The non-linearity of power amplifiers (PAs) is a major challenge in satellite communications and high-frequency bands. Traditionally, discrete fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) uses localized subcarrier mapping, which induces a high peak-to-average power ratio (PAPR) and obstructs the efficient operation of the PA. In this paper, we propose an interleaved mapping based DFT-s-OFDM with data length extension to reduce PAPR. To overcome the unrealistic constraints in interleaved mapping, where the occupied subcarriers must be a divisor of the total, we introduce a trans-formation method based on time-domain waveform invariance. Furthermore, we provide its equivalent frequency-domain matrix representation. The constructed waveform reduces PAPR and simultaneously mitigates inter-carrier interference (ICI) caused by non-linearity, effectively achieving resistance to PA non-linearity. Simulations show that our method achieves a gain of up to 1.6 dB in PAPR and 2.4 dB in block error rate (BLER) compared to localized mapping.

Design of advanced modulations needs to be revisited to cater for the diverse requirements of future 6th Generation (6G). While most existing modulation schemes operate coherently and need pronounced channel-tracking pilots to reach performance limits, noncoherent transmission offers a viable option by avoiding channel estimation. Noting that the noncoherent capacity-achieving input symbols are good sphere packing over Grassmannian field and each symbol is a subspace in Euclidean space, additional information can be embedded by further packing over the subspaces. In this paper, we propose a hybrid high-dimensional modulation scheme that bears both noncoherent and coherent components via multi-branch mapping. The packing problem is formulated into optimizing rotational angles of noncoherent codewords whose closed-form expressions are derived in this paper. A two-stage binary labeling as well as integration method with waveform are proposed to extend performance limits. Correspondingly, a channel-adjustable receiver is proposed to recover the noncoherent and coherent components sequentially. Simulations demonstrate that the proposed method yields Grassmannian most 2.4dB gain compared with pilot-based scheme under suitable coding pair of two branches.

Integrated sensing and communication (ISAC) is an important new research topic for 5th-generation communicationAdvanced (5G-A) and 6th-generation (6G) mobile communication systems, where waveform design should be studied. Orthogonal frequency division multiplexing (OFDM) is anticipated to be more favorable in the future 5G-A and 6G ISAC systems due to its good 5G New Radio (NR) backward compatibility. Different resource multiplexing methods, including time-division multiplexing (TDM), frequency-division multiplexing (FDM), and codedivision multiplexing (CDM), for sensing and communication can be assumed in the OFDM-based ISAC system. Due to the low peak-to-average power ratio (PAPR) and good ambiguity function properties, chirp sequence can be used for sensing in the OFDM-based ISAC system. This paper proposes a unified chirp sequence generation method for OFDM-based ISAC system. By parameter configuration, different resource multiplexing methods of sensing and communication, including TDM, FDM, and CDM, can be realized based on the proposed method. Evaluation results demonstrate that the proposed chirp sensing sequence achieves better PAPR performance for both TDM and FDM methods and lower sidelobe level of the ambiguity function for TDM compared to OFDM with Zadoff-Chu (ZC) sequence, which indicates that the proposed chirp sequence with TDM and FDM can be considered as a suitable sensing sequence for scenarios with coverage limitations.

Integrated sensing and communication (ISAC) is one of the most important research topics towards 5th generation (5G)-Advanced and 6th generation (6G) mobile communications. Diverse use cases could be supported from the analysis of measurement results obtained by sensing. Reference signal (RS) is necessary for the measurements, which should be designed to provide good sensing performance at low overhead. Positioning RS (PRS) defined for user equipment (UE) positioning in 5G new radio (NR) is a good starting point for sensing, whose performance on estimation accuracy and maximum unambiguous performance are validated to be good. However, the overhead of NR PRS is too high, limiting its usage for sensing. In this paper, inspired by the multiple pulse repetition frequency (PRF) method in radar systems, we propose a new sensing RS pattern composed by multiple comb RSs with co-prime comb sizes. On the one hand, the comb RS pattern has good backward compatibility with communication systems. On the other hand, the co-prime comb sizes can effectively eliminate the distance ambiguity caused by the comb RS pattern. The evaluation results show that proposed sensing RS pattern could achieve same maximum unambiguous distance with NR PRS but reduce the overhead from 7.14% to 2.21%.

