Understanding the communication and information processing mechanisms in pinning control is highly influenced by the connectivity structure of the network, particularly in higher-order networks. However, existing studies predominantly focus on static network structures and utilize complete error information, often neglecting underlying inter-node dependencies, thereby limiting the effectiveness of low-energy control in higher-order dynamic systems. To address these limitations, this paper proposes a novel pinning control scheme for the synchronization of inertial memristive neural networks (IMNNs) based on dynamic Granger causality analysis (DGCA). First, an interpretable IMNN model is constructed to explicitly characterize higher-order interactions without decomposing the system into first-order subsystems. Then, unlike traditional pinned-node selection strategies relying on static interaction rules, a causality-aware selection algorithm is developed using DGCA to dynamically identify influential nodes via time-series analysis, enhancing control efficiency under dynamic network conditions. Furthermore, a local information-based pinning controller is designed by leveraging local causal relationships and control influence regions extracted from DGCA, ensuring the stability of the synchronization error system. Theoretical guarantees are provided by deriving sufficient conditions for controller design based on Lyapunov stability theory. Finally, three numerical examples are presented to demonstrate the effectiveness and practicality of the proposed scheme.

Airborne bathymetry LiDAR, such as Mapper5000, experienced varied water-bottom-return intensities. Under the high bottom-return signal condition, owing to the clear water body, high bottom reflectance, low depth, or high-gain setting of the detector, the peak power of the received signal from the bottom often exceeds the linear dynamic range of the 532-nm detector assembly. The resulting signal saturation distorted the recorded bottom-return waveforms and resulted in undesired depth bias exceeding 2.0 m in certain extreme cases. In this study, we developed a low-complexity peak position restoration method for this “saturation in-water range bias” correction based on an intersection point of double linear approximations of the leading and trailing edges of the saturated bottom waveform double line intersection (DLI). The validation of corrected results with consistent sonar depth measurements indicated that the DLI method demonstrated considerable robustness to the effect of pulse stretching under saturation conditions and reduced the mean absolute depth bias from 1.46 to 0.47 m. Compared with Gaussian and exponentially modified Gaussian (EMG) fitting algorithms, the DLI was faster and the most effective at reducing depth bias under strong saturation conditions. The success of DLI benefited from the small width of the transmitted laser pulse and the sharp slope of the leading edge of the saturated bottom waveform. However, the interference of volume backscattering signals at shallow depths could affect the performance of DLI, which should be carefully removed.

Device-to-device (D2D) communication has been considered a promising solution for vehicle-to-vehicle (V2V) communication. In V2V underlay massive MIMO cellular networks, V2V has the potential to increase spectral efficiency (SE) by reusing cellular spectrum. However, the interference between cellular and V2V transmissions may be serious if not designed properly. In this paper, we first derive the analytical lower bounds on multi-antenna V2V and cellular SEs under both perfect and imperfect channel state information for maximum-ratio combining and zero-forcing detection. Using these lower bounds, performance analysis is given to provide more insights and guide practice. Then, we formulate a large-scale fading-based power control problem to guarantee the SE constraints of V2V pairs while maximizing the sum SE of cellular users. Moreover, the geometric programming technique is applied to solve the optimal power control problem. Numerical results show that the proposed SE-constrained power control scheme can greatly increase the reliability and achievable SE of V2V communications while the achievable sum SE is slightly degraded, compared with the fixed maximum power scheme.

We consider the cooperative rate splitting scheme in device-to-device (D2D) underlay cellular network to reduce the interference causing performance degradation of communication. At the transmitter end, the transmitted information is split into two parts: common information and private information. The common information is exchanged between base station and D2D transmitter, and encoded together. At the receiver end, the common information is firstly decoded jointly and cancelled out. In this paper, we derive the optimal power allocation factors for the common information and the private information to maximize the sum rate, just using the large-scale fading information which is known at the base station which can reduce the computational complexity greatly. The simulation results show that the proposed scheme achieves higher sum rate than traditional schemes.

