto-correlation of pilot signals, however is able to differentiate spurious modeled by single tone, unlike existing algorithms which detect spurious as signals rather than noise. Simulation results show that the proposed algorithm achieves performance equal to that of existing algorithms based on autocorrelation of pilot signals. Moreover simulation results show that the proposed algorithm achieves better performance in terms of false alarm rate (;0.8). This observation indicate that our proposed algorithm is more robust to spurious, without performance loss in probability of detection.

This paper addresses relay precoder designs for amplify-and-forward MIMO relay networks with imperfect channel state information (CSI). We first derive the capacity of a MIMO relay network with imperfect CSI. Two relay precoding schemes are then proposed - Scheme I maximizes the upper bound of the capacity obtained by applying Jensen's equality, whilst scheme II simply ignores the error of CSI of the channel from the relay to the destination. Numerical results show that, the capacity increases with the correlation coefficients between the CSI known to the estimating nodes and the CSI known to other nodes. Furthermore, the correlation coefficient of the channel from the source to the relay has more impact on the capacity than the channel from the relay to the destination. Numerical results also show that, the proposed relay precoder Scheme I achieves better capacity than Scheme II and the existing scheme, while Scheme II in some cases performs better than the existing scheme.

In this paper, we consider a two-tier network consisting of a macrocell and several closed-access femtocells sharing bandwidth with the macrocell. With multiple transmit antennas at both the macrocell and femtocell base stations, we propose a downlink power control and beamforming algorithm in two steps. In the first step, the beamformer of the macrocell base station is chosen to be the maximal ratio transmission beamformer, and we determine the power allocation at the macrocell and all femtocell base stations in a centralized manner by constraining that the signal power and leakage power associated with the femtocell beamformers satisfied certain predetermined thresholds. In the second step, the femtocell beamformers are obtained in a distributed manner by taking into account these thresholds. In the presence of imperfect channel state information, we further extend our proposed algorithm in a robust manner using robust optimization methodology.

This paper studies the relay precoding design for a MIMO relay network, where the relay only possesses imperfect channel state information (CSI). We consider a downlink non- regenerative MIMO relay network comprised of a base station (BS, or the source S), a fixed relay (the relay R) and a mobile station (MS, or the destination D). We model the discrepancy between the CSI of the R-D link available at the relay and the actual R-D link gain as a Gaussian random matrix. An upper bound of the mutual information of the considered MIMO relay network is first obtained assuming imperfect CSI knowledge of the R-D link at the relay. The relay precoder to maximize this upper bound is then derived. Numerical results show that by choosing a proper feedback rate, the proposed precoding design is able to achieve ergodic capacity nearly approaching that of the optimal precoder obtained under perfect CSI if a=f_d T <0.01. With fewer antennas used, the loss in capacity is less significant.

This paper addresses distributed precoding design for MIMO interference networks, where multiple MIMO links share the same bandwidth. In our proposed design, the precoding matrix for an individual link is given as the product of two matrices obtained in two sequential steps. In particular, the first matrix is derived by maximizing the desired signal power given the leakage power as a penalty and the second matrix is obtained by suppressing the interference among data streams belonging to one link (inter-stream interference). Comparing to the naive precoding design, where each transmitter selfishly employs waterfilling transmission scheme, the achievable rate achieved by our proposed two-step precoding design increases as the SNR increases. Moreover, the performance gain of our proposed precoding design over the naive design depends on the available spatial degrees-of-freedom.

A pilot aided joint symbol timing and frequency offset estimator for OFDM systems over power line channel (PLC) with colored noise is proposed in this paper. In the proposed estimator, the received time domain OFDM samples are first divided into groups where noise samples are approximately independent. Initial maximum likelihood (ML) based symbol timing and frequency offset estimates are obtained based on each group and final estimate is then derived by combining the initial estimates. We further simplify the proposed estimator to a practical one which does not require the knowledge of PLC fading profile, noise profile and signal-to-noise-ratio (SNR). The simulation results show that, under the presence of colored noise, the proposed practical symbol timing estimator achieves performance gain in terms of correct synchronization probability, around 2 dB at SNR=15 dB, and with improved mean square error (MSE) performance, compared to the widely adopted estimator in. With the knowledge of the statistics of colored noise, another algorithm is also proposed to further improve the estimation performance.