Online adaptation to the dynamic channel conditions requires intelligent receivers with online learning ability as well as efficient training samples. In this letter, we propose a new type of reference signal termedlearning reference signal (LRS), which serves as the online training samples for the fast adaptation of the deep neural network (DNN)-aided intelligent receiver. Specifically, we propose a model-agnostic meta-learning (MAML)-based LRS design framework, where the LRS sequence is regarded as the meta parameter and is meta-learned during the offline training. The maximum loss reduction criteria for LRS design is proposed such that the online meta-update based on LRS can maximize the reduction of the symbol error rate (SER). Furthermore, a Matthew effect in gradient-based training of LRS, which causes imbalanced update on different LRS symbols, is identified and then tackled by a novel symbol bundling and multi-stage updating method to ensure convergence. From experiments, we observe that the learned LRS contains both constellation points and non-constellation points, and achieves more than 4dB SER gain compared to using arbitrary constellation points as training samples.

With the frequent outbreak of viral pandemics, the search for efficient antiviral drugs has become an urgent task. Antiviral peptides (AVPs) have been proven to prevent the infection of host cells by viruses. This study proposes a novel tool called Datt-AVP for AVP prediction based on the peptide sequences. A dual-channel deep learning model of Long Short-Term Memory network and convolutional neural network, along with a pre-trained protein language model as a feature extractor, was applied to capture features in the antiviral peptide sequences simultaneously. Furthermore, self-attention module was added to promote the performance of our prediction results. It showed good recognition ability for antiviral peptides with different lengths. Our tool achieved an accuracy rate of 96.1% on the benchmark dataset, which out-performed state-of-the-art tools.

Integrated sensing and communication (ISAC) is an important new research topic for sixth-generation (6 G), where the waveform design should be studied. The chirp-based waveform is widely used in radar systems due to its low peak-to-average power ratio (PAPR) and good autocorrelation properties and is a candidate waveform for ISAC systems. To realize the chirpbased waveform in ISAC systems without additional hardware cost, an orthogonal frequency division multiplexing (OFDM) compatible sensing waveform is proposed in this paper. Firstly, a frequency domain processing (FDP) module is proposed to realize chirp signal under the OFDM transmitter structure. Then, to satisfy different communication requirements, the sensing symbol pattern is designed on top of the FDP module to realize multiple orthogonal chirps with flexible patterns. Evaluation results demonstrate that the proposed chirp-based waveform based on the unified waveform structure can achieve 4 dB and 13 dB signal-to-noise ratio (SNR) gain over traditional OFDM waveform with Zadoff-Chu (ZC) sequence or random payloads under the same range and velocity estimation accuracy.

Sub-Terahertz (sub-THz) communication is a potential technology to enable 6 G wireless communication with extreme experience due to its rich spectrum resources. Working on sub-THz frequency, radio frequency (RF) impairments including phase noise (PN) and non-linear power amplifier (PA) become more severe and will heavily degrade communication performance. Considering the new characteristics in such high frequency, various waveform schemes have been proposed to achieve better performance under RF impairments, among which the enhanced waveforms based on discrete Fourier transform spreading orthogonal frequency diversion (DFT-s-OFDM) are highly regarded for the low peak to average power ratio (PAPR) characteristic. This paper focuses on 6 G candidate waveforms of orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM), DFT-s-OFDM and enhanced DFT-s-OFDM including unified non-orthogonal waveform (uNOW), DFT-s-OFDM with frequency-domain spectral shaping (DFT-sOFDM with FDSS), and DFT-s-OFDM with FDSS and spectral extension (DFT-s-OFDM with FDSS and SE). In this paper, 6G candidate waveforms are evaluated for different sub-carrier spacing (SCS) considering PN and non-linear PA in sub-THz. Specially, waveforms are compared and analyzed from various aspects including PN robustness without compensation, PN compensation performance based on phase tracking reference signal (PT-RS), and output power back-off (OBO). In addition, under non-linear PA, this work has in-depth discussions on the changing trend of each RF requirement as OBO increases for different 6 G candidate waveforms. The results indicate that uNOW outperforms other waveforms for BLER performance considering both PN compensation and OBO, making it the promising 6 G candidate waveform.