Sparse code multiple access (SCMA) is a competitive non-orthogonal multiple technique for 5G, which can accommodate more communication users by exploiting the multi-dimensional codewords. However, it is inflexible to meet the various demands of detection performance, or the various demands of computational complexity in real scenario. In this paper, we propose two simplified algorithms to deal with the problem, named GAMP-MPA and SIC-GAMP-MPA. These two algorithms are developed from the mainstream SCMA detector algorithm, message passing algorithm (MPA), and inspired by generalized approximate message passing (GAMP) as well as serial interference cancellation (SIC). The modified MPA benefits from the GAMP method since GAMP converts vector problems to scalar problems which provides higher computational efficiency and easier hardware implementation. Simulation results show that the GAMP- MPA achieves better performance than MPA with an acceptable extra complexity cost, and SIC-GAMP-MPA has significantly decreased complexity compared to MPA with similar performance. Particularly, the advantages of both GAMP-MPA and SIC-GAMP-MPA are more apparent in low signal-to-noise ratio (SNR) condition.

The evolving cellular network with centric-deployed Cloud Centers has greatly improved the mobile user experience. However, the advent of vehicular networks with a wide variety of new services and devices changes the existing cellular network landscape, and the cellular-based vehicular networks are confronted with serious challenge in terms of latency reduction and flexible service delivery. In this paper, we propose to deploy the specific server, called Mobile-Edge Computing (MEC) server, within the Radio Access Network, and allow them to connect with a set of base stations alongside roads, so as to provide flexible vehicle-related service and efficiently control the radio network. Then, the vision of MEC- assisted slicing network and a traffic scheduling policy are presented to promote network customization. As an instance of the MEC-based tailored network service, a time-predicted handover mechanism for vehicles is further developed by leveraging available road information and the enhanced capacity of the MEC server to satisfy the demand for high mobility and reliability.

We investigate the problem of throughput-guaranteed energy saving for base stations in small-cell networks with universal frequency reuse. The objective function is to minimize the time average of total power expenditure in each slot over infinite horizon, while the data arrival rate is constrained in the mean rate stable region. We take advantage of both queue state information and channel state information to find the optimal strategy of user scheduling and power allocation, which consumes the least energy and causes the least inter-cell interference. According to Lyapunov optimization theory, the problem is formulated into a non-convex combinatorial optimization problem of dynamic user scheduling and power allocation in each slot. In general, the problem is NP-hard, so we devise a two-step heuristic algorithm to solve it. Simulation results show that the performance of the proposed algorithm is close to exhaustive search algorithm, and better than Round Robin Algorithm and MaxWeight algorithm.

This paper proposes a MIMO system where the base station (BS) acquires quantized phase-only (PO) measurements of the complex baseband signal by our introduced stage-wised phase quantizer. PO-MIMO requires only one-bit ADCs for data sampling, so it successfully overcomes the ADC bottleneck that appears when the signal bandwidth is extremely wide. We construct a PO generalized approximate message passing (POG-AMP) algorithm for solving the linear mixing problem with quantized or unquantized phase measurements. POG-AMP has low computational complexity, exploits the signal prior statistical distribution, and handles the nonlinear distortions exerted on the measurements (e.g., losing magnitude and quantization). Then, POG-AMP is successfully applied to construct practical channel estimator and multiuser detector for PO-MIMO. Numerical results show that the POG-AMP estimator (POG-AMPE) and POG-AMP detector (POG-AMPD) are robust to the phase-quantization loss. POG-AMPE acquires high-quality channel side information at the receiver (CSIR), and POG-AMPD is robust to the CSIR errors when the BS antennas are massive enough. By introducing moderately more BS antennas, PO-MIMO with phase measurements even performs similarly to MIMO with full measurements containing both magnitude and phase. In order to maximize the transmit energy-efficiency, the lengths of the channel training sequences should be gradually increased with the increase of the channel coherence time. Antenna correlations at the BS degrade the convergence and bit-error rate performances of POG-AMPD, but can be handled by the analog spatial filtering technique.

This paper proposes a multiuser MIMO system with both full measurements and one-bit magnitude observations, which are acquired by several linear inphase-and-quadrature (IQ) structured radio frequency (RF) chains and massive one-bit envelope chains, respectively. The total circuit power and cost are not increased significantly, since the added one-bit envelope chains have low power and low cost. Channel side information on the one-bit envelope chains is acquired by sharing the IQ-structured RF chains and executing a channel calibration operation. Two multiuser detectors are constructed based on the semidefinite relaxation (SDR) and approximate message passing (AMP). The one-bit magnitudes are interpreted as inequality constraints in the SDR detector, and exploited in a Bayesian manner by the AMP detector. Simulation results show that the one-bit magnitude measurements bring about high MIMO multiplexing and diversity gains, and decrease the transmission power. With the increase of the channel coherence time, more one-bit envelope chains are prone to be equipped, and one-bit magnitude-aided MIMO becomes more and more spectral-and-energy-efficient than the conventional MIMO.

