The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Addressing blockchain's scalability challenges posed by the growing number of nodes and transactions calls for innovative solutions, among which sharding stands out as a pivotal approach to significantly enhance blockchain throughput. However, existing schemes still face challenges related to a) the impact of vehicle mobility on blockchain consensus, especially for cross-shard transaction; and b) the strict requirements of low latency consensus in a highly dynamic network. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we first develop a standard for evaluating the network stability of nodes, combined with the nodes' trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle crossshard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in latency, throughput, consensus success rate, and node traffic load.

The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. The scalability of blockchain systems, particularly within the context of IoV, requires innovative approaches to manage the growing number of nodes and transactions. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we initially develop a standard for evaluating the network stability of nodes, combined with the nodes' trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle cross-shard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in terms of latency, throughput, and consensus success rate.

For the FDD massive MIMO system, the pilot overhead becomes a major problem, which may decrease the system performance. In this paper, a partial pilot transmission scheme and the corresponding two-dimension CSI acquisition methods are proposed. The downlink vertical pilot and horizontal pilot are mapped to one column and one row of the antenna array. CSI for the vertical and horizontal domain is fed back separately based on channel approximation algorithm. Compared with the full-dimension CSI acquisition method and the current passive antenna MIMO system (PAS), the proposed method achieves a compromise of the system overhead and the system throughput. Simulation result shows the effectiveness of the proposed method.

In this paper, CSI feedback for massive MIMO system with planar antenna array is studied. A hybrid channel state information feedback mechanism is proposed. The proposed mechanism is based on combination of beamformed CSI-RS and non-beamformed CSI-RS transmission. Each codeword in the codebook for feedback regarding the non-beamformed CSI-RS is associated with a beamformed CSI-RS. By utilizing this association, accurate CSI feedback could be achieved without incurring overwhelming CSI-RS overhead while retaining CSI-RS coverage. Evaluation results are provided to confirm the effectiveness of the proposed mechanism.

In this paper, codebook design for massive MIMO systems with 32/64Tx transmit branches is investigated. Using the same parameterized codebook framework of LTE Rel-13 for 16Tx, 32/64Tx codebooks are discussed and evaluated through system level simulations. In addition, to deal with the larger feedback overhead, three codebook sub-sampling methods are discussed. These methods aim to achieve a tradeoff between the feedback overhead and the system performance. Simulation results prove the effectiveness of the proposed methods.

The increased number of antennas for massive MIMO system implies that the quantization codebook size needs to be significantly larger, which increases precoder search complexity accordingly. This paper compares the vertical/horizontal channel characteristics and finds that the vertical channel has a much longer coherence time than the horizontal channel. Based on these results, a two-dimension CSI quantization mechanism is proposed to allow decoupled vertical/horizontal domain quantization. The mechanism allows the vertical domain precoder search to be implemented at a slower rate than the horizontal domain, thereby enabling the network to separately control the feedback rate/granularity in different spatial dimensions. The proposed mechanism achieves significant UE complexity reduction and power consumption without compromising the system performance, demonstrated by realistic system simulation.

Multi-cell coordination is a technique for interference mitigation in cellular systems. For joint-transmission (JT), the received signal power is boosted by diversity combining of signals from multiple transmission points. This however results in reduced frequency-reuse ratio and cell-splitting order, which may subsequently degrade the network-level throughput. In order to strike a balance between the diversity combining gain and frequency-reuse ratio, in this paper we propose a dynamic scheduling algorithm that adaptively schedules users in single-cell or multi-cell transmission mode to maximize the system sum throughput. Furthermore, basestation association for each user is meticulously addressed to avoid negatively impacting the frequency-reuse gain. The proposed algorithm is evaluated with system-level evaluation tools fully compliant with 3GPP LTE specification, thereby providing a realistic assessment on the gain of multi-cell coordination in terms of system capacity and coverage.

In orthogonal frequency division-multiple access (OFDMA) systems, the available downlink frequency is divided into subbands which are shared among users. The transmitter determines the users that are scheduled on each subband, and the data rate for each user. To enable these transmitter choices, each user feeds back a channel quality indicator (CQI), giving a compressed version of its observed signal-to-interference-noise ratio (SINR) on all subbands. In this paper, we classify existing CQI compression schemes into symmetric and asymmetric schemes. Symmetric schemes aim to accurately represent the CQI on all subbands, and are shown to be suitable when the receiver cannot accurately predict the subbands on which it will be scheduled. This happens, for instance, when the receiver is fast-moving, or when a round- robin scheduler is employed, or during low load conditions. Asymmetric schemes, on the other hand, greedily achieve accuracy only in high-SINR subbands, and are shown to perform well in high-load cellular networks with proportional fair scheduling and low-speed users. We propose one new scheme in each category. Simulation results for a 3GPP LTE cellular network show that our proposed schemes achieve similar or better performance than the current best-M feedback scheme used in LTE, with 30% less overhead.