To provide sensing capability in communication systems, integrated sensing and communication (ISAC) is an important research topic towards 5GA and 6G. Many sensing services and results are related to the estimated angles of targets. Virtual aperture (VA) technique in conventional radar systems can improve sensing performances of angle by special designs of transmit and receive antennas, which is different from those in communication systems. To exploit VA in ISAC systems without additional hardware cost, a unified antenna structure and a new VA scheme under the structure are proposed. Firstly, the antennas with half-wavelength spacing in communication systems are divided into multiple transmit antenna groups and one receive antenna group for sensing. Then, time-domain multiplexing (TDM)-based orthogonal signals are considered for multiple transmit antenna groups to realize VA. Beamforming is exploited in each transmit antenna group for beamforming gain. The estimation algorithm is also proposed to obtain expected larger VA. Thanks to the unified antenna structure, the total antennas can be flexibly exploited for either beamforming gain during transmission period or larger virtual aperture during estimation period. Results show that proposed VA scheme under unified antenna structure achieves 5 dB signal-to-noise-ratio (SNR) gain at$0.1^{\circ}$angle root-mean-square-error (RMSE) and improves angle RMSE from$0.1^{\circ}$to$0.03^{\circ}$at 0 dB SNR than conventional VA.

To provide extreme coverage towards 5G Evolution and 6G, high altitude platform station (HAPS) is an important component in non-terrestrial network (NTN) due to its deployment flexibility. Multi-beam transmission should be used in HAPS for high throughput, where each beam covers one cell with multiple users. However, intra-beam and inter-beam interference will be severe with traditional multi-user multiplexing schemes, which may affect the HAPS performance. In this paper, an enhanced non-orthogonal multiple access (eNOMA) scheme is firstly proposed to overcome intra- and inter-beam interference. Then a unified multi-user multiplexing scheme is further proposed to dynamically adapt between spatial-division multiple access (SDMA), non-orthogonal multiple access (NOMA) and eNOMA for various interference scenarios. The evaluation results show that the proposed scheme achieves maximum 48% spectrum efficiency (SE) gain over existing schemes under three user configuration.

5G New Radio (NR) takes multi-carrier waveform orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM) with high spectral efficiency (SE) as the core waveform, and only supports single-carrier waveform discrete Fourier transform spreading OFDM (DFT-s-OFDM) with high power efficiency (PE) when uplink coverage is limited. 5G Evolution (5GE) and 6G have diversified scenarios, including high-frequency bands and integrated space-terrestrial systems, in which the non-linearity of power amplifiers (PA) restricts the system design. Therefore, high PE or low peak to average power ratio (PAPR) becomes one of the most significant key performance indicators (KPI) for 5GE and 6G waveform design, in addition to the high SE requirement. In this paper, we propose a unified non-orthogonal waveform (uNOW) based on DFT-s-OFDM enhancement with time-domain compression and expansion (TD-CE) as well as frequency-domain spectral shaping (FDSS) to improve PAPR and SE performance, simultaneously. Firstly, we describe the uNOW transmitter structure and two receiver structures, i.e., the linear minimum mean squared error (LMMSE) time-domain equalization (TDE) for time-domain compression and enhanced LMMSE frequency-domain equalization (FDE) for time-domain expansion. Then, the basic principle of uNOW is analyzed, i.e., how TD-CE and FDSS affect the time-domain pulse-shapes to reduce PAPR. Simulation results show that when only TD-CE or FDSS is considered, both PAPR gain and throughput gain are achieved compared with DFT-s-OFDM. When considering both TD-CE and FDSS, the PAPR and throughput gain achieved by uNOW can be significantly improved. Therefore, uNOW can be considered as a promising waveform candidate for 5GE and 6G.