This correspondence researches the multiuser detection (MUD) in a new massive MIMO system, where the base station (BS) acquires the quantized amplitude-only (AO) measurements of the complex baseband signals through the envelop detection and low-resolution analog-to-digital convertor (ADC) sampling. Cumbersome frequency mixers are removed and low-resolution ADCs can easily reach the Giga-sample-per-second (GSPS) rate, so massive AO-MIMO has low power and low cost, and is especially suitable for millimeter-wave (mmWave) communication. A practical multiuser detector is constructed based on the Wirtinger-flow (WF) gradient method for phase retrieval (PR), and the message passing de-quantization (MPDQ) algorithm for the quantized compressed sensing (CS). It iterates between the missing phase estimation and signal reconstruction, and involves only matrix-vector multiplication as its complex operation. Simulation results validate the effectiveness of the proposed detector, and show that massive AO-MIMO can even perform similarly as the classical massive MIMO by adopting relatively more BS antennas.

This paper proposes a new large-scale antenna system (LSAS) where the base station (BS) is equipped with several classical radio-frequency (RF) chains but massive one-bit integrated information and energy receivers (Rxs). Multiple users with multiple antennas are served simultaneously. This new LSAS is much more energy efficient than massive MIMO because it requires much less classical power-hungry RF chains, and its massive integrated Rxs can harvest new energy. For the uplink multiuser detection (MUD), we develop a practical generalized approximate message passing detector (GAMPD), which exploits the signal prior probability distribution, and the hybrid BS measurements (full ones with both amplitudes and phases from the classical Rxs, and the one-bit amplitude measurements from the integrated Rxs) in a Bayesian manner. GAMPD involves matrix-vector multiplications, small-scale matrix inversions, and parallelized nonlinear operations, so it is suitable for hardware implementation. Our numerical results indicate that the proposed LSAS retains the advantages of massive MIMO, and can even perform similarly as the latter after introducing sufficient number of integrated Rxs.

In this paper, we research the multiuser detection (MUD) in a new massive MIMO with quantized phase-only (PO) measurements. Because the phase quantization can be implemented by one-bit ADCs, PO-MIMO overcomes the ADC bottleneck that appears when the signal bandwidth reaches multi-giga Hertz. However, the new problem is how to complete the uplink multiuser detection (MUD) based on the quantized PO measurements. In this paper, it is firstly solved by the sum-product-algorithm (SPA). Based on central limit theorem (CLT) and Taylor expansions, the SPA detector (SPAD) is simplified as a new PO generalized approximate message passing detector (POG-AMPD). POG-AMPD can exploit the quantized phase measurements and signal prior probability distribution in a Bayesian manner. It involves matrix-vector multiplications, and some parallelized non-linear operations, so it is suitable for hardware implementation. Numerical results indicate that POG-AMPD approaches to the maximum likelihood (ML) detection performance. By adopting moderately more BS antennas, massive PO-MIMO can even performs similarly as the conventional massive MIMO with full measurements.

Device-to-Device (D2D) underlay cellular communication has been recently considered as a popular paradigm to enhance system performance. In this paper, we study a power-allocation problem to maximize the system sum-rate while giving priority to cellular users (CUs), assuming that only large- scale fading information is known at the base station. Specifically, we give priority to CUs by introducing outage probability constraints. Further, we adopt the geometric programming technique to solve the optimal power-allocation problem. Numerical results show that the proposed outage- constrained power allocation scheme not only provides more reliable cellular communication but also improves the system sum-rate performance.

In uplink device-to-device (D2D) underlay cellular systems, massive multiple-input multiple-output (MIMO) seems promising as the large antenna array at the base station (BS) can nearly null the D2D-to- cellular interference. But the channel state information from all the users including D2D users to the BS is required to obtain the advantage. For the orthogonal channel training scheme, the pilot overhead becomes large with the number of users increasing, and constitutes a bottleneck for improvement of the system sum-throughput. In this paper, we propose pilot reuse (PR) for D2D underlay massive MIMO systems to reduce the pilot overhead and increase the system sum-throughput. Further, we develop a pilot scheduling algorithm under the criterion of minimizing the sum of mean square errors of channel estimation. Numerical results show that the proposed PR scheme can greatly increase the system sum-throughput at the price of slight degradation of cellular performance, compared to the conventional massive MIMO system.