Powerline communication (PLC) is a preferred choice for smart home network. In this paper, a new system structure is proposed for PLC, which is based on distributed orthogonal frequency division multiple access (DOFDMA). Subcarrier sensing, i.e., sensing every OFDM subcarrier to see if it is occupied or not, is proposed to replace the conventional carrier sensing multiple access (CSMA) for multi-user contention of a channel. This structure, borrowed from cognitive radio, allows multiple users to opportunistically share the same channel on different subcarriers and therefore increases the channel capacity. Two subcarrier sensing methods are proposed: one is based on information theory and the other is based on successive energy comparison. Simulations are provided to verify the methods.

Although cell planning techniques have been widely applied, these formulations generally assumed static demand traffic, and either the average or worst case values are used in the design. Further, the impact of user mobility is usually ignored. We use examples to illustrate that better radio resource usage is possible by incorporating the time-varying traffic in the design, and that the user mobility can be similarly converted to multi-period optimization problem. We next formulate the one-dimensional cell planning problem, and demonstrate how it can be converted into a binary linear programming problem before solving for the optimal solution.

Two new precoding schemes based on the minimum mean-square error (MMSE) criterion for amplify-and-forward MIMO relay networks with quality-of-service (QoS) requirements are proposed. In the optimal joint source and relay precoding (OJSRP) scheme, primal decomposition technique is employed to decompose the non-convex optimization problem into a sub-problem and a master problem, which are convex and solvable. To relax the computation complexity of the OJSRP scheme, the suboptimal relay only precoding (SROP) scheme, which assumes equal power distribution over source antennas and has closed form solution, is also studied. Our numerical results show that both the minimum mean-square (MSE) performance and the capacity achieved by the two proposed schemes improve with the increase in signal-to-noise- ratio (SNR). Furthermore, OJSRP scheme has better MSE performance and capacity than SROP scheme. Finally, given a total transmission power, it is found that there exists an optimal power distribution over the source and the relay to achieve the best MSE performance and capacity. This indicates that transmission power should be distributed delicately over the source and the relay to optimize the resource utilization.

This paper studies the precoder design for a 3-node non-generative MIMO relay channel, taking the direct link transmission into consideration. We first look into the scenario where precoding is only performed at the relay. Our analytical approach enables a relay precoding design to achieve better ergodic capacity than the precoding design to achieve a lower bound of the capacity reported in earlier works. We next propose an iterative algorithm to perform joint precoding design at both the source and the relay. To reduce the computation complexity of the iterative algorithm, a two-step algorithm is then developed. Our numerical results show that under some circumstances, capacity gain can be achieved by performing precoding at both the source and the relay compared to the case where precoding is performed at the relay only. The efficiency of the proposed two-step algorithm is verified since it achieves roughly identical capacity as the iterative joint precoding scheme.

The rate adaptation of MQAM systems is achieved by selecting suitable MQAM constellation sizes through comparing instantaneous received signal-to-noise-ratio (SNR) against the SNR threshold intervals. In this paper, we present an approach to study the capacity with imperfect channel estimation. Under channel estimation errors, there will be a shift in the SNR threshold intervals compared to perfect channel estimation, if a pre-defined BER performance needs to be satisfied. We first address the problem of how to obtain the SNR threshold intervals under imperfect channel estimation, given the statistics of fading channel and estimation errors, for both single receiver and diversity reception systems. Our derivation shows that the SNR threshold intervals in both cases remain unchanged. The capacity achieved by rate adaptive MQAM systems under imperfect channel estimation is computed after obtaining the SNR threshold intervals.

In this paper, an iterative decoding scheme in a random order yet with more reliable interference cancellation, for MIMO-OFDM systems with multiple ARQ processes, is proposed to improve the decoding performance and the system throughput. Simulation results show that our proposed scheme achieves better system throughput than the scheme in [5] and the conventional scheme without interference cancellation, however, with increased processing delay. The throughput gain also increases with SNR. Although the throughput gain for a 2 x 2 MIMO-OFDM system presented in this extended abstract is not so significant, it is expected that with the increase in the number of antennas, the gain will be more evident since there are more rooms for the proposed scheme to explore. To evaluate the proposed scheme in-depth, the effect of the number of antennas and the convergence property are investigated. The results will be presented in the full paper. A practical system can control the number of decoding rounds to balance the tradeoff between the decoding performance and the processing delay.

The paper provides an unequal error protection (UEP) scheme for multiple-input and multiple-output (MIMO) wireless systems over fading channels. Turbo coding and space-time processing are employed to support transmission and reception for the data with different importance or priorities. It is shown that the proposed scheme allows for the increased system capacity and/or the improved quality with low complexity and high flexibility.