Non-terrestrial network (NTN) is an important technique to provide extreme coverage towards 5G advanced and 6G. High-altitude-platform-station (HAPS), as one key factor in NTN, is attracting a lot of attentions. To achieve better resource utilization and performance, a unified design for integrated HAPS-Terrestrial networks is necessary, where the interference among these two systems may be important. Current methods usually consider fixed resource allocation among the two systems without considering the distribution of traffic load and hence have low resource utilization. In practice, the traffic load may change dynamically or semi-statically due to the wide coverage in HAPS, which should be considered in the design of integrated HAPS-terrestrial systems. In this paper, we proposed an interference coordination method based on the distribution of traffic load as well as the deployment of HAPS and terrestrial networks. The evaluation results show that the proposed method achieves higher throughput than existing fixed resource allocation method.

Non-terrestrial network (NTN) is an important technique to provide extreme coverage. Current fifth-generation (5G) new radio (NR) techniques mainly focus on the terrestrial networks. How to support NTN by 5G NR techniques is studied in 3rd generation partnership project (3GPP) since Release 15. After extensive studies, the first release of NR NTN is finished in Release 17. Uplink time synchronization is an important topic for both terrestrial networks and NTN, without which the interference among multiple users as well as the uplink and downlink misalignment will happen. Different from 5G NR, open-loop timing advance (TA) including common TA and UE-specific TA is firstly introduced in NR NTN. This paper introduces the latest progress in Release 17 and gives the solutions, procedures, and evaluation results for uplink synchronization in NR NTN.

Non-linearity of power amplifiers (PA) is the restriction of waveform design for 5G Evolution (5GE) and 6G in high frequency bands with large bandwidth. The low peak to average power ratio (PAPR) single carrier waveform discrete fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) can be used as a candidate waveform for 5GE and 6G. To satisfy the extremely high data rate requirement in 6G, how to keep the low PAPR of single carrier waveform and improve its spectral efficiency (SE) will be the challenge. In our previous study, a novel non-orthogonal waveform (NOW) design was proposed by combining DFT-s-OFDM with FTN to improve SE with low PAPR. However, due to the spectrum extension caused by FTN, large out of band emission (OOBE) and signal-to-noise ratio (SNR) loss were observed. In this paper, we propose an enhanced non-orthogonal waveform (eNOW) to improve the SE of DFT-s-OFDM by designing the frequency domain power allocation to efficiently suppress OOBE and reduce SNR loss. Simulation results demonstrate that the OOBE and SNR loss of eNOW can be significantly reduced than that of NOW by design the frequency domain power allocation. With eNOW scheme, OOBE, PAPR and throughput gain can be achieved, simultaneously.

Past research regarding on-body interaction typically requires custom sensors, limiting their scalability and generalizability. We propose EarBuddy, a real-time system that leverages the microphone in commercial wireless earbuds to detect tapping and sliding gestures near the face and ears. We develop a design space to generate 27 valid gestures and conducted a user study (N=16) to select the eight gestures that were optimal for both human preference and microphone detectability. We collected a dataset on those eight gestures (N=20) and trained deep learning models for gesture detection and classification. Our optimized classifier achieved an accuracy of 95.3%. Finally, we conducted a user study (N=12) to evaluate EarBuddy's usability. Our results show that EarBuddy can facilitate novel interaction and that users feel very positively about the system. EarBuddy provides a new eyes-free, socially acceptable input method that is compatible with commercial wireless earbuds and has the potential for scalability and generalizability

High frequency bands with large bandwidth (e.g., millimeter-wave and terahertz frequencies) is promising for 5G Evolution and 6G, but the system design is restricted by the non-linearity of power amplifier (PA). Multi-carrier waveform of orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM) may not be suitable for 5G Evolution and 6G due to its high peak to average power ratio (PAPR). Single carrier waveform represented by discrete fourier transform spreading OFDM (DFT-s-OFDM) with low PAPR could be a promising candidate waveform for 5G Evolution and 6G. Considering the extreme high data rate requirement of 5G Evolution and 6G, how to keep the low PAPR of DFT-s-OFDM and improve its spectral efficiency (SE) will be the challenge especially when high-order modulation is not applicable. In this paper, we propose a non-orthogonal waveform (NOW) to improve the SE of DFT-s-OFDM and derive the optimal time domain compression factor in terms of SNR based lossless compression. Furthermore, we investigate the impact of different compression factors to the system performance of throughput and PAPR. Simulation results demonstrate that the proposed scheme can achieve both throughput gain and PAPR gain over the traditional orthogonal waveforms DFT-s-OFDM and CP-OFDM, by flexibly adjusting the compression factor. When the compression factor is smaller than the optimal value, SNR loss to different extent may be observed.