This paper considers the error-detecting codes between two nodes. There are three transmissions between these two nodes: node A and node B. The first transmission can transmit one symbol within the alphabet {0, 1, ... , q1-1} from node A to node B; the second transmission can transmit one symbol within the alphabet {0, 1, ... , q2-1} from node B to node A; and the third transmission can transmit one symbol within the alphabet {0, 1, ... , q3-1} from node A to node B. One of the three transmissions may be erroneous. We prove that the maximum size of all 1-error-detecting codes over these three transmissions is min{q1, q2(q3-1)}.

A two-tier heterogeneous network, comprised of a macrocell underlaid with flexible and uncoordinated femtocell network, provides an economically viable way to improve indoor coverage, users' QoS and the capacity of the whole cellular network. However, mutual interference between macrocell and femtocell limits capacity and leads to the problem of coexistence. This paper proposes a game theoretical and decentralized spectrum negotiation framework to solve the above-mentioned problem. Macrocell and femtocell users have to reach a mutual satisfactory agreement on the partition of spectrum by making alternating offers to each other. By modeling the bargaining process as multi-stage dynamic game, we are able to obtain the equilibrium outcome. Theoretical and numerical analysis on negotiation results show that macrocell and femtocell can achieve a fair, efficient, and incentive compatible spectrum allocation via the proposed bilateral negotiation interactions.

Cooperative relaying is a promising technology which can improve the energy efficiency of cellular networks. However, the relays' static power consumption may cancel out the energy saved by the deployed relays. Energy harvesting (EH) technique is introduced for relay nodes in cellular networks in order to deploy flexibility and reduce the energy charge. Due to the EH property, relay nodes cannot maintain a stable energy flow and keep active with a certain probability, which would degrade the benefit from relay-aided cooperative transmission. In this paper, an analytical model is proposed to investigate the energy efficiency of cellular networks aided by EH relay nodes. Meanwhile, the closed-form expressions of coverage probabilities and mean achievable rates for different links are derived. Numerical results show that EH relays bring more improvement on energy efficiency compared with non-EH relayed transmission. The influence of the density and transmit power of stations is also given through numerical simulation.

Recently, wireless local area networks (WLANs) have become ubiquitous. In high density WLANs, interference resulting from neighbor cells is a challenging problem which would affect the network performance. To get higher throughput, it is important to improve spatial reuse of the channels. Since transmit power control (TPC) may cause asymmetric links unless carefully designed, we demonstrate that dynamic sensitive control (DSC) can be used to improve capacity in dense environment. In order to reduce the influence of inter-cell interference and achieve fairness, an adaptive DSC scheme is proposed to provide the edge users with higher clear channel assessment (CCA) threshold. The CCA adaptation strategy for access point (AP) is presented as well, and a stochastic geometry model is introduced in this paper. Coverage probability and average throughput derived based on the analytical model are used to measure the network performance. Furthermore, simulations show that the proposed DSC method can provide considerable performance improvement compared with the existing methods.

This paper studies the allocation of information flows in noiseless, memoryless communication networks in the presence of omniscient Byzantine adversary. In such networks, adversary may maliciously modify some edge-flows, and legitimate users should resort to network error correction strategies to transmit data reliably. Unlike prior papers, which focused on the capacities of the networks, we consider the expense of resources used by the flow. Hereby, this paper uses an optimization problem to define the concept of minimum cost network error correction flows. We provide a necessary and sufficient condition of feasibility of the allocation problem, and derive a cut-set outer bound on the feasible region. Using this cut-set bound, we can find the minimum cost network error correction flow in some instances. Moreover, we also consider the relationship between incoming edge-flows and outgoing edge-flows of a vertex. As for the directed acyclic graphs, we propose an algorithm to allocate the network error correction flow. This algorithm is with polynomial time complexity, and proves to be optimal when recoding at intermediate nodes is forbidden. Additionally, in order to justify the necessity of recoding at intermediate nodes, we analyze the benefit of intermediate recoding. On the one hand, we construct a series of instances to prove that intermediate recoding can bring enormous benefits in some networks. On the other hand, numerical analysis shows that the benefit is modest in small random graphs.