A clustered base transceiver station (BTS) coordination strategy is proposed for a large cellular MIMO network, which includes full intra-cluster coordination-to enhance the sum rate-and limited inter-cluster coordination-to reduce interference for the cluster edge users. Multi-cell block diagonalization is used to coordinate the transmissions across multiple BTSs in the same cluster. To satisfy per-BTS power constraints, three combined precoder and power allocation algorithms are proposed with different performance and complexity tradeoffs. For inter-cluster coordination, the coordination area is chosen to balance fairness for edge users and the achievable sum rate. It is shown that a small cluster size (about 7 cells) is sufficient to obtain most of the sum rate benefits from clustered coordination while greatly relieving channel feedback requirement. Simulations show that the proposed coordination strategy efficiently reduces interference and provides a considerable sum rate gain for cellular MIMO networks.

Block diagonalization is a preceding technique for multiuser MIMO systems that pre-cancels inter-user interference at the transmitter side. When there are a large number of base station antennas but a limited number of RF amplifiers, the system performance can be significantly improved by switching a subset of antennas to the RF chains and exploiting antenna selection diversity. The optimal antenna subset can be obtained by exhaustively searching over all possible antenna combinations. This brute-force search, however, is prohibitively complicated and impractical. To reduce the complexity, in this paper we propose several low-complexity suboptimal transmit selection algorithms that minimize a symbol error rate (SER) upper bound or maximize a capacity lower bound. Simulation results show that our proposed algorithms perform very close to the optimal exhaustive search, while the complexity is much lower.

Block diagonalization is a downlink preceding technique that pre-cancels inter-user interference in multiuser MIMO systems. When there are a large number of users, the system throughput can be significantly increased by selecting a subset of users and a subset of receive antennas for each user. The optimal user and antenna subset can be obtained by exhaustively searching over all possible user and antenna combinations to find the one with the highest sum throughput. This brute-force solution, however, is prohibitively complicated. To reduce the complexity, in this paper we propose a low-complexity suboptimal user and antenna selection algorithm. For most system configurations, we show that our proposed algorithm achieves up to 98% of the optimal sum throughput of the exhaustive search, where the complexity is orders of magnitude lower than the exhaustive search method.

This paper proposes a power control strategy for the uplink of cellular MIMO spatial multiplexing systems, with a linear MMSE receiver applied at the base station and a single active user per time instant. A fixed per-stream SINR target is employed that allows guaranteed QoS for delay-sensitive applications. A straightforward application of single antenna power control is not possible in the MIMO context due to coordination between receive antennas and nonlinear dependence between interference and eigenspaces of the channel matrices. Two schemes are proposed to solve the problem. The first equally allocates power to all transmit antennas. Deriving an SINR lower bound based on an eigenvalue approximation of the composite interference, allows application of the conventional single antenna power control framework to solve this problem. To improve the feasibility performance, a second scheme is proposed that adaptively allocates power on the transmit antennas, where an iterative algorithm based on game theory is used to sequentially update each user's power distribution. The optimal solution with full channel knowledge, and a practical near-optimal solution requiring only partial channel knowledge, are both derived. Numerical results show that power control, compared to supposedly optimal waterfilling strategies, actually achieves higher throughput at the low SINRs typical in cellular systems, with significantly lower overhead and complexity. Due to its better exploitation of spatial diversity and reduced transmit power (and hence reduced interference), adaptive power allocation increases the achievable SINR by an order of magnitude over equal power allocation, resulting in far better coverage.

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with dirty paper coding (DPC). However, algorithms that approach the DPC sum capacity do not appear viable in the forseeable future, which motivates lower complexity interference suppression techniques. Block diagonalization (BD) is a linear preceding technique for downlink multiuser MIMO systems. With perfect channel knowledge at the transmitter, BD can eliminate other users' interference at each receiver. In this paper, we study the sum capacity of BD with and without receive antenna selection. We analytically compare BD without receive antenna selection to DPC for a set of given channels. It is shown that (1) if the user channels are orthogonal to each other, then BD achieves the same sum capacity as DPC; (2) if the user channels lie in the same subspace, then the gain of DPC over BD can be upper bounded by the minimum of the number of transmit and receive antennas. These observations also hold for BD with receive antenna selection. Further, we study the ergodic sum capacity of BD with and without receive antenna selection in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is proposed for easy estimation of the gap between the sum capacity of DPC and BD without receive antenna selection.