Due to the availability of wide bandwidth, high frequency band is promising for 5G evolution and 6G. With the increase of antenna ports and bandwidth, existing subband-level precoding consumes high feedback overhead and restricts the precoding granularity. However, channel sparsity can be observed in transformed angular-delay domain in mmWave systems with massive antennas. In this letter, to utilize the sparsity, we propose a transform domain precoding (TDP) to design precoder and feedback. Realistic factors including hybrid beamforming, frequency domain windowing and power allocation are analyzed and evaluated by link-level simulations. Results show that 60%~89% overhead reduction and 2.38%~21% spectrum efficiency (SE) enhancement can be achieved by TDP.

Existing wireless mobile networks configure the uplink (UL) and downlink (DL) resources based on the orthogonal duplex principle, which has significantly evolved from the static frequency/time division duplex (FDD/TDD) to the semi-dynamic TDD in 4G and the fully-dynamic TDD in 5G. Fueled by the successful realizations of full duplex (FD) transmission, non-orthogonal duplex (NOD) emerges as an attractive candidate technique for future networks to improve the bidirectional throughput. In this paper, we investigate the performance limit of the NOD scheme, which imports the FD mode on the basis of fully-dynamic TDD. We first focus on a single-cell scenario by assuming no cooperation among cells, and strive to characterize the bidirectional capacity region by finding the capacity-optimal transmission mode scheduling policy. We obtain the optimal policies for the systems with and without modulation and coding scheme (MCS), respectively, where the policies are based on the instantaneous channel gains and the distribution of channels. We proceed to develop a scheduling policy that only relies on the instantaneous channel and queue information, and extend it to the multicell multiuser scenario with coordinated scheduling. Numerical and simulation results demonstrate the great potential of the NOD scheme in increasing bidirectional capacity and reducing the queuing delay.

Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach the performance limit with only conventional communication-theoretic tools. In this paper, we resort to deep multi-task learning for end-to-end optimization of NOMA, by regarding the overlapped transmissions as multiple distinctive but correlated learning tasks. First of all, we establish a unified multi-task deep neural network (DNN) framework for NOMA, namely DeepNOMA, which consists of a channel module, a multiple access signature mapping module, namely DeepMAS, and a multi-user detection module, namely DeepMUD. DeepMAS and DeepMUD are automatically trained in a data-driven fashion, and a multi-task balancing technique is then proposed to guarantee fairness among tasks as well as to avoid local optima. To further exploit the benefits of communication-domain expertise, we introduce constellation shape prior and inter-task interference cancellation structure into DeepMAS and DeepMUD, respectively. These sophisticated designs help to reduce the implementation complexity without sacrificing DNN's universal function approximation property, which makes DeepNOMA a universal transceiver optimization approach. Detailed experiments and link-level simulations show that higher transmission accuracy and lower computational complexity can be simultaneously achieved by DeepNOMA under various channel models, compared with state-of-the-art.

Tactile Internet of Things (IoT) requires ultraresponsive and ultrareliable connections for massive IoT devices. As a promising enabler of tactile IoT, grant-free nonorthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and nonorthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multiloss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in tactile IoT. The significant reliability gain of our scheme is validated by simulations.