In distributed multilevel diversity coding, K correlated sources (each with K components) are encoded in a distributed manner such that, given the outputs from any α encoders, the decoder can reconstruct the first α components of each of the corresponding α sources. For this problem, the optimality of a multilayer Slepian-Wolf coding scheme based on binning and superposition is established when K ≤ 3. The same conclusion is shown to hold for general K under a certain symmetry condition, which generalizes a celebrated result by Yeung and Zhang.

In this paper, we research the multiuser detection (MUD) in a new Spatial Modulation Multiple-Input-Multiple-Output (SM-MIMO) system, whose Base Station (BS) is equipped with massive Radio-Frequency (RF) chains with low-resolution Analog-to-Digital Convertors (ADCs), and the User Equipments (UEs) have multiple Transmit Antennas (TAs) but single RF chain. In the uplink, UEs transmit their data by the Cyclic-Prefix Single-Carrier (CP-SC) SM technique. The key is how to design practical MU detectors to handle the severely quantized measurements and antenna correlations. Coherent detection is focused, so a Least-Square (LS) channel estimator is designed to acquire the the channel side information (CSI) at the Receiver (CSIR). Then, we firstly solve the MUD problem by the Sum-Product-Algorithm (SPA) on a clustered factor graph (FG) whose variable nodes correspond to the transmitted vectors from the UEs. Next, based on the Central Limit Theorem (CLT) and Taylor expansions, the SPA detector (SPAD) is simplified as a new low-complexity Message Passing De-Quantization Detector (MPDQD), which exploits both the structured sparsity and prior probability distribution of the transmitted signal. By utilizing the clustering technique, damping mechanism, and Analog Spatial Filtering (ASF), the robustness of MPDQD is improved significantly. Simulation results show that MPDQD outperforms the linear detectors, works steadily under strong channel correlations, and even performs similarly as its counterpart in the un-quantized SM-MIMO.

In this work, we research the multiuser detection (MUD) in a new Single Carrier (SC) massive Spatial-Modulation-Multiple-Input-Multiple-Output (SM-MIMO) system, where the Base Station (BS) is equipped with massive antennas connecting to independent Radio-Frequency (RF) chains designed by low-resolution Analog-to-Digital Converters (ADCs). Different User Equipments (UEs) possess multiple Transmit Antennas (TAs) but only one RF chain, and transmit data-bits to the BS by Cyclic-Prefix Single-Carrier (CP-SC) SM. We construct a new Message Passing De-Quantization Detector (MPDQD) based on the MPDQ algorithm in Compressed Sensing (CS). MPDQD exploits both the special structures (e.g., sparsity) and prior probability distribution of the transmitted signal, so it reaches superior detection performances. It involves the parallelized matrix-vector multiplication as the most complex operation, so it has low complexity and is suitable for hardware implementation. Simulation results show that MPDQD outperforms the linear detectors, and can approach the performance of the un-quantized system even when ADC resolution is low (e.g., 4bits).

In large-scale Multiple Input Multiple Output (MIMO), the number of Base Station (BS) antennas reaches tens even hundreds. If so many radio frequency front-ends adopt the classical receivers, their cost and power consumption during the receiving mode would increase quickly. Consequently, we exploit one-bit Analog Digital Convertor (ADC) to design low-cost and low-power software defined radio receivers for the BS. However, the new problem is how the BS fulfills Multiuser Detection (MUD) based on the one-bit baseband data. Based on the Message Passing De-Quantization (MPDQ) algorithm, an iterative multiuser detector is constructed. Its convergence properties is analyzed by the State Evolution (SE), and then its complexity is proved to be one order of magnitude smaller than that of the Minimum Mean Square Error (MMSE) detector. Simulation results show that the proposed detector outperforms MMSE. Finally, the proposed detector with matrix-vector multiplications is suitable for hardware implementation.

In large-scale Multiple-Input-Multiple-Output, the Base Station (BS) is equipped with a large-size antenna array containing tens even hundreds Radio Frequency channels. If so many RF front-ends adopt the classical receivers, their cost and power consumption during the receiving mode would increase quickly. Therefore, we exploit low-resolution Analog-to-Digital-Convertor to design low-power and low-cost software defined radio receivers for the BS. However, the new problem is how the BS fulfills channel estimation and Multiuser Detection (MUD) under low-resolution quantization. In this paper, first, least square method is used for channel estimation, and a robust Maximum Likelihood (ML) MUD problem is constructed to take into account the channel estimation errors. Second, an iterative multiuser detector is constructed by relaxing the ML MUD problem as a convex optimization problem and then solving the convex problem through the nonmonotone spectral projected gradient method. Compared with the Minimum Mean Square Error (MMSE) detector, the proposed detector has lower computational complexity, and is more suitable for hardware implementation. Simulation results show that it also outperforms MMSE.