Power control is an important technique for interference management in interference-limited multiuser communication systems. In this paper, we propose a novel multidimensional power control technique for the uplink of cellular MIMO systems. Similar to prior single antenna power control work, we employ a fixed SINR target that allows guaranteed quality-of-service for delay-sensitive data applications. The proposed power control scheme is a more general technique because it incorporates single antenna, orthogonal multi-carrier, and utility-based power control schemes as special cases. Two approaches are proposed, the first where each user's power is equally allocated to its antenna array, in the second the allocation is adaptive. The optimal solution with full channel knowledge, and a practical near-optimal solution requiring only partial channel knowledge, are both derived. Numerical results show that power control actually achieves higher throughput a.t the low SINRs typical in cellular systems, compared to supposedly optimal water-filling strategies, with lower overhead and complexity. In the higher SINR ranges, iterative water-filling indeed slightly outperforms adaptive power allocation. Due to its better exploitation of spatial diversity and reduced transmit power (and hence reduced other-cell interference), adaptive power allocation increases the achievable SINR by an order of magnitude over equal power allocation, resulting in far better coverage.

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with Dirty Paper Coding (DPC). Deploying DPC in real-time systems is, however, impractical. Block Diagonalization (BD) is an alternative precoding technique for downlink multiuser MIMO systems, which can eliminate inter-user interference at each receiver, at the expense of suboptimal sum capacity vs. DPC. In this paper, we study the sum capacity loss of BD for a fixed channel. We show that 1) if the user channels are orthogonal to each other, then BD achieves the complete sum capacity; and 2) if the user channels lie in a common row vector space, then the gain of DPC over BD can be bounded by the minimum of the number of transmit and receive antennas and the number of users. We also compare the ergodic sum capacity of DPC with that of BD in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is derived, which can be evaluated with a few numerical integrations. With this bound, we can easily estimate how far away BD is from being optimal in terms of ergodic sum capacity, which is useful in directing practical system designs.

Multiuser spatial multiplexing uses precoding to support multiple users in multi-antenna wireless channels. In this paper, two transmit diversity techniques are proposed that use extra transmit antennas to obtain additional diversity. In one solution, an eigenmode selection technique is proposed to achieve higher diversity gain by optimally matching the data streams to eigenmodes with superior channel conditions. In the other solution, a multiuser antenna selection algorithm is proposed that achieves good performance with fewer RF chains and lower system cost. Selection criteria for single-user spatial multiplexing are extended to the multiuser scenario in the context of unitary downlink precoding, and a novel selection algorithm that is near optimal in terms of symbol error rate (SER) is proposed. Simulation results show dramatic SNR reductions of 6-10 dB at an uncoded SER of 10/sup -3/ with only one extra antenna.

Space-time block codes (STBC) are powerful techniques to achieve full spatial diversity in Rayleigh fading channels with low decoding complexity. When channel state information is available to the transmitter, space-time block codes can be improved through the use of a transmit precoder chosen based in part on the current channel state. We investigate the downlink transmission of an Alamouti space-time block coded multiuser wireless system in which complete channel knowledge is available to the base transceiver station. A novel unitary precoder is proposed that, with enough antennas, can effectively precancel co-channel interference at each mobile user thus enabling simple single user space-time block decoding. It is shown that excess transmit antennas, beyond the minimum required for precancellation, can he used to further improve the diversity performance through careful selection of the precoding matrix. Though the derivation assumes perfect channel state information, the impact of estimation error is investigated by deriving a lower bound on the signal to interference plus noise ratio. Monte Carlo simulations show that our proposed downlink precoder can effectively cancel the interference between mobiles, while still providing good diversity performance.

Space-time (ST) coded OFDM has been proved to be an effective and promising technique for future wideband wireless data access. As the multiple transmit antennas transmit signals act as co-channel interference to each other, the channel estimation for ST-coded OFDM systems is different from the single transmit antenna scenario. Channel estimation can be achieved by estimating the channel impulse response based upon a discrete Fourier transform (DFT). However, in a multipath channel with non-sample-spaced time delays, this approach will cause power leakage and result in an error floor. In this paper, based on the use of IPIC DLL (interpath interference cancellation delay locked loop), we propose a least square (LS) and minimum mean square error (MMSE) channel estimators for ST-coded OFDM with non-sample-spaced multipath time delays. It is shown that the proposed channel estimators can eliminate the error floor substantially and perform quite close to the case with ideal channel state information.