Uplink non-orthogonal multiple access (NOMA) is a promising technique to meet the requirements of the fifth generation (5G) and beyond systems. Various NOMA schemes have been proposed in both academia and industry. However, most existing schemes assume equal average received power, which limits the performance. We propose three enhancements of uplink NOMA to achieve the requirements of massive connectivity and high reliability in 5G, where unequal average received power is exploited as part of the multiple access signature. First, the optimal sequences targeting to generalized Welch-bound equality (GWBE) are obtained for unequal average received power. Then user grouping with multi-level received powers is proposed for better successive interference cancellation (SIC) at the receiver. Finally, sequence grouping based on the cross-correlation properties of sequences is proposed to reduce inter- and intra-group interference. Simulation results show that by incorporating multi-level received powers and sequence grouping into existing NOMA schemes, for an NOMA system with 400% overloading and fixed signature allocation, 3 dB and 10 dB signal-to-noise ratio (SNR) gains at 0.1 block error rate (BLER) target can be achieved compared with existing NOMA schemes and orthogonal multiple access (OMA), respectively. Besides, 0.01 BLER target can be achieved while an error floor exists in existing NOMA schemes. Under random sequence selection, collision probability is reduced by multi-level powers. In addition, GWBE sequences achieve lower BLER than existing sequences and the gain is large especially for low BLER requirements. This shows that the proposed scheme can support larger connectivity and higher reliability.

Grant‐free transmission, which simplifies the signaling procedure via uplink instant transmission, has been recognized as a promising multiple access protocol to address the massive connectivity and low latency requirements for future machine type communications. The major drawback of grant‐free transmission is that the contaminations among uncoordinated transmissions can reduce the data throughput and deteriorate the outage performance. In this paper, we propose a rate‐adaptive multiple access (RAMA) scheme to tackle the collision problems caused by the grant‐free transmission. Different from the conventional grant‐free (conv‐GF) scheme which transmits a single signal layer, RAMA transmits the signals with a multilayered structure, where different layers exhibit unequal protection property. At the receiver, the intra‐ and interuser successive interference cancellation (SIC) receiving algorithm is employed to detect multiple data streams. In RAMA, the users can achieve rate adaptation without the prior knowledge of the channel conditions, since the layers with high protection property can be successfully recovered when the interference is severe, while other layers can take advantage of the channel when the interference is less significant. Besides, RAMA also facilitates the SIC receiving since the multiple layers in the transmission signals can provide more opportunities for interference cancellation. To evaluate the system performance, we analyze the exact expressions of the throughout and the outage probability of both conv‐GF and RAMA. Finally, theoretical analysis and simulation results validate that the proposed RAMA scheme can simultaneously achieve higher average throughput and lower outage performance than conv‐GF. Meanwhile, RAMA shows its robustness with large user activation probability, where the collisions among users are severe.

Whole DNA sequence is naturally related to big data streams, it is a challenge task to make a classification and visualization for whole DNA sequences. In this paper, a new mapping method for whole DNA sequence is proposed, and a special mapping scheme is used to transfer a whole DNA sequence as multiple 2D statistical probability maps. A sample case is selected from a night monkey species from south America (Aotus Nancymaae), interesting patterns are observed from relevant maps.

Recent work has demonstrated the gain of full-duplex (FD) network over half-duplex (HD) network in bidirectional sum throughput under the assumption of symmetric uplink-downlink traffic demands and perfect self-interference suppression (SIS). In this paper, we study the performance gain of FD network over HD network under asymmetric bidirectional traffic demands and non-ideal SIS. To this end, we investigate the traditional static time division duplex (TDD) transmission mode and the advanced dynamic TDD transmission mode to obtain the performance of HD network, and investigate the pure FD transmission mode and a flexible HD-FD hybrid transmission mode, namely, XD mode, to obtain the performance of FD network. We use the number of users supported by a network as performance metric, which is defined as the minimum of the weighted numbers of users supported in uplink and downlink given random traffic demands of users. To maximize the number of users, we optimize the bidirectional transmit power for pure FD mode, bidirectional time slot configuration for dynamic TDD mode, and both for XD mode. Numerical results show an evident gain of pure FD mode and XD mode over static TDD mode for different levels of traffic asymmetry, but the gain over dynamic TDD mode is marginal, which cannot justify the application of FD technology in cellular systems without advanced interference control mechanisms.