This paper considers interactive transmissions in the presence of omniscient Byzantine attacks. Unlike prior papers, it is assumed that the number of transmissions, the number of erroneous transmissions therein, and the direction of each transmission are predetermined. Besides, the size of the alphabet in each transmission is unequal and predefined. Using these transmissions, two nodes communicate interactively to send a message. In this model, both attack strategies and coding bounds are considered. Although the codebook can not fully describe the interactive code, we still assert the existence of successful attack strategies according to the relations between codewords in the codebook. Furthermore, to ensure that the code is able to detect or correct a given number of transmission errors, upper bounds on the size of code are derived. Finally, the tightness of the bounds is discussed.

In large-scale Multiple-Input-Multiple-Output (MIMO) downlink, one state-of-the-art precoder significantly reduces the Peak-to-Average Power Ratio (PAPR) of the emitted signals from the Base Station (BS) antennas through minimizing the ℓ∞-norm of the precoded signals (generated by the precoder and transmitted by the BS RF frontends). In this paper, its computational complexity is analyzed based on the random matrix theory, and its two variants are constructed as follows. First, the ℓ∞-norm is approximated by a high order ℓp-norm (e.g. ℓ16), and then the downlink precoding problem is solved by classical Gradient Descent (GD) method. Second, the ℓ∞-norm is removed directly after additional box constraints are exerted on the elements of the precoded signals. Then the precoding problem is solved by spectral GD algorithm. Simulation results show that the above three convex-optimization based precoders can reduces the PAPR by more than 11.5dB compared to conventional precoding schemes.

In this paper, an iterative multiuser detector is proposed for uplink large-scale Multiple Input Multiple Output (MIMO). It is developed based on the Generalized Approximate Message Passing (GAMP) algorithm, and its convergence properties are analyzed by a one-dimensional iteration termed as State Evolution (SE). The SE analysis proves that the complexity of the GAMP detector (GAMPd) is one order of magnitude smaller than that of the Minimum Mean Square Error (MMSE) detector. Simulation results show the GAMPd performs similarly to MMSE at least, and even outperforms the latter when the number of BS antennas is not much larger than the number of users. In addition, the GAMPd with matrix-vector multiplications is suitable for hardware implementation.

As energy consumption reduction has been an important concern for the wireless industry, energy-efficient communications is of prime interest for future broadband networks. In this paper, we study the energy efficiency of relay-assisted cellular networks using tools from stochastic geometry. We first derive the coverage probability for the macro base station (MBS) to user (UE), the MBS to relay station (RS), and the RS to UE links, and then we model the power consumption of MBSs and RSs. Based on the analytical model and expressions, the energy efficiency of relay-assisted cellular networks is then evaluated and is shown to be strictly quasi-concave on the transmit power for the MBS to UE link or the RS to UE link. Numerical results also show that the energy efficiency first improves while it hits a ceiling as the MBS density increases.

Approximate Message Passing (AMP) simplified from Loopy Belief Propagation (LBP), is an important algorithm for sparse signal reconstruction in Compressed Sensing (CS). To improve the performance of current AMP algorithms, a weighted-damped AMP algorithm (WDAMP) is derived from a weighted version of BP that adopt probability damping technique. Simulation results show that WDAMP outperforms normal AMP for both 1-D and 2-D signal reconstruction. For 1-D signal reconstruction, probability damping brings most of the improvement. For 2-D signal reconstruction, weighting technique makes the major contribution. In summary, WDAMP outperforms conventional AMP.

This paper considers the problem of cognitive access to channels of primary users in a hierarchical network with multiple practical time varying channels. The proposed access strategy is not only based on the sensing about the occupation of the channel by the primary user, but also based on the channel state of the secondary user. We have established a new and more meaningful performance metric to measure secondary user's total throughput among channels, rather than just the channel occupation rate. The optimization objective of cognitive access is designed to maximize the total throughput among multiple channels subject to collision constraints used for primary users protection. It is shown that our proposed algorithm can achieve higher throughput under tight collision constraints through grasping the access opportunities which have good channel qualities. Numerical results are also presented to validate the theoretical analysis and the expressions of algorithm parameters. This paper shows that the total throughput can be improved significantly by introducing the new access strategy, which takes the advantage of access opportunities of the channel in time domain when it is free and of high quality.