In this paper, a joint symbol timing and frequency-offset estimator based on the maximum likelihood (ML) criterion, is derived for OFDM systems over frequency selective fading channels. .We use the linear minimum mean square estimation technique (LMMSE) to estimate the nuisance parameters such as transmitted signals and fading gains, and arrive at an algorithm to jointly estimate symbol timing and frequency offset. Simulation results show that our proposed symbol timing estimator performs better than the estimator reported by van de Beek et al (1997), while our proposed frequency offset estimator achieves a lower MSE at higher SNR (/spl ges/ 15 dB). Simulation results also show that our proposed symbol timing and frequency offset estimator has better MSE performance than the cyclostationarity-based estimator studied in T. Pollet and M. Moeneclaey (1995) at higher SNR (for example 15 dB). However, in terms of probability of correct timing synchronization and MSE, our symbol timing estimator achieves roughly the same performance as the estimator by Lee et al (2002), although a different approach is used in our estimator. Our proposed estimator is next modified to two practical ones independent of SNR, furthermore, one of which needs only the knowledge of the maximum delay spread to perform estimation. We also extend our proposed estimator to applications where multiple OFDM symbols can be observed to perform synchronization.

An adaptive SNR-priority-based channel allocation scheme over multiple access fading channel is proposed. The performance of this scheme is a compromise between the conventional TDMA scheme which has short channel access waiting time but poor spectral efficiency, and the best SNR user transmitting scheme which has better bandwidth efficiency but longer channel access waiting time. The capacity achieved by this scheme is studied and compared with the conventional rate adaptive TDMA systems. The operation of this scheme requires channel estimation which is performed at the frame period for all N mobiles. For the SNR-priority -based system, the frame period is divided into K slots and only K mobiles that observe the first K best channel conditions are assigned to transmit. By assuming that only best-effort traffic is served, the channel capacity and the system throughput of the SNR-priority-based TDMA system achieved with rate adaptive MQAM modulation technique are studied. The average channel inter-access time for such a system is also studied through simulation.

This paper studies the capacity of a multiple chip rate (MCR) system under a practical environment. In our analysis, the use of non-orthogonal spreading codes, differences in carrier frequencies and power spectrum density of signals for different users, are taken into consideration. Capacities of the two MCR systems with different subsystem configurations are obtained. A comparison between the capacity of an MCR system and a multiple processing gain (MPG) system is also given. From the numerical results obtained, it can be concluded that for MCR systems, the full assignment configuration (a) can provide a higher system capacity than configuration (b), and configuration (b) with /spl Delta//spl omega/ = 0 achieves the same capacity as that of the MPG systems. Hence with good control of the spectrally overlaid structure, MCR systems can achieve higher capacity than MPG systems.

This paper investigates capacity of CDMA system employing adaptive transmission technique under multipath fading channel. Three power control schemes with rate adaptive capability are proposed to exploit the time variation of channel fading. In all the three schemes, adaptive transmission employing BPSK is realized through adapting the processing gain (PG) to the channel conditions. A minimum PG constraint is made to ensure multiple access capability. When PG is at this minimum value, the system can have an option to adjust the transmission rate through using higher constellation QAM. For scheme 1, system capacity rather than the lower bound reported in [Kim, S.W., 1999] is obtained. System capacities for scheme 2 and 3 are analyzed and computed using numerical calculations. Our numerical results show that the proposed power control schemes 2 and 3 can obtain higher system capacities than that of ideal constant received power at the base station, for a given constant average transmission power per user.

In a spectrally overlaid multi-band CDMA (SOM-CDMA) system, wide-band, medium-band and narrow-band CDMA subsystems with different spreading bandwidths are spectrally overlaid with each other. This system architecture has an advantage that different traffic types can exist together within a given band of spectrum, without having to allocate a separate spectrum band for each type of traffic. However, spectrally overlaid subsystems may also induce more interference on particular reference mobiles. Under this scenario, to study the effect of interference on the system capacity becomes necessary in order to ensure that the system can provide the required quality of service (QoS) for each type of service. Previous works on SOM-CDMA systems focused only on interference from mobiles in an isolated cell. In this paper, the capacity of SOM-CDMA cellular system in terms of aggregate data rate, by taking both inter-cell and intra-cell interferences into consideration, is investigated. Our results show that under the standard path loss model and ideal power control assumptions, the maximum aggregate data rate of SOM-CDMA systems will be degraded to approximately 50% of the isolated cell case. We also observed that the system capacity increases with the cell radius, while below a certain radius, spectral overlay becomes impractical. The bound of maximum mobiles admitted in SOM-CDMA with given SNR requirement and data rate of each frequency band can be a guide to system designers who design the call admission scheme.