Massive multi-input multi-output (MIMO) can support high spectral efficiency with simple linear transceivers, and is expected to provide high energy efficiency (EE). In this paper, we analyze the scaling laws of EE with respect to the number of antennas M at each base station of downlink multi-cell massive MIMO systems under spatially correlated channel, where both transmit and circuit power consumptions, channel estimation errors, and pilot contamination (PC) are taken into account. We obtain the maximal EE for the systems with maximum-ratio transmission and zero-forcing beamforming for given numbers of antennas and users by optimizing the transmit power subject to the minimal data rate requirement and maximal transmit power constraint. The closed-form expressions of approximated EE-maximal transmit power and maximal EE, and their scaling laws with M are derived. Our analysis shows that the maximal EE scales with M in O(log2M/M) for the system without PC, and in O(1/M) for the system with PC. The EE-maximal transmit V power scales up with M in O(√(M/ln M)) until reaching the maximal transmit power for the system without PC, and in O(1) for the system with PC. The analytical results are validated by simulations under a more realistic 3D channel model.

This paper addresses an energy-saving problem for the downlink of a cloud-assisted heterogeneous network (HetNet) using a time-division duplex (TDD) model, which aims to minimize the base stations (BSs) sum power consumption while meeting the rate requirement of each user equipment (UE). The basic idea of this work is to make use of the scalability of system configurations such that green resource management can be employed by flexibly switching off some unnecessary hardware components, especially for off-peak traffic scenarios. This motivates us to utilize a flexible BS power consumption formulation to jointly model its signal processing and circuit power, transmit power, and backhaul transmission power. Instead of using the integer variables [1,0] to control the “on/off” two status of a BS in most previous work, we employ the group sparsity of a transmit power vector to denote the activity of each frequency carrier (FC) such that the signal processing and circuit power can be scaled with the effective bandwidth, thereby leading to multiple sleep modes for a BS in multi-FC systems. Based on this BS power model and the group sparsity concept, a simplified resource allocation scheme for joint BS-UE association, FC assignment, downlink power allocation, and BS sleep modes determination is presented, which is based on the average channel statistics computed over the coherence time of the large scale fading (LSF). This semidynamic green resource management mechanism can be formulated as a NP-hard optimization problem. In order to make it tractable, the successive convex approximation (SCA)-based algorithm is applied to efficiently find a stationary solution using a cloud-based centralized optimization. Simulation results also verify the effectiveness of the proposed mechanism under the developed BS power consumption model.

With nodes in Delay Tolerant Network(DTN) distributing sparsely and moving rapidly, they usually suffer from intermittent connections and communications, thus bringing about limited message forwarding opportunities. All these could lead to inefficient forwarding, low delivery, long latency and limited transmission capacity in performance. In this paper, efficient encoding and decision methods are presented and integrated into the DTN routing strategy. The custody-encoding-forwarding mode is designed by merging the random linear network coding into the DTN routing, together with the replica re-allocation and memory management, and built on that encoding scheme, the intra/inter flow adaptive collaborative network coding is elaborated to implement the fresh custody-decision-encoding-forwarding mode. A decision-making strategy based on Bayesian Network(BN) measures the ``degree'' in a specific generation in networks by taking comprehensive consideration of current and historical network conditions to enhance the network robustness and self-adaptivity. By evaluating the delivery, delay and overhead performance on ONE and MATLAB platforms, the effectiveness of proposed strategies is validated in the end.

Coordinated multi-point (CoMP) transmission through joint processing (JP) and coordinated beamforming (CB) is promising to provide high spectral efficiency by avoiding in-tercell interference in downlink heterogeneous cellular networks. Pseudo-inverse based zero-forcing beamforming (P-ZFBF) is a popular low-complexity multi-user precoder that can achieve maximal sum rate under sum power constraint. The P-ZFBF under pure CoMP-JP transmission mode is able to achieve high multiplexing gain and array gain, which however leads to very inefficient usage of transmit power in heterogeneous networks. The P-ZFBF under pure CoMP-CB mode can fully use transmit power but achieves low multiplexing and array gains. This paper investigates hybrid cooperative transmission strategies to exploit the advantages of CoMP-JP and CoMP-CB. Both the optimal and closed-form suboptimal hybrid strategies are proposed. Simulation results demonstrate their evident performance gain over the pure CoMP-JP and CoMP-CB transmission modes.