In this paper, a two-way relay channel is considered, where two sources transmit information to each other with the help of a half-duplex decode-and-forward (DF) relay. We investigate the energy efficiency (EE) for the DF two-way relay transmission (TWRT). The EE expression is derived first and the properties of the EE are then analyzed. An novel algorithm is proposed to find the maximal EE under the relay power constraint for the DF-TWRT. Simulation results confirm the theoretical findings of the EE for the DF-TWRT.

In recent years, energy consumption of information and communication industry have received more and more attention. But the energy efficient optimization problem of interference-limited system is difficult to solve because it is a non-convex problem. In order to deal with this problem, two sub-optimal algorithms, based on dual theory and differ-concave theory respectively, are proposed in this paper. Under different signal to interference and noise ratio (SINR), there must have one of these algorithms can achieve the energy efficient upper bounder of interference-limited system. Besides the energy efficient performance, the proposed algorithms also have low complexity. the simulation results show that the proposed algorithms can get great convergence property by choosing initial value carefully.

Cognitive-radio-networks with multiple input multiple output is an effective method to improve spectrum efficiency. Linear pre-coders is a crucial technology to realize this goal. However, most of existing algorithm developed based on the Gaussian input assumption. This assumption limited the application of linear precoding and caused great performance loss. In this paper, for such a problem, a linear pre-coders algorithm for maximizing the secondary users spectrum efficiency is designed. Different from previous spectrum efficient algorithm which is developed under the Gaussian input assumption, the proposed algorithm can work under any input assumption by inheriting the advantage of mercury water filling algorithm (MWF) and singular value decomposition (SVD). Numerical results show that the proposed algorithm exhibits a large gain over existing algorithms when we take a practical modulation type.

In this paper, we address the problem of coordinated user scheduling for the downlink of a multi-cell distributed antenna system (DAS). With the practical assumption that only large-scale channel state information (CSI) is known at the transmitter, a low-complexity greedy scheduling scheme is proposed. In order to provide fairness among users, the proposed scheme adopts round-robin scheduling within each cell and optimizes the scheduling order for each cell to maximize the minimum ergodic capacity of the users. For each user, the selection transmission scheme (just the distributed antenna element (DAE) with the largest channel gain to the user is selected for transmission) is implemented and each selected DAE transmits with equal power. Simulation results demonstrate that the proposed greedy scheme promises much better performance than that without inter-cell coordination and its performance is quite close to the optimal one. Moreover, by simulations we find that the proposed scheme achieves nearly the same system ergodic sum capacity with the one targeted to maximize the system ergodic sum capacity.

This paper studies a spectrum sharing scenario between a cooperative relay network (CRN) and a nearby ad-hoc network. In particular, we consider a dynamic spectrum access and resource allocation problem of the CRN. Based on sensing and predicting the ad-hoc transmission behaviors, the ergodic traffic collision time between the CRN and ad-hoc network is minimized subject to an ergodic uplink throughput requirement for the CRN. We focus on real-time implementation of spectrum sharing policy under practical computation and signaling limitations. In our spectrum sharing policy, most computation tasks are accomplished off-line. Hence, little real-time calculation is required which fits the requirement of practical applications. Moreover, the signaling procedure and computation process are designed carefully to reduce the time delay between spectrum sensing and data transmission, which is crucial for enhancing the accuracy of traffic prediction and improving the performance of interference mitigation. The benefits of spectrum sensing and cooperative relay techniques are demonstrated by our numerical experiments.

In this note, we point out a few problems with the proof for Theorem 1 and Theorem 2 in the above-mentioned paper and give a correct version for the incorrect parts.

In this paper, we study a cognitive coexistence strategy for heterogeneous networks. While a lot of previous works focused on spectrum underlay approaches for weak interference scenarios, a high power infrastructure (IS) transmitter creates a large dead zone for nearby ad-hoc (AH) links using the same spectrum. To address this problem, we propose to utilize a half-duplex decode-and-forward (DF) relay node to assist the IS transmitter. The transmission time and power of relay-assisted IS network is optimized to reduce its generated interference while still guaranteeing its quality-of-service (QoS) level. The resource allocation problem of relay-assisted IS network is formulated as a convex optimization problem, for which a tailored dual optimization method is proposed. Our numerical results show that our relay-assisted scheme can produce less interference and/or achieve a higher QoS level.

Multicell processing (MCP) techniques have attracted considerable research attention since they can effectively mitigate other-cell interference (OCI). However, the potential gain of MCP comes with several practical challenges including huge overhead and stringent synchronization requirements. In this paper, we propose cooperative opportunistic scheduling to coordinate OCI for downlink transmission in multiple antenna cellular networks, which incurs less overhead and relaxes synchronization requirements. As a baseline for comparison, we study the average throughput with noncooperative opportunistic scheduling and derive the throughput expression in closed form. We show that the scaling law of average throughput with noncooperative opportunistic scheduling follows the log log K form, where K is the number of users per cell. Furthermore, with the assumption that adjacent cells can exchange channel state information, we propose a cooperative opportunistic scheduler. Using extreme value theory, we obtain concise expression for the average throughput with the cooperative opportunistic scheduler, which is proven to scale like log K. Our analysis are verified by simulation results.

In this paper, the downlink performance of a distributed antenna system (DAS) with random antenna layout is investigated. We address the problem of characterizing the downlink capacity with the generalized assumptions: (al) per distributed antenna power constraint, (a2) generalized mobile terminals equipped with multiple antennas, (a3) a multi-cell environment. Based on system scale-up, we derive a good approximation of the ergodic downlink capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameter in the approximation. The approximation is illustrated to be quite accurate and the iterative method is verified to be quite efficient by Monte Carlo simulations.

A novel low complexity user selection strategy was proposed for MIMO broadcast channels, where zero-forcing beamforming was considered. As the number of simultaneously supportable users at the same frequency is limited by the number of transmit and receive antennas, the base station may select a subset of users in a cell to serve. Exhaustive search for the optimal user set, however, is computationally prohibitive. We proposed a new sub-optimal user selection algorithm using the revised distance metric between subspaces spanned by the vectors of userspsila channels. Simulation results show that this new algorithm performs very close to the capacity-based algorithm with much lower computational complexity.

A novel low complexity scheduling algorithm was proposed for multi-user MIMO downlink channels. Considering the scenario that zero-forcing beamforming is employed at the base station, the number of simultaneously supportable users is limited by the number of transmit antennas at base station and user receive antennas. Thus the base station may select a subset of users to serve. Exhaustive search for the optimal user set, however, is computationally prohibitive. Based on the orthogonal projection operators of subspaces, we proposed a sub-optimal user selection algorithm using the distance metric between subspaces spanned by the vectors of users' channels. Furthermore, an equivalent version of this algorithm was presented after proving the equivalence between the distance metric and the correlation metric. Simulation results show that this algorithm performs very close to the capacity-based algorithm with much lower computational complexity.

This paper addresses the problem of joint transceiver design for Tomlinson-Harashima Preceding (THP) in the multiuser multiple-input-multiple-output (MIMO) downlink. Different from previous work, we perform stream-wise (both inter-user and intra-user) interference pre-cancelation at the transmitter. A minimum total mean square error (MT-MSE) criterion is used to formulate our optimization problem. By some convex analysis of the problem, we obtain the necessary conditions for the optimal solution. Then an iterative algorithm is proposed to handle this problem and its convergence is proved. Simulation results are given to verify the efficacy of our proposed scheme and to show its superiority over existing THP-relevant schemes.

In this paper, we investigate the uplink of a power- controlled multi-user distributed antenna system (DAS) with antennas deployed on a circle. Applying results from random matrix theory, we prove that the per-user sum rate and the total transmit power both converge as user number and antenna number go to infinity with a constant ratio. The relationship between the asymptotic per-user sum rate and the asymptotic total transmit power is established. We then deal with a typical path loss exponent of 4, present the rate-power relationship in a closed form and find the optimal circle radius for antenna deployment. Simulation results are also provided to demonstrate the validity of our analysis.

Distributed wireless communication system (DWCS) has been considered as a promising architecture for future wireless access networks. In this paper, we develop an adaptive transmit-receive mode selection scheme for a multi-user system with this architecture. In the proposed scheme, large-scale fading information between users and antennas is employed to decide the most appropriate mode from three available modes named independent processing mode, joint processing mode and time division mode. Selection criterion for a system with two single-antenna users and two distributed antennas is discussed in detail. Numerical results indicate that the proposed scheme achieves high capacity with relatively low complexity.